Multi-Detector Computed Tomography in Oncology: CT Perfusion Imaging [1 ed.] 1842143093, 9781842143094

Table of contents :
CT Perfusion Imaging
Editors
Kenneth A Miles MBBS FRCR MS c MD FCRP
Brighton and Sussex Medical School Brighton
Charles-André Cuenod MD P h D
Laboratoire de Recherche en Imagerie Université Paris V
Paris
France
Foreword by Dame Janet Husband
To Elizabeth, Matthew, Benjamin, and Samuel …
C ontents
C ontributors
Daniel Balvay P h D
Massimo Bellomi MD
Rayleen V Bowman MBBS P h D FRACP
Natalie Charnley MBC h B M R C P FRCR
Xiaogang Chen P h D
Charles-André Cuenod MD P h D
Kwun M Fong MBBS FRACP P h D
Laure Fournier MD P h D
Ethan J Halpern MD
Robert Hermans MD P h D
Michelle Hutnik
Elizabeth P Ives MD
Aliya Jiwani
Ting-Yim Lee P h D FCCPM
William W Li
Anne E Miles BS c PGCE MA
Kenneth A Miles MBBS FRCR MS c MD FCRP
Blake Murphy BS C
Giuseppe Petralia MD
Karim Samji MBBS BS c
Errol Stewart BS C
Ian A Yang MBBS P h D FRACP
F oreword
Professor Dame Janet Husband DBE FMedSci PRCR FRCP
P r eface
Ken Miles MD
P erfusion computed tomography: a historical perspective
ANATOMICAL AND PHYSIOLOGICAL KNOWLEDGE IN ANCIENT CIVILIZATIONS
THE DEVELOPMENT OF MODERN ANATOMY
ANATOMY AND IMAGING
PHYSIOLOGY AND THE CIRCULATION
PERFUSION COMPUTED TOMOGRAPHY: C OMBINING ANATOMICAL IMAGING AND QUANTITATIV
REFERENCES
Scientific basis and validation
DEFINITION OF BASIC TERMS AND DISCUSSION OF FUNDAMENTAL RELATIONSHIPS
T r ansfer flux of solute through the capillary endothelium
C entral volume principle
IMPULSE RESIDUE FUNCTION AND CONVOLUTION
Impulse residue function
C onvolution
F l o w scaled impulse residue function and model deconvolution
C OMPUTED TOMOGRAPHY TUMOR PERFUSION MEASUREMENT WITHOUT THE NEED FOR KINET
F ick principle
No outflow assumption
KINETICS MODELING
C ompartment models
Distributed parameter models
∫ C
INPUT ARTERIAL CONCENTRATION FUNCTION
P a rtial volume averaging
Dispersion of measured arterial concentration function relative to true input
Delay in arrival of contrast agent in tissue relative to the measured arteria
Hepatic artery and portal vein input to the liver
Recirculation
PRACTICAL ISSUES
Scanning protocol: image interval, scanning duration, and radiation dose
C alibration of CT scanner
C ontrast, noise, and radiation dose
V ALIDATION OF COMPUTED TOMOGRAPHY TUMOR BLOOD FLOW AND RELATED MEASUREMENTS
IN ANIMAL TUMOR MODELS
Methods not requiring tracer kinetics modeling
T r acer kinetics modeling methods R3230AC rat-derived mammary adenocarcinom
VX2 brain tumor
VX2 skeletal muscle (thigh) tumor
VX2 liver tumor
V ALIDATION OF CT TUMOR BLOOD FLOW AND RELATED MEASUREMENTS IN CLINICAL STUDI
Reproducibility studies
A ccuracy studies
C ONCLUSION
Image acquisition and contrast enhancement protocols for
CT perfusion
INTRODUCTION AND OVERVIEW:
CT perfusion
IMAGE ACQUISITION
Phase of respiration
Overall length of time of the image series
T he number and frequency of images
T he number and thickness of CT slices
X-ray exposure factors
C ONTRAST ENHANCEMENT
T y pe of contrast medium
V olume and concentration of contrast medium
CHOICE OF PROTOCOL
C OMPARING RESULTS OBTAINED USING DIFFERENT PROTOCOLS
T umor enhancement
C omparing tumor perfusion and enhancement
SUMMARY
REFERENCES
Image processing
DETERMINATION OF AVERAGE MEAN TRANSIT TIME OF A REGION OF INTEREST
IMAGE PROCESSING
Segmentation of specific organs and tumor
Segmentation of vascular structures
Segmentation of different tissue types
C ONCLUSION
A ngiogenesis, tumor perfusion, and cancer management
ANGIOGENESIS IN TUMOR GROWTH, PROGRESSION, AND METASTASES
CHARACTERISTICS OF TUMOR ANGIOGENESIS AND PERFUSION
CANCER MANAGEMENT WITH ANTIANGIOGENIC THERAPY
IMAGING ANGIOGENESIS
F unctional CT
DCE-MRI
MRA
PET
Ultrasound
IMAGING TUMOR VASCULATURE FOR ANTIANGIOGENIC A GENT DEVELOPMENT
C ONCLUSIONS
A CKNOWLEDGMENTS
REFERENCES
T umors of the brain, head, and neck
P a rt A: CT perfusion imaging in cerebral neoplasms
INTRODUCTION
MENINGIOMA
GLIOMA
CEREBRAL LYMPHOMA
METASTATIC BRAIN DISEASE
CT PERFUSION TO DIFFERENTIATE BETWEEN BRAIN TUMOR AND CEREBRAL INFARCTION
THERAPEUTIC PLANNING AND MONITORING
Radiation planning
THERAPEUTIC MONITORING
C ONCLUSION
INTRODUCTION
P a rt B : CT perfusion in head and neck cancer
CT PERFUSION IMAGING IN HEAD AND NECK CANCER
USE OF CT PERFUSION AS PREDICTOR OF LOCAL C ONTROL AFTER RADIOTHERAPY
C ONCLUSION
REFERENCES
CT perfusion applications in lung cancer
P A THOLOGY OF LUNG CANCER
P r esentation
Increasing detection of pulmonary nodules in clinical practice
C ONTRAST-ENHANCED CT
TECHNICAL CONSIDERATIONS: MEASUREMENT OF C ONTRAST ENHANCEMENT AND PERFUSION
EVALUATION OF PULMONARY NODULES
CT enhancement and pathology of lung nodules
C omparison with FDG-PET scans
POTENTIAL LIMITATIONS
V ariations in technique
Enhancing benign lesions
Small nodules
T umoral heterogeneity
P otential technical artifacts
Radiation dose
FUTURE DEVELOPMENTS
Refinements
P r ognostication and treatment monitoring
W hole tumor quantitation
C ONCLUSIONS
REFERENCES
T umors of the gastro-intestinal tract
Part A: Rectal cancer
BACKGROUND
TECHNIQUE
P a tient preparation
Image acquisition protocol
C ontrast medium administration
DATA AND IMAGE ANALYSIS
CURRENT EXPERIENCE AND CLINICAL APPLICATION
C ancer of the pancreas
Part B: Other gastrointestinal tumors
P r imary liver tumors
REFERENCES
T umors of the urogenital tract
PERFUSION IMAGING OF OTHER TUMORS OF THE UROGENITAL TRACT
Renal cancer
C e rvical cancer
REFERENCES
CT perfusion of lymph nodes
INTRODUCTION
L YMPH NODE METASTASES
L YMPHOMA
SUMMARY
REFERENCES
CT perfusion of liver metastases and early detection of micrometastases
INTRODUCTION
LIVER PERFUSION PHYSIOLOGY
QUANTITATIVE MEASUREMENT OF LIVER MICROCIRCULATION WITH FUNCTIONAL CT
Introduction
Different methods for liver perfusion quantification with CT
Slope-ratio methods
T r acer kinetic modeling
Deconvolution method for liver perfusion imaging
Theory of the deconvolution method (see also Chapter 2)
H t h d
Extraction of microvascular parameters
Measurement of the tissue blood flow with the central volume theorem The blo
Extraction of hepatic perfusion parameters
α α
FUNCTIONAL DETECTION OF LIVER METASTASES
CT perfusion and overt metastases
Micrometastases
Relationship between hepatic CT perfusion and survival
Other imaging techniques
Dynamic liver scintigraphy
Duplex Doppler ultrasound
C ontrast-enhanced dynamic MRI
PHYSIOPATHOLOGY OF MICROCIRCULATION ALTERATIONS IN METASTATIC LIVER
In vivo videomicroscopy
LIMITATIONS OF CT PERFUSION IN THE LIVER
C ONCLUSION
REFERENCES
Beyond RECIST: CT perfusion in evaluating treatment r esponse and complicat
INTRODUCTION
CT PERFUSION IN ASSESSING TUMOR RESPONSE TO DRUGS WHICH AFFECT TUMOR VASCULAT
CT PERFUSION IN ASSESSING TUMOR RESPONSE TO NOVEL ANTIVASCULAR AGENTS IN PHAS
CT PERFUSION IN ASSESSING TUMOR RESPONSE TO RADIOTHERAPY
O THER THERAPIES
IDENTIFICATION OF SUBPOPULATIONS ENRICHED FOR RESPONSE
ASSESSMENT OF TREATMENT COMPLICATIONS
C ONCLUSIONS
REFERENCES
P e rfusion CT–PET: opportunities for combined assessment of tumor v ascula
BIOMOLECULAR MEDIATORS FOR TUMOR VASCULARITY AND METABOLISM
V ASCULAR–METABOLIC RELATIONSHIPS IN TUMORS
V ascular–metabolic relationships and tumor aggression
Changes in tumor vascularity and metabolism f ollowing therapy
HEPATIC PHOSPHORYLATION OF GLUCOSE ASSESSED BY CT PERFUSION–PET
SUMMARY
REFERENCES
Index

Citation preview

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Multidetector Computed Tomography in Oncology CT Perfusion Imaging

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Multidetector Computed Tomography in Oncology CT Perfusion Imaging Editors

Kenneth A Miles MBBS FRCR MSc MD FCRP Brighton and Sussex Medical School Brighton UK Charles-André Cuenod MD PhD Laboratoire de Recherche en Imagerie Université Paris V Paris France Foreword by Dame Janet Husband

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© 2007 Informa UK Ltd First published in the United Kingdom in 2007 by Informa Healthcare, Telephone House, 69–77 Paul Street, London EC2A 4LQ. Informa Healthcare is a trading division of Informa UK Ltd. Registered Office: 37/41 Mortimer Street, London W1T 3JH. Registered in England and Wales number 1072954. Tel: +44 (0)20 7017 5000 Fax: +44 (0)20 7017 6699 Website: www.informahealthcare.com All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior permission of the publisher or in accordance with the provisions of the Copyright, Designs and Patents Act 1988 or under the terms of any licence permitting limited copying issued by the Copyright Licensing Agency, 90 Tottenham Court Road, London W1P 0LP. Although every effort has been made to ensure that all owners of copyright material have been acknowledged in this publication, we would be glad to acknowledge in subsequent reprints or editions any omissions brought to our attention. Although every effort has been made to ensure that drug doses and other information are presented accurately in this publication, the ultimate responsibility rests with the prescribing physician. Neither the publishers nor the authors can be held responsible for errors or for any consequences arising from the use of information contained herein. For detailed prescribing information or instructions on the use of any product or procedure discussed herein, please consult the prescribing information or instructional material issued by the manufacturer. A CIP record for this book is available from the British Library. Library of Congress Cataloging-in-Publication Data Data available on application ISBN-10: 1-84214-309-3 ISBN-13: 978-1-84214-309-4 Distributed in North and South America by Taylor & Francis 6000 Broken Sound Parkway, NW, (Suite 300) Boca Raton, FL 33487, USA Within Continental USA Tel: 1 (800) 272 7737; Fax: 1 (800) 374 3401 Outside Continental USA Tel: (561) 994 0555; Fax: (561) 361 6018 Email: [email protected] Distributed in the rest of the world by Thomson Publishing Services Cheriton House North Way Andover, Hampshire SP10 5BE, UK Tel: +44 (0)1264 332424 Email: [email protected] Composition by Cepha Imaging Pvt Ltd, Bangalore, India. Printed and bound in India by Replika Press Pvt Ltd

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To Elizabeth, Matthew, Benjamin, and Samuel …

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Contents List of contributors

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Foreword

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Preface

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Computed tomography perfusion: a historical perspective Anne E Miles and Kenneth A Miles

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Scientific basis and validation Ting-Yim Lee and Errol Stewart

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Image acquisition and contrast enhancement protocols for perfusion CT Kenneth A Miles

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Image processing Ting-Yim Lee, Xiaogang Chen, and Kenneth A Miles

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Angiogenesis, tumor perfusion, and cancer management William W Li, Aliya Jiwani, and Michelle Hutnik

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Tumors of the brain, head, and neck Part A: Perfusion CT imaging in cerebral neoplasms Karim Samji and Kenneth A Miles Part B: Perfusion CT in head and neck cancer Robert Hermans

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Perfusion CT applications in lung cancer Kwun M Fong, Rayleen V Bowman, Ian A Yang, and Kenneth A Miles

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Contents

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Tumors of the gastro-intestinal tract Part A: Rectal cancer Massimo Bellomi and Giuseppe Petralia Part B: Other gastrointestinal tumors Kenneth A Miles

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Tumors of the urogenital tract Elizabeth P Ives and Ethan J Halpern

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CT perfusion of lympth nodes Kenneth A Miles

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CT perfusion of liver metastases and early detection of micrometastases Charles-André Cuenod, Laure Fournier, Daniel Balvay, and Kenneth A Miles

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Beyond RECIST: perfusion CT in evaluating treatment response and complications Natalie Charnley and Kenneth A Miles

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Perfusion CT–PET: opportunities for combined assessment of tumor vascularity and metabolism Kenneth A Miles

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Contributors Daniel Balvay PhD Laboratoire de Recherche en Imagerie Université Paris V Paris France Massimo Bellomi MD School of Medicine University of Milan and Department of Radiology European Institute of Oncology Milan Italy Rayleen V Bowman

MBBS PhD

FRACP

The University of Queenland Brisbane Australia Natalie Charnley

MBChB MRCP

FRCR

The University of Manchester Wolfson Molecular Imaging Centre Manchester UK

Xiaogang Chen PhD Radiology and Nuclear Medicine Department The University of Western Ontario London, ON Canada Charles-André Cuenod MD PhD Laboratoire de Recherche en Imagerie Université Paris V Paris France Kwun M Fong MBBS FRACP PhD Department of Thoracic Medicine The Prince Charles Hospital Brisbane Australia Laure Fournier MD PhD Laboratoire de Recherche en Imagerie Université Paris V Paris France

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Ethan J Halpern MD Department of Radiology Thomas Jefferson University Jefferson Medical College Philadelphia, PA USA Robert Hermans MD PhD Department of Radiology University Hospitals Leuven Leuven Belgium Michelle Hutnik The Angiogenesis Foundation Cambridge, MA USA Elizabeth P Ives MD Department of Radiology Thomas Jefferson University Philadelphia, PA USA Aliya Jiwani The Angiogenesis Foundation Cambridge, MA USA Ting-Yim Lee PhD FCCPM Radiology and Nuclear Medicine Department The University of Western Ontario London, ON Canada

William W Li The Angiogenesis Foundation Cambridge, MA USA Anne E Miles BSc PGCE University of London London UK Kenneth A Miles

MA

MBBS FRCR MSc

MD FCRP

Brighton and Sussex Medical School Brighton UK Blake Murphy BSC Radiology and Nuclear Medicine Department The University of Western Ontario London, ON Canada Giuseppe Petralia MD Department of Radiology European Institute of Oncology Milan Italy Karim Samji MBBS BSc Brighton and Sussex University Hospitals NHS Trust Brighton UK

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Contributors

Errol Stewart BSC Radiology and Nuclear Medicine Department The University of Western Ontario London, ON Canada

Ian A Yang MBBS PhD FRACP Department of Thoracic Medicine The Prince Charles Hospital Brisbane Australia

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Foreword The extraordinary advances in medical technology over recent years have placed imaging at the center of cancer diagnosis and assessment but today the information used to direct patient management is based almost entirely on morphological assessment. However, we are now at the brink of a new and exciting era in which functional data will become an integral component of routine tumor imaging. It is well recognized that a tumor cannot grow beyond the size of 1mm3 without a blood supply and that assessment of tumor vasculature provides a measure of tumour aggressiveness as well as insight into other factors related to prognosis, prediction of response to treatment and risk of recurrence. This text brings imaging of tumor vasculature into the domain of leading edge clinical practice and describes the added value and limitations of current perfusion techniques in individual tumor types. While Magnetic Resonance Imaging (MRI) provides a unique tool for assessing tumors using a multifunctional approach, multidetector CT (MDCT) is more widely available for staging tumors and indeed remains the workhorse of cancer imaging today. It is appropriate therefore that MDCT should be exploited to provide both morphological and functional measurements of tumor vasculature in the routine assessment of patients with cancer. This approach is a welcome step forward in striving to reach the goal of providing a detailed portrait of the morphological and functional aspects of a tumor prior to therapy. Opportunities for developing multifunctional assessment of tumors by a combination of PET and CT data will take us further down this road, particularly as we look forward to the introduction of new tracers which will allow interrogation of multiple biological processes.

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Professor Miles has undertaken pioneering work in the development of perfusion imaging with CT and here he brings together the views of a team of highly regarded world experts. Together they present a contemporary analysis of the current evidence of the role of perfusion MDCT in cancer medicine thus providing the foundation on which to build a robust framework for the future. It is hoped that in the not too distant future these techniques will be applied to patients with cancer routinely and that this will lead to improved survival and better patient outcomes. Professor Dame Janet Husband DBE FMedSci PRCR FRCP The Institute of Cancer Research Royal Marsden NHS Trust

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Preface It is now more than 25 years since Leon Axel proposed a method for determination of cerebral blood flow from rapid-sequence contrastenhanced computed tomography (CT). Today, the availability of rapid imaging with multidetector CT systems and commercial analysis software has made perfusion imaging with CT an everyday technique for clinical practice. CT remains an essential tool in the assessment of patients with cancer, not only for diagnosis but also for assessment of disease extent and severity, and for the evaluation of response to treatment. Perfusion CT is readily performed as an adjunct to conventional CT, providing valuable information about tumor vascularity. In many ways, perfusion CT is not unlike CT angiography, but depicts the functional status of the tumor circulation at tissue level rather than visualizing the morphology of discrete vessels. By reflecting the processes of tumor angiogenesis, this additional information can aid in diagnosis, assess tumor aggression, and help to overcome some of the limitations associated with morphological criteria for evaluation of tumor response. The development of perfusion CT also links with another recent advance in cancer imaging, the introduction of integrated positron emission tomography (PET)–CT systems. The increasing use of intravenous contrast material during PET–CT can be extended to include a CT perfusion study. In this way, it is now possible to depict tumor morphology, perfusion, and glucose metabolism to provide an exceptionally detailed assessment of tumor biology in a single examination. This book is the first to be dedicated solely to the application of perfusion CT in oncology. The aim is to provide the technical knowledge required to reliably obtain CT perfusion images of tumors, to give an understanding of the pathophysiology of tumor angiogenesis and its

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relationship to contrast enhancement on CT, and to outline the ability of perfusion CT to enhance diagnosis, prognosis, and therapy monitoring for patients with cancer. The technique is also portrayed in its historical context. The book will therefore be of interest to radiologists and radiographers currently using perfusion CT or considering its introduction to their institution. The information will also be valuable to clinicians treating patients with cancer and to researchers involved in the development of new cancer therapies. Ken Miles MD Brighton and Sussex Medical School

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1 Perfusion computed tomography: a historical perspective Anne E Miles and Kenneth A Miles

Perfusion computed tomography (CT) has been made possible by the joining together of two great medical specialities: anatomy and physiology. It is important for us today as doctors and scientists to look back into history and to our predecessors, at the state of knowledge from ancient up to modern times, as well as looking forward to the possibilities and dreams to which we aspire. J B Thornton wrote: ‘The more we treat the theories of our predecessors as myths, the more inclined we shall be to treat our own theories as dogmas.1 By looking back through history, we are able to see the grand schemes and ideas developing over time, and the continuity of ideas from great thinkers which can be added to and extended to attain new levels of knowledge by our own thinkers today. This chapter does not attempt to mention all the important thinkers and achievers who have helped anatomy and physiology become what they are together. It would be an impossible task. Rather, by dipping in and out of history, we will attempt to cover some of the more major scientists, philosophers, and physicians who have contributed to making CT perfusion what it is today.

ANATOMICAL AND PHYSIOLOGICAL KNOWLEDGE IN ANCIENT CIVILIZATIONS Many of the medical accomplishments of the great civilizations of antiquity have been sadly ignored in the West because of the lack of accurate records, and problems with deciphering material that has been, and still is being, discovered. One of the earliest Egyptian papyruses

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(Edwin Smith Papyrus) is probably a copy of one that was first written between 3000 and 2500 BC. Translated in 1930, this papyrus is essentially a surgical document, and, for the first time in recorded history, the word ‘brain’ is mentioned. The heart is also mentioned as the center of a distributing system of vessels which pulsates.2 A papyrus purchased by George Embers at Thebes in Egypt, not a copy but an original document written about 16 centuries before the Christian Era, is now considered to be the oldest known anatomical document. It mentions that ‘there are vessels from it [the heart] to all the members’.2 A more accurate impression of the structure of the human body was obtained in ancient Egypt during the period of the New Kingdom (late dynasty XVII through dynasty XX). This was probably possible because of the practice of embalming and mummification which had reached its highest level at that time. In China, one of the oldest civilizations known, the doctrine of Confucianism imposed restrictions upon dissection similar to those later experienced by Galen. Dissection was not practiced, in order not to defile the human body. Nevertheless, the medical scholars of ancient China revealed through their writings a keen sense of awareness of the human body for the treatment of disease. Huang Ti (2600 BC) is the father of Chinese medicine. In his ‘Canon of Medicine’ or ‘Nei Ch’ing’ he writes that ‘all the blood of the body is under control of the heart. The heart is in accord with the pulse. The pulse regulates all the blood and the blood current flows in a continuous circle and never stops’.2 Remarkably, this document clearly recognizes a relationship between blood, pulse, and the heart. It took William Harvey in the 17th century to confirm this knowledge to the Western world. India was yet another ancient civilization in which traditional healing methods and practical skills were remarkably advanced. Much of this knowledge spread slowly via Asia, and reached Europe during the Middle Ages as a result of translations that were made by Persian and Arab scholars in the 11th century. In the Western world, Hippocrates (about 460–377 BC), born on the island of Cos in the Aegean, is considered by many to be the greatest of all physicians and ‘the Father of Medicine’. However, much of the ‘Hippocratic Corpus’, a large collection of philosophical, scientific, and medical works, was written between 300 and 200 BC by a collection of physicians, probably of the medical school of Cos, rather than Hippocrates himself. These writings were further compiled and edited by the scholars of the library at Alexandria.2 Hippocrates is believed to have disliked dissection, and his descriptions were probably based on visual examinations of the body surface and the investigation

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of wounds. His knowledge of internal organs was thus largely speculative. Indeed, in the ancient Greek world, science was considered to be mainly a metaphysical field of study, with direct observations of phenomena being of secondary importance.1 Aristotle (384–322 BC) was the greatest natural philosopher of his era. Although not a physician, he still contributed much to the study of medicine. Born in the city of Stagyra, and son of a court physician to king Philip of Macedonia, Aristotle carried out extensive and fairly accurate studies, including dissections, on a wide range of animals. Like Hippocrates, Aristotle’s knowledge of the human body was derived from external observations and speculation based on his animal dissections. Aristotle mentioned the aorta for the first time in history.2 He stated that it arose from the heart and not from the head and brain as was previously stated by Polybus, Hippocrates’ son-in-law. He also believed, however, that the mind was held in the heart. Charles Darwin thought that Aristotle was the world’s greatest natural scientist, and that Aristotle laid the foundation for comparative anatomy as a result of the animal dissections he carried out and his speculations about the layout of the human body.

THE DEVELOPMENT OF MODERN ANATOMY Anatomy is one of the oldest branches of medicine, and in Western civilizations, the physician and philosopher Claudius Galen is often the most celebrated anatomist of antiquity. Galen was born at Pergamum in AD 129. His father, Aelius Nicon, an architect and builder with an interest in mathematics, logic, and astronomy, planned for his son to study philosophy or politics, the traditional pursuits of the cultured governing clan into which he had been born. But the healing god Asclepius apparently intervened in one of Nicon’s dreams. He was to allow Galen to study medicine. Galen studied medicine for a total of 12 years in Smyrna, Corinth, and at Alexandria. When he returned to Pergamum in AD 157, he worked as a physician in a gladiator school for 3 or 4 years: a very prestigious appointment. These few years provided him with valuable practical experience in trauma and sports medicine. He later regarded wounds as ‘windows into the body’, as dissection was forbidden in imperial Rome. From AD 162, Galen lived mainly in Rome, where he gained a reputation as an experienced physician, and eventually became a court physician to emperor Marcus Aurelius. The rest of his life was spent in the royal court, writing and experimenting.

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Galen expanded his knowledge partly by experimenting with live animals. A ‘party piece’ he frequently exhibited involved publicly dissecting a living pig by cutting its nerve bundles one at a time. Eventually he would cut a laryngeal nerve (now also known as Galen’s nerve) and the pig stopped squealing. It is interesting that acts considered as unacceptably cruel today were considered necessary or even entertaining then. Galen transmitted Hippocratic medicine all the way to the Renaissance, describing the philosopher’s system of four bodily humors linked to the four classical elements. Galen’s anatomical writings were riddled with errors and shortcomings, but he made some important findings which should not be overlooked. He demonstrated that arteries carry blood not air, and he made the first Western studies about nerve functions, brain and heart. He also argued that the mind was in the brain, not in the heart, as Aristotle had claimed. Galen also believed that a tumor might form where there was too much blood in the veins,3 perhaps heralding the later concept of angiogenesis. However, he did not recognize blood circulation, and thought that venous and arterial systems were separate. Despite such errors, Galen’s anatomical writings remained unchallenged up to the time of Versalius in the 16th century. His works took on an almost Christian sacredness. To criticize any of them was life-endangering heresy.4 The Royal College of Physicians of London in 1559 even made one of its members, Dr John Geynes, retract his statement that there were 22 inaccurate passages in the works of Galen. The study of anatomy seemed to die between the fall of Rome and the Renaissance. There appear to be several possible reasons for this decline. First, with Galen’s authority dominating medicine, scientists no longer bothered to experiment, and studies into anatomy and physiology stopped. The dying of the Roman Empire also appeared to deaden intellectual, artistic, and scientific activity, and the almost universal prohibition of dissection of human-beings caused problems. Although the art of healing was highly regarded in the Middle Ages, anatomy, being concerned with the dead, was considered immoral and irreligious. It was not until the start of the Renaissance that a few Italian city-states (Bologna, Padua, and Pavia) began to permit the dissection of a few executed criminals each year. One of the first of these human dissections was carried out at the University of Bologna by the anatomist Mondino de Luzzi (1276–1326). The subject was an executed female, and Mondino sat reading from the work of Galen whilst his assistant performed the dissection. Mondino’s ‘Anathomia’ was the first modern work to deal exclusively with anatomy. However, he never questioned the authoritative writings of Galen, even when the findings were contradictory.

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Many famous artists of the early Renaissance pursued the study of the human body, including actual dissections, in order to show the beauty of the human form in an accurate and realistic manner. This union of art and science brought the study of human anatomy onto a new, promising, and irreversible course2. In more than 750 anatomical drawings of the musculoskeletal, vascular, nervous, and urogenital systems, Leonardo da Vinci produced work of unchallenged artistic beauty and scientific accuracy that quickly transcended the needs of the artist and drifted into the scientific pursuit of anatomy for its own end. Leonardo placed a great deal of importance on the laws of geometry and mechanics as applied to the human form. He remarked: ‘let no man read me who is not a mathematician. No human investigation can lay claim to being true science unless it can stand the test of mathematical demonstration. The man who undervalues mathematics nourishes himself upon confusion’. The mathematician and physiologist Fick, whom we will discuss later, would have been delighted at Leonardo’s sentiments. Andreas Vesalius (1514–1564) has been described as the most commanding figure in European medicine between Galen and Harvey.2 Born in Brussels, Belgium, he came from a distinguished family of physicians. After studying at Pedagogium Castri and Collegium Trilingue at Louvain, he entered the distinguished but extremely conservative medical school of the University in Paris in 1536. Few dissections were carried out at the University, as all teaching continued to be based on Galenism. In 1537, Versalius returned to Louvain from Paris, and the following year he conducted one of the first human dissections to be held in the city for 18 years. He later traveled to Venice and received his Doctor of Medicine degree from the University of Padua. On the following day, the senate of Venice appointed Versalius Professor of Surgery, with the responsibility also for Anatomy at the University. This progression to professor has to be rapid by anyone’s standard! Although a Galenist at first, by studying the human body itself, Versalius began to reveal differences from the descriptions made by Galen. In 1540, Versalius made a dramatic demonstration in Bologna of the skeletons of a man and an ape and demonstrated more than 200 differences where Galen was mistaken with respect to the human body but not to that of the ape. Versalius’ book ‘Fabrica’ (finished in 1542) was based upon actual dissection and original observations. Although his work was initially considered outrageous by the Galenists of the time, it was rapidly acknowledged as an outstanding exposition of the true structure of the human body. Its publication marked a new era

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in medicine and the beginning of modern anatomy. Versalius died in 1564 upon return from a hazardous pilgrimage to Jerusalem, and after the publication of ‘Fabrica’ no other noteworthy anatomical book was to appear for over a century.

ANATOMY AND IMAGING The ability to depict the internal anatomy of the human body through non-invasive imaging rather than dissection or surgery dates back to the discovery of X-rays in 1895 by Wilhelm Conrad Röntgen (1845–1923).4 Whilst Chair of Physics at the University of Würzburg, Röntgen had been studying the phenomena associated with the passage of electricity through a gas at extremely low pressure, using an evacuated glass tube developed by Sir William Crookes (1832–1919). On November 8, 1895, Röntgen had enclosed the Crookes tube in a sealed, thick black carton to exclude all light. When the electric current was switched on, he noticed that a paper plate coated with barium platinocyanide began to fluoresce, even when it was as far as 2 meters away from the tube. He deduced the existence of hitherto unknown rays, dubbing them ‘X-rays’. The now famous radiograph of the hand of Röntgen’s wife, Bertha, was taken within 1 month of his discovery. The shadow cast by her bones and the ring on her finger were clearly visible, surrounded by a penumbra produced by the flesh. In December of that year, Röntgen published his findings in the Proceedings of the Würzburg Physical–Medical Society in an article entitled ‘On a New Kind of Ray: A Preliminary Communication’. By January 1896, X-rays were being used in several countries around the world to diagnose fractures and to detect radio-opaque foreign bodies such as bullets. The use of X-rays for diagnosis and therapy grew rapidly thereafter. In 1901, Röntgen was awarded the Nobel Prize for Physics in recognition of his discovery. The means of using X-rays to depict the anatomy of the circulation came in the 1920s with the development of cerebral angiography by the Portuguese neurosurgeon Egas Moniz (1874–1955).5 Whilst Professor of Neurology at Lisbon, Moniz had sought to identify a radioopaque dye that was non-toxic and would pass through the capillaries without causing a blockage. Working first on animals and cadavers before moving to human subjects, Moniz tried injections of air, bromides, and iodides. The X-ray attenuating properties of the iodine atom form the basis of contrast agents used for angiography and CT perfusion today (Figure 1.1). However, iodine-containing compounds with lower toxicity than the simple iodides used by Moniz were developed

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Figure 1.1 Venous phase of a cerebral angiogram depicting the great cerebral vein of Galen. Although Galen had a detailed knowledge of anatomy, he believed that the arterial and venous systems were separate. The ability to depict the anatomy of the cerebral circulation non-invasively came with the development of cerebral angiography by Moniz in the 1920s

in the 1930s, when it was noticed that iodine-containing products intended to improve the treatment of syphilis, were radio-opaque when excreted in the urinary system. Based on the results of his technique, Moniz also described the formation of new vessels in brain tumors.6 In 1949 Moniz received the Nobel Prize for Physiology or Medicine, more for his discovery of prefrontal leukotomy than for the development of cerebral angiography. The development of CT by Hounsfield and Ambrose in 1973 represented a major advance in the ability of imaging to demonstrate anatomy.7,8 By providing a means to depict the human body in crosssection, CT was able to reveal brain structures that had been invisible on radiographs until then. At the time of his discovery, Sir Godfrey Hounsfield (1919–2004) was an electrical engineer working in the United Kingdom for EMI Ltd. He came up with the concept that it was possible to determine the contents of a box by taking X-ray readings at multiple positions around the object. The practical computer-based methodology he subsequently devised to achieve this aim proved to be consistent with mathematical theory that had been described by the

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Austrian Johann Radon in 1917. At the time of his work, Hounsfield was unaware of similar developments in the USA by the physicist Allan McLeod Cormack. In 1979, Hounsfield and Cormack shared the Nobel Prize for Physiology or Medicine. Early experiments on a prototype CT scanner built by Hounsfield are said to have included CT imaging of a cow’s head obtained from a butcher’s shop.4 Initial images were disappointing in that they visualized none of the details of brain structure, including the ventricles. His coworker, Ambrose, suggested that anatomical detail might have been obscured by damage to the brain resulting from the blow to the head used to kill the cow. His theory was proved correct when the experiment was repeated using a cow’s head from a Kosher butcher, for which the means of slaughter had been exsanguination. The images obtained on this occasion displayed the internal brain structure with beautiful clarity. The first clinical CT system was installed at the Atkinson Morley’s Hospital in Wimbledon, London. The subsequent expansion of CT into clinical practice was extraordinarily rapid. The improved visualization of brain tumors afforded by using contrast media developed for angiography and urography was realized very rapidly. Today, along with magnetic resonance imaging and ultrasound, the capacity of CT to produce highly detailed images of the internal structure of the human body is used not only for diagnostic purposes but also as a valuable adjunct to the teaching of anatomy in medical education.9

PHYSIOLOGY AND THE CIRCULATION The science of physiology, the concern for the internal processes and functioning of the body as opposed to just the structure or anatomy, really took off as a dynamic new science with William Harvey’s demonstration of the circulation of the blood. In fact, several people, namely Servetus, Colombo, and Cesalpino (16th century Europeans) had already discovered the pulmonary circulation, but it was Harvey who became famous for publicizing it to the whole world in his book, Exercitatio Anatomica de Motu Cordis et Sanguinis in Animalibus (1628), which for centuries afterward was universally known as ‘de motu cordis’. This book was written within a few years of another two famous English books: the King James’ authorized version of the Bible (1611) and the Folio edition of Shakespeare’s plays (1623). All three books went down in history as essential reading in their respective fields4. Harvey’s book was the first significant medical book ever to be published by an English scholar.

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Harvey was born in 1578 at Folkestone, England, and after his graduate degree at Cambridge he travelled to Padua for his medical training, where Versalius had held the Chair of Anatomy. He returned to London at the age of 24 and soon became the royal physician attending James I. He was described as an aloof man with a displeasing disposition, but he was deeply respected for his scientific knowledge. He spent his whole life dissecting as many different animals and people as he could obtain. Harvey was very careful not to ridicule any concept of Galen. Through his lectures and vivisections, he painstakingly worked at repeatedly demonstrating and convincing the English medical profession of his new ideas before he put them down in writing. Versalius gave medicine a magnificent anatomical view of the body, but Harvey built on Versalius’ anatomy to give medicine the full picture of how the heart worked and how blood moved to bring life to that body. Although Harvey gave Galen some credit for his thoughts on the circulation, Harvey failed to mention that he himself had been aware of the advanced ideas of Servetus, Colombo, and Cesalpino. Quantitative measurements play a fundamental part in physiology, and, interestingly, the first device for quantifying circulatory parameters predates Harvey’s work by 25 years. In his work of 1603 entitled Method vitandorum errorum omnium qui in arte medica contingent [Methods of avoiding all errors pertaining to the art of medicine], Santorio Santorio (1561–1636), later Professor of Theoretical Medicine at the University of Padua, describes an instrument for measuring the pulse rate.10 A jump to 19th century Germany shows us the next important piece of the jigsaw which helps to make up the complete picture of the development of CT perfusion. Adolf Fick (1829–1901) had a remarkable talent for mathematics and physics, but was persuaded to study medicine by his elder brother, Heinrich. Heinrich, a professor of law, realized that medicine would benefit from Adolf’s talents in other areas. Soon after completing his medical degree, Fick (Figure 1.2) turned his attentions to physiology, eventually accepting the Chair of Physiology at Würzburg. In his Medical Physics,11 Fick introduced profound ideas on physiological problems such as the mixing of air in the lungs, measuring carbon dioxide output in humans, and the work of the heart. This book was the first of its kind, and included studies of the hydrodynamics of the circulation. Throughout his life, Fick contributed a steady stream of information on all three disciplines of mathematics, physics, and medicine. Even though his major work was on the physiology of muscle contraction, he used his knowledge to demonstrate how mass balance could be used to measure cardiac output. The concept, now known as the Fick Principle, was published in the Proceedings

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Figure 1.2 Adolf Fick, who will be remembered for expanding physiology to new dimensions by incorporating mathematics and physics at a new, advanced level. (Reproduced with permission from reference 1)

of the Würzburg Physical–Medical Society for July 9, 1870. Interestingly, the preceding item in the Proceedings announced Röntgen’s election to the society. As was typical of Fick, he did not attempt to advance or investigate the proof of his principle, and it took until 1886 for Grehaut and Quinguad to validate the Fick Principle which forms the basis of some CT perfusion algorithms in use today. The localization of tumors on the basis of their circulatory properties began in the late 1940s. George Moore, a young surgeon in his late 20s training at Minneapolis, described the use of radioactive diiodofluorescein for the diagnosis and localization of brain tumors.12 Interestingly, Moore’s work was published in the same year as Seymour Kety’s first description of a non-invasive method to quantify cerebral perfusion in humans by application of the Fick Principle to measurements of the arterial and venous concentrations of a freely diffusible tracer.13 To this day, the Kety–Schmidt method remains the basis for tissue perfusion imaging using [15O]water positron emission tomography and stable xenon CT.

PERFUSION COMPUTED TOMOGRAPHY: COMBINING ANATOMICAL IMAGING AND QUANTITATIVE PHYSIOLOGY The anatomical imaging modality of CT and the principles of tracer theory and physiological imaging began to be combined within a few years of Hounsfield’s description of CT. The earliest use of CT for

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quantifying vascular physiology comprised attempts to measure cerebral blood volume from contrast-enhanced CT in the mid-1970s,14,15 but the physiological models adopted resulted in error due to omitting the effects of leakage of contrast medium into the extravascular space. In 1980, Leon Axel, working in the Department of Radiology at the University of California, described a method that used deconvolution to determine cerebral blood flow from rapid-sequence contrast-enhanced CT,16 with the production of parametric images presented the following year.17 However, at that time the technique was constrained by the slow speed of image acquisition and data processing of the commercially available conventional CT systems. Nevertheless, the slower acquisition protocols required for the estimation of blood–brain barrier (BBB) permeability were feasible, and the first CT-derived parametric images of BBB permeability in human brain tumors were published by Groothius et al. in 1991.18 The introduction of spiral CT systems in the early 1990s made it possible to acquire images with the high frequency required for reliable perfusion imaging. Hitherto, such acquisition protocols had only been achievable using electron beam CT systems, for which availability was extremely low. The first report of CT perfusion imaging using a spiral system was by Miles (Figure 1.3) et al., working at Addenbrooke’s

Figure 1.3 Ken Miles (left), one of the first to implement perfusion computed tomograpy (CT) on spiral CT systems, receiving from Sir Godfrey Hounsfield (right), the inventor of CT, a certificate commemorating the 1999 Sir Godfrey Hounsfield Lectureship of the British Institute of Radiology

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Hospital, Cambridge, UK.19 Calculating perfusion from the maximal rate of tissue enhancement, the analysis method adopted was based on the Fick principle rather than the deconvolution approach described by Axel. In 1992, the Cambridge group presented the first application of this technique to tumor imaging to the Radiological Society of North America, demonstrating CT perfusion images of liver tumors, which were later published in Radiology.20 Within a short time, spiral CT systems became widely available, to be followed by a similar expansion of multidetector CT. The ability to use spiral CT for perfusion imaging, combined with the subsequent release of commercial software to perform CT perfusion analysis, finally made CT perfusion a practical technique for the clinical investigation of tumors, as described in the succeeding chapters of this book.

REFERENCES 1. Fishman AP, Richards DW. Circulation of the Blood: Men and Ideas. New York: Oxford Univesity Press, 1964. 2. Persaud TVN. Early History of Human Anatomy: From Antiquity to the Beginning of the Modern Era. Illinois: Charles C Thomas, 1984. 3. Porter R. Clinical science. In: The Greatest Benefit to Mankind: a Medical History of Humanity from Antiquity to the Present. London: HarperCollins, 1997: 561–96. 4. Friedman M, Friedland GW. Medicine’s 10 Great Discoveries. New Haven: Yale University Press, 1998. 5. Ferro JM. Egas Moniz (1874–1955). J Neurol 2003; 250: 376–7. 6. Doby T. Victory in an important area: cerebral angiography (1926–1931). In: Development of Angiography and Cardiovascular Catheterization. Littleton, MA: Publishing Sciences Group, 1976: 73–93. 7. Hounsfield GN. Computerized transverse axial scanning (tomography). I. Description of system. Br J Radiol 1973; 46: 1016–22. 8. Ambrose J. Computerized transverse axial scanning (tomography). II. Clinical application. Br J Radiol 1973; 46: 1023–47. 9. Miles KA. Diagnostic imaging in undergraduate medical education: an expanding role. Clin Radiol 2005; 60: 742–5. 10. Gedeon A. Science and Technology in Medicine: an Illustrated Account Based on Ninety-nine Landmark Publications for Five Centuries. New York: Springer, 2006. 11. Fick A. Compendium der Physiologie des Menschen mit Einschluss der Entwickelungsgeschichte. Wien: Leipzig, 1860. 12. Moore GE. Use of radioactive diiodofluorescein in diagnosis and localization of brain tumours. Science 1948; 107: 569–71. 13. Kety SS, Schmidt CF. The nitrous oxide method for the quantitative determination of cerebral blood flow in man: theory, procedure and normal values. J Clin Invest 1948; 27: 476–83. 14. Penn RK, Walser R, Ackerman L. Cerebral blood volume in man. JAMA 1975; 234: 1154–5.

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15. Zilkha E, Ladurner G, Iliff LD, Du Boulay GH, Marshall J. Computer subtraction in regional cerebral blood-volume measurements using the EMI-scanner. Br J Radiol 1976; 49: 330–4. 16. Axel L. Cerebral blood flow determination by rapid-sequence computed tomography: theoretical analysis. Radiology 1980; 137: 679–86. 17. Berninger WH, Axel L, Norman D, Napel S, Redington RW. Functional imaging of the brain using computed tomography. Radiology 1981; 138: 711–16. 18. Groothius DR, Vriesendorp FJ, Kupfer B et al. Quantitative measurements of capillary transport in human brain tumours by computed tomography. Ann Neurol 1991; 30: 581–8. 19. Miles KA, Hayball M, Dixon AK. Colour perfusion imaging: a new application of computed tomography. Lancet 1991; 337: 643–5. 20. Miles KA, Hayball MP, Dixon AK. Functional images of hepatic perfusion obtained with dynamic computed tomography. Radiology 1993; 188: 405–11.

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2 Scientific basis and validation Ting-Yim Lee and Errol Stewart

INTRODUCTION In this chapter, the theory of computed tomography (CT) tumor perfusion measurement will be discussed. Because blood attenuates X-rays uniformly on the scale of the spatial resolution of a CT scanner, flowing blood cannot be differentiated from stationary blood. To measure tumor perfusion with CT, contrast is injected intravenously, to ‘label’ the blood. Assuming that the injected contrast is uniformly mixed with blood, tracing blood through the tumor circulation is equivalent to tracking a bolus of contrast through the tumor. As such, we can make use of the extensive literature on tracer kinetics modeling in the measurement of CT tumor perfusion. Note that ‘tumor’ in this discussion can be broadened to include peritumoral and normal tissue, since the same consideration would apply in their cases; furthermore we can use the terms perfusion and blood flow interchangeably. Also, in the diagnosis of tumor or the study of tumor biology, it is highly advantageous that besides perfusion we can measure additional functional parameters, as discussed in the following section, in the same study. The fundamental processes underlying CT measurement of tumor perfusion and associated hemodynamic (functional) parameters are the transport by blood flow of an intravenously administered iodinated contrast agent to the tumor and exchange by diffusion of these contrast molecules between the intravascular space and the extravascular interstitial space.1–10 With the current fast CT scanners, both tissue and vascular contrast concentrations can be measured and traced over time at short intervals to allow detailed modeling of the distribution of contrast agent in tissue. Both compartmental and distributed parameter models for contrast transport and exchange have been developed to quantify tumor blood flow, blood volume, mean transit time, and capillary permeability surface area product.1–10 These parameters as

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well as the models used to measure them will be further discussed in the following sections.

DEFINITION OF BASIC TERMS AND DISCUSSION OF FUNDAMENTAL RELATIONSHIPS In modeling the transport and distribution of X-ray contrast agent in tissue, the following terms and fundamental relationships need first to be discussed. Blood flow (F) This is the volume flow rate of blood through the vasculature in a tumor. It is usually expressed in units of ml/min/100g. Note that blood flow measured with CT using the methodology discussed here includes flow in large vessels, arterioles, capillaries, venules, and veins. In particular, flow in arteriovenous shunts will also be included. Blood volume (Vb) This is the volume of blood within the vasculature in a tumor that is actually ‘flowing’. Again, as in the case of blood flow, blood volume includes blood in large vessels, arterioles, capillaries, venules, and veins. Any stagnant pool of blood will not be included in the blood volume. It is measured in units of ml/100g. Mean transit time (Tm) This is the average time taken by blood elements to traverse the vasculature from the arterial end to the venous end in a tumor. If the perfusion pressure (or pressure head) is high, blood elements are traveling at a higher velocity, resulting in a shorter mean transit time than when perfusion pressure is low. In this sense, mean transit time is a surrogate measure of perfusion pressure.11 Mean transit time is usually measured in seconds. Distribution volume (Vd) This is defined as the volume within which the mass of contrast agent in a unit mass tumor would be distributed at the concentration of the blood with which the tumor is at equilibrium. If Me is the mass of contrast agent within a component of the unit mass tumor, for example the interstitial space, and Cb is the blood concentration, then the distribution volume in the interstitial space, Ve, is Me/Cb and is in units of ml/100g. Note that the total distribution volume of contrast agent in the unit mass tumor Vd = Ve + Vb. Capillary permeability surface area product (PS) The product of permeability and blood concentration of a solute gives the unidirectional diffusional flux from blood to interstitial space per unit surface area of capillary endothelium.11,12 PS is the product of permeability and the total surface area of capillary endothelium in a unit mass of tumor

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(usually 100g), and hence is the total diffusional flux across all capillaries. It is measured in units of ml/min/100g. Expressed in the stated units, PS can be interpreted as the unidirectional flux of solutes from blood plasma to intersitial space that is equivalent to the complete transfer of all the solutes in PS milliliters of blood per minute to the interstitial space. Note that PS is implicitly tied to the experimental set-up, in which a permeable membrane is separating two solvents containing a solute at different concentrations. It is equal to the diffusional flux of solute across the whole permeable membrane (capillary endothelium) when the solvent (blood) is ‘stationary’ with respect to the membrane. For the more physiological case of blood flowing through capillaries, the unidirectional flux of blood-borne solutes through all the capillaries is dependent on blood flow and PS. This relationship will be discussed in the following. Extraction efficiency (fraction) (E) This is the fraction of solutes present in arterial inlets, with the potential to diffuse into the interstitial space, that actually becomes transferred from blood to interstitial space during a single passage of blood from the arterial end to the venous end of the capillaries of a tumor.13 The mass of solute transferred to the interstitial space is F ⋅ (Ca − Cv), where Ca is the arterial and Cv is the venous concentration of the solute. The mass of solute delivered to the tissue which can diffuse into the interstitial space is F ⋅ (Ca – Ce) until the arterial concentration approaches the interstitial concentration, Ce. An operational definition of E is, therefore: E=

C a − Cv Ca − C e

(1)

Transfer flux of solute through the capillary endothelium The Johnson and Wilson model is a convenient starting point for this discussion.14,15 In this model (Figure 2.1), it is assumed that the tumor consists of capillaries and interstitial tissue. All the capillaries are lumped together as a single cylinder of length L and volume Vb. The interstitial tissue is assumed to be a cylindrical annulus around the capillary. As the blood-borne solute enters the capillary, it starts to diffuse across the capillary endothelium, and thus the blood concentration of solute Cb will be a function of both axial position, x, along the capillary as well as time, t. The interstitial concentration of solute is Ce(t) and depends only on time, i.e. the interstitial space is treated as a

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Interstitial space Ce(t), Ve

FCa(t)

Intravascular space Cb(x,t), Vb

FCv(t)

PS

x

Figure 2.1 The Johnson and Wilson model for the distribution of blood-borne solutes in tumor. The symbols are explained in the text. Solute concentration in the intravascular (blood) space, Cb(x,t), is dependent on position along the capillary, to reflect that it is decreasing from the arterial (Ca(t)) to the venous (Cv(t)) end of the capillary. The interstitial space is assumed to be a compartment with no concentration gradient within it

‘well-stirred’ compartment. The transport and exchange of solute through the capillaries can be described by the following equation: ∂C b ( x,t ) FL ∂C b ( x,t ) PS + + ⎡ C ( x,t ) − Ce ( t ) ⎤⎦ = 0 ∂t Vb ∂x Vb ⎣ b

(2)

For the case when Ce(t) is a constant, say Ce, equation (2) has the solution: PS − x V ⎞ − PS x V ⎞ ⎛ ⎛ C b (x,t) = Ca ⎜ t − b x ⎟ e FL + Ce − Ce e FL H ⎜ t − b x ⎟ FL ⎠ FL ⎠ ⎝ ⎝

(3)

where H(t) is the unit step function. As expected, at x = 0, Cb(x,t) = Ca(t), and: PS ⎛ − ⎞ V ⎞ − PS ⎛ C v ( t ) = C b ( x,t ) x = L,t ≥ Vb = Ca ⎜ t − b ⎟ e F + Ce ⎜ 1 − e F ⎟ F⎠ ⎝ ⎠ ⎝ F

Further, the arteriovenous difference can be written as: PS ⎛ − ⎞ Ca ( t − Tc ) − C v ( t ) = ⎜ 1 − e F ⎟ ⎡⎣ Ca ( t − Tc ) − Ce ⎤⎦ ⎝ ⎠

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where Tc = Vb/F is the capillary transit time. Thus: E≈

PS − Ca ( t − Tc ) − C v ( t ) = 1− e F Ca ( t − Tc ) − Ce

(4)

as Crone13 and Renkin16 had previously derived (equation (1)). Moreover, Fick’s law gives the change in the interstitial concentration as: dCe ( t ) = F ⎡⎣Ca ( t − Tc ) − C v ( t ) ⎤⎦ dt

which, according to equation (4), can also be expressed as:

Ve

dCe ( t ) = FE ⎡⎣Ca ( t − Tc ) − Ce ⎤⎦ dt

The last equation can be interpreted as that the forward flux from the capillary to the interstitial space is FE·Ca(t − Tc), and the backflux from the interstial space to the capillary is FE⋅Ce. Thus, FE is the unidirectional flux of solute per unit concentration, or transfer constant, from blood to interstitial space or from interstitial space to blood. The above derivation is obtained under the special case when Ce(t) is held constant in time. For the general case when Ce(t) is an arbitrary function of time, St Lawrence and Lee7 have shown that the unidirectional flux of solute per unit concentration is still FE. There exist three regimes for the exchange of solute between blood and interstitial space: (1) when PS > F, so that FE approaches F, the exchange is flow limited; and, (3) when PS is of the same magnitude as F, the exchange is neither diffusion nor flow limited.

Central volume principle Blood flow, blood volume, and mean transit time are related via the central volume principle,17 which states that: F=

Vb Tm

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This principle applies even for the case of non-intravascular (diffusible) solutes, provided that the distribution volume is extended to include the portion of the interstitial space occupied by the solute at equilibrium.18 X-ray contrast agent In general, this is a derivative of iodobenzoic acid with a molecular weight of about 700 daltons and is hydrophilic and inert. Because of the latter properties, it is excluded from the intracellular space in both blood and parenchymal tissue, and only distributes in blood (plasma) and interstitial (extravascular extracellular) space. However, for convenience in this chapter, blood flow and volume are normalized with respect to whole blood. The whole blood normalized quantities can be converted into plasma normalized quantities by multiplying by the factor: 1 − r·H, where H is the hematocrit of blood determined using samples from large peripheral vessels, and r is the ratio of small vessel (at the tissue level) to large vessel hematocrit.19–22 CT enhancement This refers to the increase in X-ray attenuation in either blood or tumor in the presence of X-ray contrast agent. Enhancement is usually measured in Hounsfield units (HU or CT number), and is related to the concentration of contrast agent present. The important advantage of CT relative to magnetic resonance (MR) imaging is that enhancement is linearly related to contrast agent concentration and is independent of the microenvironment present at the tissue level.23 A rule of thumb is that 1 mg of iodine per ml will lead to an enhancement of around 30 HU when scanning at 80kVp, independent of how the contrast agent is distributed within the 1-ml volume.23 For CT tumor perfusion studies, both blood and tumor enhancements are measured in equivalent HU within the same CT image so that crosscalibration is not necessary.

IMPULSE RESIDUE FUNCTION AND CONVOLUTION In tracer kinetics modeling, the impulse residue function (IRF)24 and the mathematical operation of convolution are frequently used to simplify and provide insight into the dual processes of the convective transport by blood flow of intravenously administered iodinated contrast agent to the tumor, and the exchange by diffusion of these contrast molecules between the intravascular space and the extravascular interstitial space. In the following, the IRF will be defined and the convolution operation explained in more detail.

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Impulse residue function Considering a certain mass of tumor, tumor perfusion (F) carries with it contrast at a concentration of Ca(t). The tumor time–density curve (TDC), Q(t), as measured by dynamic CT scanning, is also called the tumor residue function. In the special case when F·Ca(t) is a delta function, such that a unit mass of contrast is deposited in the tumor instantaneously at time zero as a bolus, then the corresponding tumor residue function has the general shape as shown in Figure 2.2a. The mathematical form or numerical representation of the tissue residue function corresponding to the instantaneous deposition of unit mass of contrast is called the impulse residue function (IRF).24 We will give an intuitive explanation of Figure 2.2 in this section, and a more vigorous (mathematical) justification will be presented later when kinetics modeling is discussed. There are two distinct phases in the IRF shown in Figure 2.2a. In the first or vascular phase, there is an abrupt rise, because the contrast is injected directly into the arterial input, a plateau of duration that is equal to the minimum transit time, Tmin, through the vasculature of the tumor, and a decrease at Tmin. Note that if blood flows as a ‘plug’ through the tumor vasculature, as is assumed in distributed parameter models14 to simplify the modeling, Tmin is also equal to the

b 70

1.2 1 0.8 0.6 0.4 Tmin = Tm =

0.2

Vb F

Flow scaled impulse residue function (ml min−1 (100 g)−1

Impulse residue function (no units)

a

60 50

Vb

40 30 20

Tmin = Tm =

Vb F

10 0

0

0

5

10 15 20 25 30 35 40 Time (s)

0

5

10 15 20 25 30 35 40 Time (s)

Figure 2.2 (a) The impulse residue function according to the Johnson and Wilson model. The symbols are defined in the text. (b) The blood flow scaled impulse residue function is obtained by scaling the impulse residue function in (a) by blood flow, which in this example is 60 ml/min/100 g

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mean transit time (Tm). As the mass of contrast agent injected is unity, the plateau height in the vascular phase is also unity. The drop at Tmin represents the fraction of contrast injected that is not extracted by the tumor and is carried out with the venous blood. The second or interstitial phase begins at Tmin and shows a slow decay towards baseline. The initial height of the second or interstitial phase is the fraction of contrast that is extracted by the tumor, or it is equal to the extraction efficiency of contrast by the tumor. The slowly decaying interstitial phase represents the return of the extracted contrast from the tumor to the bloodstream and then clearance via blood flow. The tumor IRF can be interpreted as the fraction of contrast that remains (‘resides’) in the tumor after an ‘impulse’ injection of unit mass of contrast, and as such is unitless. If the amount deposited is M0 instead, then the corresponding tissue residue function is the product of M0 and the IRF and in this case M0·IRF has the same units as the tissue residue function. The IRF is a theoretical concept and cannot be measured easily in clinical practice since it requires, as a close approximation, an intra-arterial bolus injection into one of the supply arteries of the tumor.

Convolution An intravenous injection of contrast gives rise to an arterial input TDC, Ca(t), which is not a delta function, and the corresponding tumor residue function or tumor time–density curve, Q(t), is related to Ca(t) via the impulse residue function as follows:17 t

Q(t) = F ⋅ Ca (t) ⊗R(t) = F ⋅ ∫ Ca ( u ) ⋅ R ( t − u ) ⋅ du

(5)

0

where ƒ is the convolution operator. The product of F·Ca(u)·∆u gives the amount of contrast deposited in a small time interval ∆u at time u, and as explained below with the help of Figure 2.3, the convolution operation is a generalized multiplication of the mass of contrast deposited at different times (t′) and the IRF. If blood flow is unchanged (i.e. stationary) between two identical ‘unity’ bolus injections as shown in Figure 2.3a, then the tumor TDC corresponding to each injection is the IRF discussed above, resulting in Figure 2.3b. In addition, if enhancement measured with a CT scanner is linear with respect to tumor concentration of contrast, the tumor TDC

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a

b 2.0 Tissue conc. (HU/g)

Arterial conc. (HU/ml)

1.2 1.0 0.8 0.6 0.4 0.2 0.0

1.5 1.0 0.5 0.0

0

15 30 45 60 75 90 105 Time (s)

c

d Tissue conc. (HU/g)

Arterial conc. (HU/ml)

200 160 120 80 40

0

15 30 45 60 75 Time (s)

90 105

0

15 30 45 60 75 90 105 Time (s)

0

15 30 45 60 75 90 105 Time (s)

10 8 6 4 2 0

0 0

15 30 45 60 75 90 105 Time (s)

e

f Tissue conc. (HU/g)

350 Arterial conc. (HU/ml)

23

300 250 200 150 100 50

30 25 20 15 10 5 0

0 0

15 30 45 60 75 90 105 Time (s)

Figure 2.3 Graphical illustrations of the convolution operation involving an arterial time–density curve (TDC) and impulse residue function (IRF). A blood flow of 60.0 ml/min/100 g is assumed in the illustrations

corresponding to the case in Figure 2.3c, two bolus injections of different amounts of contrast, is Figure 2.3d. Finally, a general arterial TDC can be represented as a series of bolus injections, as in Figure 2.3e. For each of these bolus injections the CT scanner measures a response that is the product of the IRF, blood flow, and the arterial concentration

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at each of the injection times. The resultant tumor TDC (Figure 2.3f) in response to the general arterial concentration Ca(t) is the sum of all the scaled IRFs after they have been shifted in time in accordance with the times of their corresponding bolus injections. In summary, if the IRF is known, the corresponding tumor TDC in response to the arterial TDC, Ca(t), can be obtained as a summation of scaled and time shifted IRFs. The scale factors and time shifts are given by F⋅Ca(t) and t. This operation, as illustrated in Figure 2.3f, is called a convolution.

Flow scaled impulse residue function and model deconvolution Equation (5) can be rewritten as: Q( t ) = Ca ( t ) ⊗ ⎡⎣ F ⋅ R ( t ) ⎤⎦

(5a)

where F·R(t) is the blood flow scaled impulse residue function (FIRF). Note that whereas IRF is unitless, FIRF has the unit of blood flow: ml/min/100g. Convolution makes it possible in our discussion to separate the influence of the details of the administration of contrast from the effects of the inherent properties of the tumor on the tumor residue function, Q(t). The arterial input TDC in equation (5a), Ca(t), is governed by factors such as injection rate and dosage related to the contrast injection. It is also affected by factors related to the central hemodynamics of the subject, such as ejection fraction and heart rate. The blood flow scaled impulse residue function, F⋅ R(t), reflects tumor blood flow F and other inherent properties of the tumor. Furthermore, for inert contrast agent, it is possible as shown in the following section to determine the functional (mathematical) form of the FIRF in terms of tumor blood flow (F), blood volume (Vb), mean transit time (Tm), distribution volume (Ve), and capillary permeability surface area product (PS) or extraction efficiency (E) of contrast agent. In CT tumor perfusion studies, both Q(t) and Ca(t) are measured, and the FIRF is convolved with the measured Ca(t) to obtain a predicted ˜ (fitted) tumor residue function, Q(t). Model deconvolution of the flow scaled impulse residue function from the measured Q(t) and Ca(t) determines values for the set of parameters: F, Vb, Ve, and PS (E) iteratively by adjusting their values, and hence the corresponding FIRF, and ˜ checking the deviations of Q(t) from Q(t) at each iteration until the minimum is achieved.

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COMPUTED TOMOGRAPHY TUMOR PERFUSION MEASUREMENT WITHOUT THE NEED FOR KINETICS MODELING Before a more thorough discussion of kinetics modeling to derive the tumor FIRF, a method for CT tumor perfusion measurement that is independent of the model assumed for the transport and exchange of contrast between blood and tissue is first discussed in this section. The method was first proposed by Peters et al. for nuclear medicine applications,25 and has been adapted by Miles26 for CT perfusion measurements.

Fick principle Considering a mass of tumor, F is the perfusion, Ca(t) is the contrast concentration in the arterial inlet(s), and Cv(t) is the contrast concentration in the venous outlet(s). At time T, the accumulated mass of contrast from the arterial in-flow, Qin(T), is: T

Qin ( T ) = F ⋅ ∫ Ca ( t ) ⋅ dt 0

The accumulated mass of contrast from venous outflow is: T

Q out ( T ) = F ⋅ ∫ C v ( t ) ⋅ dt 0

The mass of contrast in the tumor at time T, Q(T), is, by conservation of mass or the Fick principle:27 T

Q( T ) = F ⋅ ∫ ⎡⎣ Ca ( t ) − C v ( t ) ⎤⎦ ⋅ dt

(6)

0

Equation (6) states that the accumulated mass of contrast in the tumor over a time period [0, T] is equal to the product of tumor perfusion and the time integral of the arteriovenous difference in contrast concentration.

No outflow assumption One immediate simplification is to assume that during the time period [0, T] the venous concentration, Cv(t), or the venous outflow, is

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equal to zero. This assumption is valid if T is less than the minimum transit time of the tumor. If the ‘no venous outflow’ assumption holds, equation (6) simplifies to:26 T

Q( T ) = F ⋅ ∫ Ca ( t ) ⋅ dt 0

which can be rewritten in a form that facilitates the calculation of tumor perfusion or blood flow, F, as: ⎡ dQ( t ) ⎤ ⎢ dt ⎥ = F ⋅ Ca ( T ) ⎣ ⎦t=T

(7)

In particular, the rate of contrast accumulation in tissue dQ(t)/dt will be maximal when the arterial concentration is at its maximum: ⎡ dQ( t ) ⎤ ⎢ dt ⎥ = F ⋅ ⎡⎣ Ca ( t ) ⎤⎦ max ⎣ ⎦ max

(7a)

Equation (7a) states that tumor perfusion is the ratio of the maximal rate of accumulation of contrast in tissue or the maximum slope of Q(t) to the maximum arterial concentration. For this reason, calculation based on equation (7a) is also called the maximum slope method. Also, in order to satisfy the no venous outflow assumption, a relatively high injection rate (15–20 ml/s) has to be used.28 To avoid the no venous outflow assumption, Cv(t) has to be measured. This will be possible only if we can identify draining veins. If we are interested in measuring blood flow of the whole organ, for example the brain, then the jugular vein can be used. This technique was used by Kety and Schmidt to measure global brain blood flow in human subjects in the 1940s.29 For regional tumor blood flow measurement, the local draining veins have to be identified and contrast concentration in them measured. In general, this is not possible. When there is significant outflow of contrast with venous blood, equation (6) suggests that the true perfusion would be underestimated by equation (7a).

KINETICS MODELING Here the discussion is limited to modeling methods that can simultaneously measure at least more than one from the set of parameters: blood

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flow, blood volume, mean transit time, capillary permeability surface area product, and extraction efficiency.

Compartment models A good example of the use of compartmental models to describe the distribution of blood-borne solutes in tissue was the study by Blasberg and Patlak.30,31 Groothuis et al.32,33 were the first to apply compartmental models to describe the distribution of X-ray contrast agent in tumor, and used CT to study this distribution in canine and human brain tumors. Since X-ray contrast agents are hydrophilic, they are excluded from the intracellular space both in blood and in parenchymal tissue. Furthermore, because contrast agents are usually inert (i.e. not metabolized) in tissue, the modeling of the distribution of contrast agent in tissue calls for only two compartments, blood (intravascular) and interstitial space (Figure 2.4). The following equation can be written, by applying the Fick principle to the interstitial space:

Ve

dCe ( t ) = K1 ⋅ C b ( t ) − k 2 ⋅ C e ( t ) dt

(8)

where Cb(t) and Ce(t) are the blood and interstitial concentrations of contrast agent (solute) respectively. Ve is the distribution volume of contrast agent in the interstitial space. K1 is the forward transfer constant from the intravascular space into the interstitial space and k2 is the backflux constant from interstitial space to intrasvascular space. As discussed previously, for contrast agent, both the forward and

Capillary endothelium

K1 Intravascular space Cb, Vb

k2

Interstitial space Ce, Ve

Figure 2.4 Two-compartment model used to describe the distribution of inert contrast agent in tumor. The symbols are defined in the text

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backward transfer constants are equal to FE. Since the intravascular space is a compartment, distinction between arterial concentration, Ca(t), and blood concentration in the space, Cb(t), is not necessary. The solution of equation (8) leads to an expression for the interstitial concentration of contrast agent: t

Ce ( t ) = k ⋅

∫ C ( u) ⋅ e

− k⋅ ( t−u )

a

du

(9)

0

where k = FE/Ve. Since a CT scanner measures tissue enhancement that has contributions from both the intravascular and the interstitial space, thus: t

Ce ( t ) = k ⋅

∫ C ( u) ⋅ e

− k⋅ ( t−u )

a

du + Ca ( t ) ⋅ Vb

(10)

0

where Q(t) is the mass of contrast agent in a unit mass of tissue. Equation (10) can also be rewritten as: Q( t ) = Ca ( t ) ⊗ [ FE ⋅ e-kt +V b ⋅ δ( t )]

(11)

where δ(t) is the delta function. By comparing equations (5a) and (11), the FIRF for the two-compartment model shown in Figure 2.4 is: F ⋅ R ( t ) = FE ⋅ e-kt + V b ⋅δ( t )

(12)

Equation (11) is the operating equation for estimation of the functional parameters: FE, Ve, and Vb with the use of the two-compartment model (Figure 2.4) to describe the distribution of X-ray contrast agents in CT tumor perfusion studies. The estimation can be achieved by a variety of non-linear regression methods.34 Potential problems in measuring Ca(t) and the issue of dual input for the liver will be dealt with in a separate section later. Some authors have proposed the following simplification to equation (11) by invoking the assumption that there is no backflux of contrast agent from the interstitial to the intravascular space,35 which is the case when the size of the distribution volume Ve is very much larger relative to the transfer constant FE. Under that assumption, FE is very much smaller compared to Ve, and thus, e−kt =1 for all t.

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Equation (12) for the blood flow scaled impulse residue function then simplifies to: F ⋅ R ( t ) = FE ⋅ H( t ) + V b ⋅δ( t )

(12a)

where H(t) is the unit step function and δ(t) is the delta function. The tumor residue function, Q(t), simplifies to: t

Q( t ) = FE ∫ Ca ( u )du + Vb⋅ Ca ( t ) 0

which can be rearranged as: t

∫0 Ca ( u )du Q( t ) = FE ⋅ + Vb Ca ( t ) Ca ( t )

(11a)

Equation (11a) is the well-known Patlak plot:30,31 if Q(t)/Ca(t) is t

plotted vs.

∫ C (u)du a

Ca (t), the result is a straight line with a slope of

0

FE and an intercept of Vb. However, for tumor imaging, it is doubtful whether the no backflux assumption will be valid in general. Another drawback of compartmental models is that F and E (PS) cannot be measured separately because they are determined together as the transfer constant (FE). This is expected because, by assuming the intravascular space to be a well-mixed compartment, all information related to the convective transport of solute along the capillaries is lost. Although FE reflects blood flow, it is confounded by the extraction efficiency. FE can be a relative measure of perfusion within a tumor only if E is uniform within the tumor. Except when E is known to be close to unity, FE is not an absolute measure of tumor perfusion.

Distributed parameter models The impetus for the use of non-compartmental models is to enable the separation of F and E (PS). Distributed parameter models do not assume that the intravascular space is a compartment with a spatially uniform solute concentration. Instead, there is a concentration gradient from the arterial to the venous end of capillaries to reflect the diffusion of solute across the capillary endothelium into the interstitial space as blood travels down the length of capillaries. The simplest distributed parameter model was first proposed by Johnson and Wilson,14 in which the

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interstitial space is still assumed to behave as a compartment (Figure 2.1). This assumption is justified because capillaries in tissue tend to be randomly oriented so that the arterial and venous ends of different capillaries are juxtaposed with respect to each other, leading to a uniform concentration of solute in the interstitial space when a large ensemble of capillaries is studied, as would be the case in CT scanning. The governing equations of the Johnson and Wilson model can be written as: ∂C b ( x,t ) FL ∂C b ( x,t ) PS + + ⎡ C ( x,t ) − C e ( t ) ⎤⎦ = 0 ∂t Vb ∂x Vb ⎣ b

(13a)

dCe ( t ) PS L = Ve ⎡C ( x,t ) − Ce ( t ) ⎤⎦ dx dt L ∫0 ⎣ b

(13b)

Equation (13a) describes the convective and diffusional transport of solute in capillaries and is the same as equation (2), while equation (13b) gives the rate of change of solute concentration in the interstitial compartment. However, even with the simplification that the interstitial space is a compartment, the solution of the Johnson and Wilson model can only be expressed in the frequency domain with the use of Laplace transformation.14 This severely limited its application, until St Lawrence and Lee discovered an adiabatic approximation to derive a closed form solution of the model in the time domain.7 The motivation for the adiabatic approximation is twofold. First, the time rate of change of Ce(t) is much slower than that of Cb(x,t), such that Ce(t) can be approximated by a staircase function consisting of discrete, finite steps, provided that the time interval of each step is small relative to the transit time of the tissue (capillaries). With this approximation, Ce(t) is constant within each step of the staircase. Second, as discussed above in the solution of equation (2), when Ce(t) is a constant, Cb(x,t) can be expressed in terms of Ce(t) by solving equation (13a). Thus, at each step of the staircase approximation of Ce(t), equation 13a is solved for Cb(x,t) in terms of Ce(t). With Cb(x,t) expressed in Ce(t), equation (13b) can be used to determine the increase in Ce(t) at the end of the step. This procedure can be repeated for each step in the staircase approximation of Ce(t), resulting in a time domain solution for the mass of solute per unit mass of tissue, Q(t), which can be expressed in the form of equation (5a) with blood flow scaled impulse residue function (FIRF), F·R(t), expressed as:7 ⎧⎪F F ⋅ R( t ) = ⎨ - k ( t-Tm ) ⋅ H( t - Tm ) ⎪⎩FE ⋅ e

0 < t ≤ Tm t > Tm

(14)

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where Tm = Vb/F according to the central volume principle, k = FE/Ve, and H(t) is a unit step function. Figure (2b) is a plot of F·R(t) or the blood flow scaled impulse residue function. It lends itself to the following interpretation: if a bolus of contrast agent is injected directly into the arterial inlet of the tumor so that Ca(t) is held at unity concentration for a very short while, the total mass of solute delivered to the tissue is numerically equal to F. The blood flow scaled impulse residue function, F·R(t), therefore reaches a height of F immediately and maintains this height for a duration equal to the mean transit time (Tm) of the tissue, Vb/F. The shaded area in Figure (2.2b) is therefore the blood volume Vb. After a time equal to Vb/F or the mean transit time (Tm), unextracted contrast agent starts to leave the tissue, F·R(t) drops to a height of FE, and thereafter contrast agent in the interstitial space backdiffuses into the intravascular space and is washed out by blood flow. This portion of F·R(t) is described by a decreasing monoexponential function with a rate constant equal to FE/Ve. With Ca(t) and Q(t) measured by CT scanning, the parameters: F, Vb, Ve, and E (PS) in F·R(t) for the Johnson and Wilson model, equation (14), can be determined by model deconvolution as discussed above.

INPUT ARTERIAL CONCENTRATION FUNCTION While the measurement of the mass of contrast agent per unit mass of tissue, Q(t), is relatively simple because of the inherent linear relationship between CT enhancement and contrast agent concentration, there are a number of additional factors requiring attention with respect to the determination of Ca(t). These factors include: (1) partial volume averaging; (2) possible dispersion of the arterial concentration function between the site of measurement and the site of input into the tissue;36 (3) delay in arrival at the input relative to the measurement site;36 and (4) the fact that some tissues can have more than one input; for example, the liver, besides the hepatic artery input, also receives blood from the portal vein, which drains venous blood from the gut.

Partial volume averaging This arises because the artery selected for the measurement of Ca(t) is smaller than the resolution limits of CT scanners (typically ~0.7 mm). Enhancement in a region of interest placed on the artery will be averaged with that in the surrounding tissue, or partial volume averaging (PVA),

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resulting in the measured arterial concentration, Cam ( t ), being lower than the true concentration, Ca(t). That is: Cam ( t ) = k pva ⋅ Ca ( t )

(15)

m where k is less than or equal to one. When Ca ( t ) instead of Ca(t) is used in equation (5a), the measured blood flow, Fm, is equal to the true blood flow divided by kpva (F/kpva). Since kpva is less than one, Fm is greater than the true F when PVA affects the measurement of Ca(t). In brain tumor studies, since all of the intracranial arteries that can be used for Ca(t) are small, PVA is expected to be significant, or kpva is less than one in most cases. To correct for the effect of PVA is equivalent to determining a value of kpva. If there exist regions in veins, for example the superior sagittal sinus, which are free of PVA, then unlike arterial curves, venous curves, Cv(t), can be measured accurately. Let h(t) be the transit time spectrum from artery to vein,36 then

C v ( t ) = Ca ( t ) ⊗ h( t ) =

1 ⋅ ⎡ Cm ( t ) ⊗ h( t ) ⎤⎦ k pva ⎣ a

The area underneath the vein curve, Av, is: ∞

A v = ∫ C v ( u )du = 0

1 ⋅ k pva

∞

∫ ⎡⎣C 0

m a

( u ) ⊗h( u )⎤⎦ du

(16)

Since h(t) is a transit time spectrum from artery to vein: ∞

∫ h( u )du = 1 0

and ∞

∞

0

0

m m m ∫ ⎡⎣ Ca ( u ) ⊗ h( u ) ⎤⎦ ⋅ du = ∫ Ca ( u ) ⋅ du = A a

m

(17)

where A a is the area underneath the measured arterial curve. From equations (16) and (17), k is determined as the ratio: k pva =

A am Av

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Dispersion of measured arterial concentration function relative to true input To minimize the effect of partial volume averaging, the artery chosen for the measurement of arterial concentration function is upstream to, and hence larger in size than, the input to the tissue. Dispersion refers to the spreading of the arterial input curve at the input to a tissue region relative to the measurement site, which is upstream to the tissue region.36 The dispersion can be characterized by the transit time spectrum, h(t), between the measurement site and the tissue region.36 Let Cam ( t ) be the measured arterial curve, and then the arterial curve at the input to the tissue region can be expressed as: Ca ( t ) = Cam ( t ) ⊗ h( t )

(18)

Substituting equation (18) into equation (5a), the operating equation for model deconvolution: Q(t) = Cam (t) ⊗ h( t ) ⊗ ⎡⎣ F ⋅ R ( t ) ⎤⎦

(19)

Equation (19) suggests that model deconvolution between the measured arterial and tissue curves gives h(t) b [F◊R(t)] instead of just the blood flow scaled impulse residue function, F◊R(t). If h(t) can be approximated by a delta function, the effect of dispersion on the estimation of the parameters: F, Vb, Ve and PS (E) in F◊R(t) is minimized since: h( t ) ⊗ ⎡⎣F ⋅ R ( t )⎤⎦ = F ⋅ R ( t ) when h( t ) ≈ δ ( t ) St Lawrence and Lee,8 using phantom experiments that reproduced the flow conditions of the human vascular system, demonstrated that the dispersion effect was negligible (i.e. h(t) is well represented by a delta function), even for a distance of 90 cm between the measurement and the input site. Further investigations in animals or patients are required to resolve this important issue.

Delay in arrival of contrast agent in tissue relative to the measured arterial concentration function Since the measurement site is upstream to the input, there will be a delay in the arrival of contrast agent at the tissue relative to the measured

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arterial concentration function. To account for the finite transit time, T0, between the measurement region and the input to the tumor: Ca ( t ) = Cam ( t − T0 ) Substituting into equation (5a): Q( t ) = Cam ( t − T0 ) ⊗ ⎡⎣ F ⋅ R ( t ) ⎤⎦ = Cam ( t ) ⊗ ⎡⎣ F ⋅ R ( t − T0 ) ⎤⎦

(20)

by manipulation of the convolution integral. Equation (20) suggests the obvious fact that the finite transit time (T0) between the measurement site for the arterial concentration and the input to the tumor is equivalent to a delay of the same magnitude in arrival in the tumor relative to the measured arterial TDC, Cam ( t ) . From this latter viewpoint, there is an additional arrival time (T0) parameter in the blood flow scaled impulse residue function for each of the models we have discussed above. Accordingly: • for the two-compartment model: − k t−T F ⋅ R ( t − T0 ) = FE ⋅ e ( 0 ) + Vb ⋅ δ ( t − T0 )

(21)

• for the two-compartment model under the assumption that there is no backflux of contrast agent from the interstitial to the intravascular space: F ⋅ R ( t − T0 ) = FE ⋅ H( t − T0 ) + V b ⋅ δ( t − T0 )

(22)

• for the Johnson and Wilson model: ⎧0 0 ≤ t ≤ T0 ⎪ F ⋅ R ( t − T0 ) = ⎨F T0 ≤ t ≤ T0 + Tm (23) ⎪ -k( t-T0-Tm ) ⋅ H( t − T0 − Tm ) t > T0 + Tm ⎩FE ⋅ e

Hepatic artery and portal vein input to the liver The liver receives its blood from both the hepatic artery delivering oxygenated blood from the heart and the portal vein draining venous blood from the gastrointestinal tract.37 Approximately two-thirds of the

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blood flow to the liver is supplied by the portal vein, and the remaining one-third is supplied by the common and proper hepatic arteries.38 The hepatic artery input, Ch(t), can be approximated by the aortic input at the level of the liver, while the portal vein input, Cp(t), can be directly measured if one of the CT slices includes the portal vein in its field of view. If α is the fraction of total blood flow to liver tissue that arises from the hepatic artery (or hepatic arterial fraction), then Ca(t) can be expressed as the weighted sum of Ch(t) and Cp(t):39 Ca ( t ) = α ⋅ Ch ( t ) + (1 − α ) ⋅ Cp ( t )

(24)

By replacing Ca(t) in equations (5a), (21), and (23) with that expressed in equation (24), the hepatic arterial fraction can be estimated together with other parameters in the two-compartment and Johnson and Wilson models.

Recirculation The effect of recirculation on the kinetics modeling proposed above can be analyzed in a straightforward manner as follows. To account for recirculation, the arterial TDC, Ca(t), can be written as a summation of the first pass component, C FP a (t), and subsequent recirculation components. For simplicity we will assume that only the first recirculation component, C aR1(t), is important, and the following derivation can easily be generalized to more than one recirculation component. If tumor perfusion is unchanged (stationary) for the duration of the first pass and subsequent recirculation phases, then equation (5a) would apply to all phases with the same blood flow scaled impulse residue function, F·R(t). The tissue residue function for the first pass phase, QFP(t), and the first recirculation phase, QR1(t), can be written as: QFP (t) = CaFP (t) ⊗⎡⎣F ⋅ R(t) ⎤⎦

(25a)

QR1(t) = CaR1 (t) ⊗⎡⎣F ⋅ R(t) ⎤⎦

(25b)

Adding equations (25a) and (25b) together, we have: QFP (t) + QR1 ( t ) = ⎡⎣Ca FP (t) + CaR1 ( t ) ⎤⎦ ⊗ ⎡⎣F ⋅ R(t) ⎤⎦

(26)

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Since, by definition: Q(t) = Q FP (t) + Q R1 ( t ) Ca (t) = CaFP (t) + CaR1 ( t ) equation (26) can be simplified into: Q(t) = Ca (t) ⊗ ⎡⎣ F ⋅ R(t) ⎤⎦ which is the same as equation (5a). Thus, provided that blood flow is stationary, the blood flow scaled IRF, F·R(t), can be calculated by model deconvolution between the measured arterial and tissue TDCs, which contain both the first pass and subsequent recirculation phases. There is no need to correct for recirculation in the measured TDCs.

PRACTICAL ISSUES In this section, a number of issues concerning the practical implementation of CT tumor perfusion are discussed.

Scanning protocol: image interval, scanning duration, and radiation dose The image interval and scanning duration used in CT tumor perfusion studies have to be optimized with respect to the model, either twocompartment or the Johnson and Wilson model, employed in the description of the blood flow scaled impulse residue function (FIRF), that is, equation (21), (22), or (23). For the two-compartment model, equation (21) shows that the FIRF is a slowly decaying exponential with a half-life measured in minutes. Hence, the image interval required can be as long as 3–15s and the duration of scanning required a few minutes, provided that it is at least 3–5 times the half-life of the FIRF. Since current CT scanners can acquire an image in less than a second, this protocol requires intermittent scanning, which reduces the radiation dose to the patient, or the protocol can be modified to increase the anatomical coverage of a study.40 The disadvantage is, as discussed above, that F and E (PS) cannot be measured separately because they are determined together as the transfer constant (FE).

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For the Johnson and Wilson model, the blood flow scaled impulse residue function has two distinct phases. The first or vascular phase, as shown in Figure 2.2, is relatively brief, being equal to the mean transit time through the tumor vasculature, which is in seconds rather than minutes. The second or interstitial phase is a slow exponential decay with a half-life which is usually measured in minutes. A two-phase scanning protocol is required for the Johnson and Wilson model. The first phase of scanning has an image interval of 0.5–1 s and a duration of 30–45 s to allow reliable determination of the vascular phase of the FIRF. The short image interval in the first phase requires the X-ray to be on all the time, to scan continuously, and as such constitutes the bulk of the radiation dose to the patient. The brevity of the duration of the first phase means that breath-holding required for studies affected by breathing motion during this phase of scanning is possible. The second phase of scanning has an image interval of 10–20 s and duration of several minutes to characterize the rate constant, k, in equation (23). At this infrequent image interval, X-rays are turned on intermittently to acquire a scan. Stewart et al. have proposed an intermittent scanning method for this second phase that allows the correction of breathing motion without respiratory gating. For reasons of space, interested readers are referred to their original publication.39 The following is a typical two-phase scanning protocol, as an example: • first phase: image interval 1 s and 30 s duration • a delay of 15 s between first and second phases • second phase: image interval 15 s and 2 min duration. At each CT slice location, the first phase acquires 30 images while the second phase acquires only eight images. Thus, the first phase contributes close to 80% of the total radiation dose if the same X-ray tube current (mA) is used in both phases. We can also compare the radiation dose required by the two-compartment and Johnson and Wilson models. For the same duration of 2.5 min and at 15s image interval, the two-compartment model protocol acquires 10 images per slice location while the Johnson and Wilson protocol acquires 38. The radiation dose for the Johnson and Wilson model scanning protocol is 3.8 times higher than that of the twocompartment model, again assuming that the same X-ray tube current is used to acquire each image. On the other hand, the advantage of the Johnson and Wilson model is that absolute tumor perfusion can be measured.

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Calibration of CT scanner In CT tumor perfusion studies, there is an implicit assumption that enhancement measured by a CT scanner in HU in artery and tumor is linearly proportional to their contrast concentration, Ca(t) and Q(t) in equation (5a). As long as the proportional relationship is the same for both artery and tumor, calibration of enhancement in Hounsfield units (HU) versus contrast concentration is not required. This is true for CT scanners when beam hardening is negligible. For brain tumor perfusion studies, given the relatively low enhancement in arteries and veins in the brain at the dosage of contrast injected,41 this condition is usually satisfied. For other tissues, particularly in the abdominal or thoracic region, the dosage of contrast injected may have to be limited to reduce the beam hardening effect arising from the superior vena cava or the aorta, if the injection site is any one of the upper-extremity veins.

Contrast, noise, and radiation dose The quality of the arterial and tissue TDCs, used to derive perfusion and related parameters with either the maximum slope method or model deconvolution method, depends on their signal-to-noise ratio. The signal is the arterial and tissue contrast enhancement detected by a CT scanner. Since all CT contrast agents are iodinated compounds, the main process contributing to contrast enhancement is the absorption of X-ray photons at the energy of the K-edge of iodine, or 33.2keV. A low kVp X-ray beam would increase the signal (enhancement) but at the expense of noise. As demonstrated by Lee et al. with a brain-size phantom complete with a bone ring to simulate the skull, increasing kVp from 80 to 120, but keeping the mAs used to acquire a CT image the same, increases the signal-to-noise ratio by 11%, but increases the radiation dose to the subject by over 300%.42 Thus, when signal-to-noise performance is normalized with respect to radiation dose imparted, 80kVp is better than 120kVp for CT perfusion studies. Essentially the same conclusion was reached in studies by Wintermark et al.43 and Hirata et al.,44 in which patients were scanned at both 80 and 120kVp, and signal-to-noise ratios as well as radiation doses were compared. The same conclusion was arrived at by Huda et al. using a body-sized phantom: when mAs was kept the same but kVp increased from 80 to 120kVp, the signal-to-noise ratio increased by less than 1.2-fold while the radiation dose increased by over 3-fold.45 Thus, 80 kVp is the optimal kVp to be used in CT tumor perfusion studies, regardless of whether the tumor is located in the brain or abdominal organs.

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VALIDATION OF COMPUTED TOMOGRAPHY TUMOR BLOOD FLOW AND RELATED MEASUREMENTS IN ANIMAL TUMOR MODELS The accuracy and precision of CT tumor blood flow and related measurements has been investigated in animal models of brain tumor, soft tissue tumor, and liver tumor, using methods that do not require modeling, as well as kinetics modeling methods. These preclinical studies have the advantage that the accuracy of CT measurements can be validated against reference gold-standard microsphere measurements, which require tissue sampling and hence sacrifice of the animal. In this section, the results from these validation studies are discussed.

Methods not requiring tracer kinetics modeling Pollard et al.46 compared CT tumor perfusion measurements obtained by application of the Fick principle with the no outflow assumption (equation (7a)) against those measured with the gold-standard (fluorescent) microsphere method47 in male Fischer rats implanted with R3230AC rat-derived mammary adenocarcinoma tumor in both caudal thigh regions. The regression equation between the two types of perfusion measurements was: Microsphere perfusion = 2.06 × CT perfusion– 0.221 (r2 = 0.68, p 0.10). The variability in CT blood flow and blood volume measurements in the repeated studies was 13% and 7%, respectively.

VX2 skeletal muscle (thigh) tumor VX2 tumor cells were implanted in the left thigh of nine New Zealand White rabbits and left to grow to 0.4 cm in diameter. Blood flow (BF), blood volume (BV), mean transit time (MTT) and PS in the implanted tumor were measured using the Johnson and Wilson model.49 The ex vivo method of radioactive microspheres was used to validate the CT blood flow measurements. The precision of the CT technique for the measurement of BF, PS, BV, and MTT was determined by repeating the scan three times in four of the nine rabbits under steadystate conditions. There was a significant linear correlation (r = 0.96) between CT- and microsphere-measured blood flow values, with a slope not significantly different from unity (0.98 ± 0.02, p < 0.0001). The precision of blood flow, capillary permeability surface area product,

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blood volume, and mean transit time was 14%, 18%, 20% and 24%, respectively.

VX2 liver tumor VX2 carcinoma cells were implanted into the livers of eight male New Zealand white rabbits. Each CT tumor perfusion study started with a 30-s cine scan, with breath-hold at the injection of 5ml contrast, followed by 4-s cine scans without breath-hold every 10s for 2min. These latter images were registered with the breath-hold images to eliminate breathing motion.39 Total liver blood flow and hepatic arterial fraction were calculated according to equations (5a), (23), and (24). Hepatic artery blood flow (HABF) was further calculated as the product of total liver blood flow and hepatic arterial fraction. The HABF measured by CT in normal tissue and tumor rim and core were compared with those measured by the ex vivo microsphere technique under normocapnia, hypercapnia, and hypocapnia conditions. The ex vivo microsphere HABF measurements under normal conditions were: 47 ± 24, 35 ± 12, and 95±21 ml min/100g in normal tissue, tumor core, and rim, respectively. In comparison, CT perfusion HABF measurements were: 54±13, 35±14, and 122±25 ml min/100g in the same regions, respectively. No significant differences were found between HABF measurements obtained with the two techniques (p>0.05). Furthermore, there was a strong correlation when HABF values from the two techniques were compared: slope of 1.12, intercept of 6.94, and r2 =0.76. The Bland–Altman plot50 comparing CT perfusion and microsphere measurements gave a mean difference of −13.34mlmin−1 (100g)−1. The limits of agreement, the interval in which 95% of the differences lay, were –28.43 and 55.11 ml min/100g. The reproducibility of CT perfusion measurements in liver tumor was investigated in another five male New Zealand White rabbits (3.1–3.4kg) implanted with VX2 liver tumors. Each rabbit was scanned twice, 15min apart, with the same protocol as in the validation studies discussed above. Total hepatic blood flow (HBF), hepatic blood volume (HBV), hepatic arterial fraction (HAF), contrast arrival time (T0), and permeability surface area product (PS) were determined as described above. Data from the tumor core, tumor rim, and adjacent normal tissue were analyzed using a repeated measures analysis of variance (ANOVA).51 The reproducibility was assessed by the coefficient of variation (CV). In all cases, the ANOVA showed no significant difference between perfusion parameter measurements made in the two consecutive CT perfusion scans. Table 2.1 shows the CV of the five parameters assessed.

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Table 2.1

Reproducibility of computed tomography measurements Coefficient of variation (%)

Tumor rim Tumor core Normal tissue

T0

HBF

HBV

PS

HAF

17.6 17.9 8.2

12.7 10.2 8.8

16.5 9.6 7.6

20.7 16.4 65.4

12.9 7.2 16.0

T0, contrast arrival time; HBF, hepatic blood flow; HBV, hepatic blood volume; PS, permeability surface area product; HAF, hepatic arterial fraction

VALIDATION OF CT TUMOR BLOOD FLOW AND RELATED MEASUREMENTS IN CLINICAL STUDIES Reproducibility studies Goh et al.52 investigated the reproducibility of measurements of blood flow, blood volume, mean transit time, and permeability surface area product obtained with the Johnson–Wilson model (equations (5a) and (23)) in 10 patients with colorectal cancer. Each patient underwent two single-phase 65-s perfusion studies separated by 48 hours to allow assessment of reproducibility of the CT measurements listed above. The mean difference (95% limits of agreement) for blood volume, blood flow, mean transit time, and permeability when a 5-mm slice thickness was used were 0.04 (−2.50 to +2.43)ml/100g +8.80 (−50.5 to +68.0)ml/min/100 g; –0.99 (−8.19 to +6.20)s; and +1.20 (−5.42 to +7.83)ml/min/100 g, respectively. Similar reproducibility results were obtained for a 20-mm slice thickness. Ng et al.53 investigated the reproducibility of measurements of blood volume and permeability surface area product obtained using a twocompartment model (Figure 2.4) under the assumption that there is no backflux of contrast agent from the interstitial to the intravascular space, that is, equations (5a) and (22). As discussed before, the FE estimated from equations (5a) and (22) can be equated to the PS of the tumor only when PS is much smaller than blood flow (F). In their study, blood flow was not estimated, and thus it is not certain whether the estimated FE is equal to PS. Nevertheless, the mean difference (95% limits of agreement) for tumor FE was 1.4 (−4.0 to 6.8)ml/min/100 ml for 10-mm slice thickness and 0.8 (−3.6 to 5.2)ml/min/100 ml for 40-mm slice thickness. The mean difference (95% limits of agreement) for blood volume was 1.9 (−5.1 to 8.9)ml/100ml for 10-mm slice thickness and

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1.4 (−3.7 to 6.6)ml/100 ml for 40-mm slice thickness. The coefficient of variation for permeability was 18.7% for 10-mm slice thickness, improving to 11.9% for 40-mm slice thickness. The coefficient of variation for blood volume was 41.7% for 10-mm slice thickness, improving to 32.6% for 40-mm slice thickness.

Accuracy studies Hattori et al.54 compared CT tumor perfusion measurement obtained by application of the Fick principle with the no outflow assumption (equation (7a)) against that measured with positron emission tomography using the [15O-]carbon dioxide (C15O2) steady-state method in 16 patients with superficial tumors including the sites of larynx, hypopharynx, tongue, lung, and breast. The CT blood flow measurement on average was about 20% higher than the PET measurement, and the correlation coefficient between the two sets of measurements was high (r = 0.79). These results suggest that CT tumor blood flow measurement in patients using the no venous outflow assumption is accurate, albeit with a systemic overestimation of about 20%.

CONCLUSION A CT tumor perfusion study can be performed using very simple procedures, making it particularly suited for incorporation into the routine diagnostic imaging protocol of cancer patients. The kinetics modeling discussed in this chapter permits simultaneous quantitative determination of a number of functional parameters reflecting the activity of angiogenesis in tumors: blood flow, blood volume, mean transit time, capillary permeability surface area product, and arrival time of contrast agent, from a single study. For the special case of the liver, an additional parameter determined is hepatic arterial fraction of the liver blood flow. Furthermore, measurements of these parameters have been shown to be accurate and reproducible. With the high spatial resolution of CT scanning, each of these functional parameters can be determined and displayed as functional maps in voxels as small as 10mm3 (1.5mm× 1.5 mm×5mm). This would allow visualization of the inherently very heterogeneous angiogenic activities seen in tumors. The utility of imaging angiogenesis has been demonstrated in a number of experimental studies, and further clinical studies will prove whether the favorable results obtained in animal models can be reproduced in human subjects.

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REFERENCES 1. Axel L. Cerebral blood flow determination by rapid-sequence computed tomography; theoretical analysis. Radiology 1980; 137: 679–86. 2. Axel L. Tissue mean transit time from dynamic computed tomography by a simple deconvolution technique. Invest Radiol 1983; 18: 94–9. 3. Miles KA. Measurement of tissue perfusion by dynamic computed tomography. Br J Radiol 1991; 64: 409–12. 4. Blomley MJK, Coulden R, Bufkin CRT et al. Contrast bolus dynamic computed tomography for the measurement of solid organ perfusion. Invest Radiol 1993; 28 (Suppl 5): S72–7. 5. Blomley MJK, Dawson P. Bolus dynamics: theoretical and experimental aspects. Br J Radiol 1997; 70: 351–9. 6. Dawson P, Blomley MJK. Contrast agents as extra-cellular fluid space markers: adaptation of the central volume theorem. Br J Radiol 1996; 69: 717–22. 7. St Lawrence K, Lee T-Y. An adiabatic approximation to the tissue homogeneity model for water exchange in the brain I. Theoretical derivation. J Cereb Blood Flow Metab 1998; 18: 1365–77. 8. St Lawrence K, Lee T-Y. An adiabatic approximation to the tissue homogeneity model for water exchange in the brain II. Experimental validation. J Cereb Blood Flow Metab 1998; 18: 1378–85. 9. Cenic A, Nabavi DG, Craen RA, Gelb AW, Lee T-Y. Dynamic CT measurement of cerebral blood flow: a validation study. AJNR Am J Neuroradiol 1999; 20: 63–73. 10. Cenic A, Nabavi DG, Craen RA, Gelb AW, Lee T-Y. A CT method to measure hemodynamics in brain tumors: validation and application to cerebral blood flow maps. AJNR Am J Neuroradiol 2000; 21: 462–70. 11. Schumann P, Touzani O, Young AR et al. Evaluation of the ratio of cerebral blood flow to cerebral blood volume as an index of local cerebral perfusion pressure. Brain 1998; 121: 1369–79. 12. Rapoport SI. Blood-Brain Barrier in Physiology and Medicine. New York: Raven Press, 1976. 13. Crone C. The permeability of capillaries in various organs as determined by use of the ‘indicator diffusion’ method. Acta Physiol Scand 1963; 58: 292–305. 14. Johnson JA, Wilson TA. A model for capillary exchange. Am J Physiol 1966; 210: 1299–303. 15. Sawada Y, Patlak CS, Blasberg RG. Kinetic analysis of cerebrovascular transport based on indicator diffusion technique. Am J Physiol 1989; 256: H794–812. 16. Renkin EM. Transport of potassium-42 from blood to tissue in isolated mammalian skeletal muscle. Am J Physiol 1959; 197: 1205–10 17. Meier P, Zierler KL. On the theory of the indicator dilution method for measurement of blood flow and volume. J Appl Physiol 1954; 6: 731–44. 18. Robert GW, Larson KB, Spaeth EE. The interpretation of mean transit time measurements for multiphase tissue systems. J Theor Biol 1973; 39: 447–75. 19. Brooks DJ, Thomas DG, Marshall J, Jones T. Studies on regional cerebral pH in patients with cerebral tumours using continuous inhalation of 11CO2 and positron emission tomography. J Cereb Blood Flow Metab 1986; 6: 529–35. 20. Brudin LH, Valind SO, Rhodes CG, Turton DR, Hughes JM. Regional lung hematocrit in humans using positron emission tomography. J Appl Physiol 1986; 60:1155–63.

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21. Okazawa H, Yonekura Y, Fujibayashi Y et al. Measurement of regional cerebral plasma pool and hematocrit with copper-62-labeled HSA-DTS. J Nucl Med 1996; 37: 1080–5. 22. Sakai F, Igarashi H, Suzuki S, Tazaki Y. Cerebral blood flow and cerebral hematocrit in patients with cerebral ischemia measured by single-photon emission computed tomography. Acta Neurol Scand 198; 127 (Suppl): 9–13. 23. Lee TY, Ellis RJ, Dunscombe PB et al. Quantitative computed tomography of the brain with xenon enhancement: a phantom study with the GE9800 scanner. Phys Med Biol 1990; 35: 925–35. 24. Bassingthwaighte JB, Chinard FP, Crone C, Lassen NA, Perl W. Definitions and terminology for indicator dilution methods. In: Crone C, Lassen NA, eds. Capillary Permeability. Copenhagen: Munskgaard 1970: 665–9. 25. Peters AM, Gunasekera RD, Henderson BL et. al. Noninvasive measurement of blood flow and extraction fraction. Nucl Med Commun 1987; 8: 823–37. 26. Miles KA. Measurement of tissue perfusion by dynamic computed tomography. Br J Radiol 1991; 64: 409–12. 27. Miles KA, Charnsangavej C, Lee FT et al. Application of CT in the investigation of angiogenesis in oncology. Acad Radiol 2000; 7: 840–50. 28. Klotz E, Konig M. Perfusion measurements of the brain: using dynamic CT for the quantitative assessment of cerebral ischemia in acute stroke. Eur J Radiol 1999; 30: 170–84. 29. Kety SS, Schmidt CT. The determination of cerebral blood flow in man by the use of nitrous oxide in low concentrations. Am J Physiol 1945; 143: 53–61. 30. Patlak CS, Blasberg RG, Fenstermacher JD. Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. J Cereb Blood Metab 1983; 3: 1–7. 31. Patlak CS, Blasberg RG. Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. Generalizations. J Cereb Blood Metab 1985; 5: 584–90. 32. Groothuis DR, Lapin GD, Vriesendorp FJ, Mikhael MA, Patlak CS. A method to quantitatively measure transcapillary transport of iodinated compounds in canine brain tumors with computed tomography. J Cereb Blood Flow Metab 1991; 11: 939–48. 33. Groothuis DR, Vriesendorp FJ, Kupfer B et al. Quantitative measurements of capillary transport in human brain tumors by computed tomography. Ann Neurol 1991; 30: 581–8. 34. Gill PE, Murray W, Wright MH. Practical Optimization. London: Academic Press, 1981. 35. Miles KA. Tumour angiogenesis and its relation to contrast enhancement on computerised tomography: a review: Eur J Radiol 1999; 30: 198–205. 36. Calamante F, Gadian DG, Connelly A. Delay and dispersion effects in dynamic susceptibility contrast MRI: simulations using singular value decomposition. Magn Reson Med 2000; 44: 466–73. 37. Richardson PD, Withrington PG. Liver blood flow. I. Intrinsic and nervous control of liver blood flow. Gastroenterology 1981; 81: 159–73. 38. Schenk W Jr, McDonald J, McDonald K, Drapanas T. Direct measurement of hepatic blood flow in surgical patients: with related observations on hepatic flow dynamics in experimental animals. Ann Surg 1962; 156: 463–71.

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39. Stewart EE, Chen X, Hadway J, Lee T-Y. Correlation between hepatic tumor blood flow and glucose utilization in a rabbit liver tumor model. Radiology 2006; 239: 740–50. 40. Roberts H, Roberts T, Lee T-Y, Dillon W. Dynamic, contrast-enhanced CT of human brain tumors: quantitative assessment of blood volume, blood flow, and microvascular permeability. Report of Two Cases. AJNR Am J Neuroradiol 2002; 23: 828–32. 41. Nabavi DG, LeBlanc LM, Baxter B et al. Monitoring cerebral perfusion flow after subarachnoid hemorrhage using CT. Neuroradiology 2001; 43: 7–16. 42. Lee TY, Ellis RJ, Dunscombe PB et al. Quantitative computed tomography of the brain with xenon enhancement: a phantom study with the GE9800 scanner. Phys Med Biol 1990; 35: 925–35. 43. Wintermark M, Maeder P, Verdun FR et al. Using 80 kVp versus 120 kVp in perfusion CT measurement of regional cerebral blood flow. AJNR Am J Neuroradiol 2000; 21: 1881–4. 44. Hirata M, Sugawara Y, Fukutomi Y et al. Measurement of radiation dose in cerebral CT perfusion study. Radiat Med 2005; 23: 97–103. 45. Huda W, Scalzetti EM, Levin G. Technique factors and image quality as functions of patient weight at abdominal CT. Radiology 2000; 217: 430–5. 46. Pollard RE, Garcia TC, Stieger SM et al. Quantitative evaluation of perfusion and permeability of peripheral tumours using contrast-enhanced computed tomography. Invest Radiol 2004; 39: 340–9. 47. Heymann MA, Payne BD, Hoffman JI, and Rudolph AM. Blood flow measurements with radionuclide-labeled particles. Prog Cardiovasc Dis 1977; 20: 55–79. 48. Lykke AW, Cummings R. Increased vascular permeability in the primary cutaneous allograft response in the rat. Experientia 1969; 25: 1287–8. 49. Purdie TG, Henderson E, Lee TY. Functional CT imaging of angiogenesis in rabbit VX2 soft tissue. Phys Med Biol 2001; 46: 3161–75. 50. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986; 1: 307–10. 51. Eliasziw M, Young SL, Woodbury MG, Fryday-Field K. Statistical methodology for the concurrent assessment of interrater and intrarater reliability: using goniometric measurements as an example. Phys Ther 1994; 74: 777–88. 52. Goh V, Halligan S, Gartner L, Bassett P, Bartram CI. Quantitative colorectal cancer perfusion measurement by multidetector-row CT: does greater tumour coverage improve measurement reproducibility? Br J Radiol 2006; 79: 578–83. 53. Ng QS, Goh V, Klotz E et al. Quantitative assessment of lung cancer perfusion using MDCT: does measurement reproducibility improve with greater tumor volume coverage? Am J Roentgenol 2006; 187: 1079–84. 54. Hattori H, Miyoshi T, Okada J et al. Tumor blood flow measured using dynamic computed tomography. Invest Radiol 1994; 29: 873–6.

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3 Image acquisition and contrast enhancement protocols for CT perfusion Kenneth A Miles

INTRODUCTION AND OVERVIEW: Most people working in computed tomography (CT) departments are familiar with image acquisition and contrast enhancement protocols that aim to delineate the blood vessels supplying a particular organ, for example CT angiography for the renal or pulmonary arteries. On the other hand, experience using CT to depict perfusion or other aspects of vascular physiology within those organs may be less widespread. Nevertheless, some awareness of vascular physiology is required for the optimal performance of CT angiography in that the timing of the image acquisition must be matched to the temporal changes in vascular enhancement as the contrast medium passes through the circulation. Many modern CT systems now incorporate an automated method for timing the image acquisition in accordance with vascular enhancement. However, prior to this development, it was commonplace to acquire a rapid sequence of images without table movement during a small initial test injection of contrast medium. This sequence of images was used to identify the time of optimal vascular enhancement and so guide the timing of the main CT angiography study. Conceptually, image acquisition and contrast protocols for CT perfusion in oncology are similar to those initial timing sequences, with the temporal changes in contrast enhancement providing the information necessary for determining tumor perfusion. However, the focus is not simply upon contrast enhancement within the major vessels but, more importantly, enhancement within the small vessels inside the tumor itself. In effect, CT perfusion can be considered ‘CT angiography for the tumor microcirculation’. Just as the timing sequences described above were once performed in conjunction with CT angiography, CT perfusion data acquisitions can

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be combined with other CT acquisitions for tumor evaluation within a single examination. Due to the simplicity and speed of acquisition, the inclusion of CT perfusion adds little time to the examination. For example, a conventional portal phase post-contrast-enhanced series of the abdomen and pelvis can be acquired immediately after a CT perfusion acquisition and, in some circumstances, using the same bolus of contrast medium. CT perfusion can also be performed during selective hepatic arteriography or superior mesenteric arterial portography to obtain pure measurements of hepatic arterial and portal perfusion respectively.1,2 The advent of imaging systems that combine CT with positron emission tomography (PET) also creates opportunities to perform CT perfusion as an adjunct to PET imaging of tumor metabolism or receptor activity, etc., without need of the on-site cyclotron required to obtain perfusion data using PET (Chapter 14). Figure 3.1 provides an overview of acquisition and contrast-enhancement protocols for CT perfusion. Rather than acquire a series of images at different anatomical locations across an organ, CT perfusion typically requires a series of images over time, performed with no movement of the examination table through the gantry. These ‘raw’ images undergo computer processing to generate a range of parametric maps of tumor

CT perfusion Time

Perfusion (m1/min/ml)

Blood volume (%)

Permeability (µ1/min/ml)

Figure 3.1 Perfusion computed tomography (CT) requires a series of images performed at a single anatomical location following administration of contrast medium. The subsequent image processing is analogous to that performed using CT angiography data

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perfusion, blood volume, etc. This processing stage is directly analogous to deriving maximal intensity projections from CT angiograms, for example. All CT perfusion protocols require an initial baseline image with no contrast material. The precise protocol adopted thereafter depends primarily upon the physiological information required for a particular clinical scenario and the image processing method used.3 Manufacturers usually offer recommended protocols to be used in conjunction with their analysis software. This chapter aims to inform the reader as to the basis behind the design of image acquisition and contrast enhancement protocols for CT perfusion. The acquisition and contrast-enhancement parameters to be considered are (1) the phase of respiration, (2) the overall length of time of the image series, (3) the number and frequency of images, (4) the number and thickness of CT slices, (5) the X-ray exposure factors, and (6) the type, volume, and concentration of contrast medium injected. Some example protocols are given in Table 3.1. The chosen protocol will determine the radiation dose received by the patient.

Table 3.1 Example image acquisition and contrast-enhancement protocols for perfusion computed tomography

Image acquisition Overall time Number of images Image frequency No. slices ¥thickness Tube voltage Tube current Contrast medium Concentration Volume Injection rate Processing Parameters calculated

Analysis method

Protocol 1

Protocol 2

Protocol 3

60 s 60 ¥4 Every 1 s Single location: 4 ¥ 5 mm 120 kVp 50 -100 mAs

50 s 25 ¥ 2 Every 2 s Single location: 2 ¥10 mm 80 kVp 100 -200 mAs

90 s 9¥ 6 Every 10s Multiple spiral: 6¥10 mm 80 kVp 100 –200 mAs

370 mg/ml 50 ml 4–7 ml/s

370 mg/ml 40 ml 7–10 ml/s

300 mg/ml 100 ml Decreasing rate: 4ml/s–2ml/s–1ml/s

Perfusion, Perfusion, Standardized blood volume, blood volume, perfusion value, mean transit mean transit time permeability, time, permeability blood volume Deconvolution Single Two compartment compartment (Patlak)

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Frequently, the desire for image data of higher quality must be balanced against this radiation dose. Although, in the context of oncology, the radiation exposure associated with CT perfusion is small compared to the radiotherapy dose that many patients will receive, there remains a need to limit the radiation burden associated with CT perfusion studies. A typical radiation dose received from a CT perfusion study of a limited volume (e.g. 4¥5 mm slices) is between 2 and 10mSv, depending on the body region, and is therefore comparable to that obtained from a gamma camera study of tumor perfusion using single photon emission tomography, for instance. As multidetector CT systems are developed with larger detector tracks, offering the possibility of singlelocation acquisitions over larger tumor volumes, the radiation dose associated with CT perfusion could potentially increase by a considerable amount. It should also be remembered that the aim of CT perfusion is to produce physiological data rather than display anatomical structure, and, thus, spatial resolution, although important, is not the primary consideration.

IMAGE ACQUISITION Phase of respiration Respiratory motion can result in significant misregistration of images acquired during the dynamic image sequence for CT perfusion in the chest and abdomen. Misregistration leads in turn to errors in perfusion values and artifacts on parametric images. CT perfusion protocols for chest and abdomen are usually acquired with either suspended respiration or quiet breathing. Suspended respiration is unlikely to be appropriate for image series longer than 45–60 seconds, unless acquisition pauses are created to allow the patient to take a breath, as might be the case for multiple spiral protocols. Many patients who have suspended their breathing will slowly exhale during the acquisition, resulting in motion artifacts. Protocols that adopt quiet respiration can result in high-quality perfusion images, but the patient must be warned to avoid the temptation to take a deep breath when experiencing the ‘hot flush’ commonly associated with a rapid bolus of contrast medium. Quiet respiration will also be appropriate when co-registering CT perfusion data with PET images, acquired using an integrated PET–CT system, as suspended respiration is not possible for the PET images, which take several minutes. Respiratory gating of CT images has the potential to reduce CT perfusion motion artifacts in the future.

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Overall length of time of the image series The overall length of the image series governs which physiological parameters can be measured. Tumor blood flow, blood volume, and mean transit time (MTT) can be assessed during the first pass of contrast material through the vascular system, at which time the contrast material is predominantly intravascular. The length of the first pass depends upon an individual’s cardiac output and circulating blood volume, but typically comprises the first 45–60 seconds immediately after intravenous injection (e.g. Table 3.1, protocols 1 and 2). A longer series is required for the assessment of tumor vascular permeability (e.g. Table 3.1, protocol 3). This additional time is needed to allow sufficient contrast material to pass into the extravascular space for reliable measurements. Using the deconvolution technique to assess colorectal cancer, Goh et al. showed that acquisition times of 45 s resulted in significantly lower values for vascular permeability than acquisitions of 65 s or more, whereas measurements of blood flow, blood volume, and MTT were consistent across all acquisition times between 45 s and 130 s.4

The number and frequency of images The number and frequency of images obtained will be a balance between the quality of the perfusion data and the radiation dose for the patient. A high image frequency will be required for reliable measurements from first pass data. Although modern CT systems can acquire images with a frequency of faster than one image every second, studies of cerebral perfusion have shown that image frequency can be reduced to every 2–3 seconds without adversely affecting the perfusion values obtained (e.g. Table 3.1, protocol 2).5 To assess vascular permeability, images can be acquired less frequently (every 5–10 s), as changes in contrast enhancement become less rapid (e.g. Table 3.1, protocol 3).

The number and thickness of CT slices When focusing on the first pass of contrast medium, the volume of tumor included in a CT perfusion study is usually constrained in the craniocaudal (z axis) direction by the width of the CT detector, typically 2–4cm for current multidetector (MDCT) systems. This limitation is due to the need to acquire images at a frequency close to the time taken for the CT system to rotate the X-ray tube around the patient (typically 0.5–1 s) which does not allow sufficient time for tabletop movement during the study. The detector can be divided so that a number of slices are obtained simultaneously, e.g. four slices 5 mm thick. Although slices

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as thin as 0.5 mm can be obtained, reducing slice thickness increases image noise, which can only be compensated effectively by increasing the radiation exposure. Moving the imaging table through the gantry during acquisition allows greater volumes of tissue to be examined. A table-toggling approach, in which table movement oscillates during a continuous acquisition, obviates the delay that would otherwise be required to return the table to its starting position.6 The greater is the extent of table movement, the lower is the frequency of image acquisition obtainable at a particular slice location. Using a 1-second tube rotation, tabletoggling a distance that is twice the width of the detector tract (i.e. approximately 4–8 cm on current MDCT systems) will keep the cycle time for data acquisition at each slice location to within 2 seconds, thereby allowing reliable perfusion measurements. A further increase in the tumor volume assessed can be achieved by using repeated spiral acquisitions, but the greater coverage is off-set by the necessary increase in cycle time. Such protocols are suitable for the assessment of tumor vascular permeability and blood volume (e.g. Table 3.1, protocol 3). Repeated spiral acquisitions are also frequently used for measurements of peak tumor enhancement, for instance in the characterization of pulmonary nodules7 (see Chapter 7). With suitable calibration of the CT system, such measurements of peak enhancement can also be converted to the standardized perfusion value (SPV).8 Due to the reduced image frequency, there is potential for such protocols to miss the time of peak tumor enhancement. However, this error may be small for tumors exhibiting increased vascular permeability, because the rapid passage of contrast material into the extravascular space will reduce the washout of contrast material following the first pass. A technique that uses a single spiral (helical) volume acquisition close to the time of peak cerebral enhancement during the first pass of contrast medium has been proposed for the measurement of cerebral volume over the whole brain.9 This method could potentially be applied to tumor imaging in other body regions, to increase the volume of tumor assessed without a major increase in radiation burden. Correct timing could be achieved by using a single-location timing sequence following a small test-bolus of contrast medium to determine the time of peak tissue enhancement (analogous to the timing sequences often used during CT angiography to determine the time of peak vascular enhancement). Data from this timing sequence could also be processed to obtain absolute perfusion data with the chosen slice level (Figure 3.2). Although such a protocol would allow measurements of tumor blood

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1. Low mA pre-contrast spiral acquisition

2. Single-location time sequence (low mA, small volume of contrast)

3. Spiral acquisition at time of peak tumor enhancement: superimpose peak enhancement image (color) on pre-contrast CT

Figure 3.2 CT perfusion technique that combines single-level measurements of perfusion, blood volume, and transit time with a volumetric assessment of tumor blood volume and standardized perfusion value

volume and SPV, assessment of MTT and vascular permeability would not be possible with this technique. Greater anatomical coverage can allow compensation for respiratory misregistration by retrospective selection of sets of images from the whole volume, in which the tumor is displayed at the same craniocaudal location. This technique has been successfully applied to CT perfusion of the liver.10 Portal perfusion measurements without respiratory compensation were significantly higher than when compensation was applied. An increase in the extent of tumor assessed by CT perfusion also has the potential to reduce measurement errors resulting from any heterogeneity of the tumor vasculature. However, the level of benefit may be dependent upon tumor type and analysis method. Using deconvolution analysis in colorectal cancer, Goh et al. found no improvement in measurement reproducibility when increasing z-axis coverage from 5 mm to 20 mm,11 whereas for Patlak analysis of lung tumors, Ng et al. found that reproducibility was improved by increasing z-axis coverage from 10 mm to 40 mm.12

X-ray exposure factors Tube voltage (kVp), tube current (mA), and exposure time are important aspects of the acquisition protocol. Higher values for each of these

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parameters reduce image noise but increase radiation dose for the patient. Typical exposure times for current CT scanners are 1 second or less. Selecting a smoother reconstruction filter (e.g. soft-tissue) will reduce image noise, but will also reduce spatial resolution. The tube voltage also governs the amount of attenuation produced by a given iodine concentration of contrast medium (Figure 3.3). A greater increase in attenuation is produced at tube voltages lower than that typically used for diagnostic studies (e.g. 80–100kVp instead of 120–140 kVp). However, a lower tube voltage heightens image noise and beam hardening effects. The precise relationship between attenuation change and iodine concentration varies between CT systems, with aging of the X-ray tube, and for different slice positions across the track of a multidetector CT system.13 The likely explanation for these variations in iodine response is differences in the X-ray spectra generated by a particular X-ray tube. The X-ray spectrum will be modified by beam hardening within the anode, which will not only change as the anode wears over time but also vary across the X-ray beam (anode heal effect). Because algorithms for calculating perfusion from CT typically divide tissue enhancement measures by vascular enhancement, the effects of variable iodine response will cancel out for a given perfusion measurement, provided that the tissue and vascular enhancement data are obtained from the same portion of the detector track. The different analysis methods used in processing CT perfusion data (see Chapter 2) are more or less sensitive to image noise, and thus the X-ray exposure should be matched to the processing algorithm.

Attenuation (HU)

800

600 400 80 kVp 100 kVp 120 kVp 140 kVp

200 0 0

5 10 15 Iodine concentration (mg/ml)

20

Figure 3.3 Attenuation values associated with different concentrations of contrast medium measured in a phantom using tube voltages between 80 and 140 kVp.

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Deconvolution analysis uses all the images within an acquisition sequence, and is therefore affected less by noise in individual images. Hence, a lower tube current can be selected, allowing a higher image frequency (e.g. protocol 1, Table 3.1). However, single-compartment analysis depends upon accurate attenuation measurements within critical images in the sequence, and too much image noise can result in overestimation of tissue enhancement rates with miscalculation of perfusion values. Thus, time–attenuation data from protocols that adopt a higher tube current (with a lower image frequency to minimize the radiation dose) are appropriate for compartmental analysis methods (e.g. protocols 2 and 3, Table 3.1). Such data sets can also be successfully processed using deconvolution analysis (Griffiths, personal communication, 2001).

CONTRAST ENHANCEMENT Type of contrast medium Non-ionic contrast medium is preferred to an ionic preparation due to the lower effects of flushing and nausea. Such side-effects increase the chance of the patient moving during the image sequence, resulting in errors due to misregistration between sequential images. The performances of the non-ionic contrast media for CT perfusion are broadly similar, although measurements of vascular permeability may vary slightly for different contrast media depending on the molecular weight.

Volume and concentration of contrast medium Regardless of the analysis method, first-pass studies of tumor perfusion will be improved by maximizing contrast enhancement in blood vessels and tissues to produce better signal-to-noise ratios for time–attenuation data. Maximal enhancement can be achieved by a rapid injection rate (at least 4ml/s) and by using contrast media with a high iodine concentration (i.e. 350–400mg iodine/ml, Figure 3.4). When using compartmental analysis, perfusion measurements are affected by the width of the bolus of contrast medium in the vascular system (Figure 3.4). For this analysis method, the injection rate should be at least 7ml/s, and the volume of contrast medium no greater than 50ml (protocol 2, Table 3.1). Some authors suggest injection rates of up to 10 ml/s, but injections above this rate are unlikely to improve the bolus further on account of buffering in the venous system between the

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1. Narrow vascular bolus

Artery

HU

2. Maximized tissue contrast enhancement Tissue

Time

Figure 3.4 Time–attenuation curves for CT perfusion are improved by use of high-concentration contrast media

site of injection and the heart. A narrow vascular bolus is needed because the validity of compartmental analysis requires that no contrast medium should leave the tumor region of interest by the time of measurement. The magnitude of error resulting from failure to meet this requirement will depend upon the minimum transit time (minTT) and mean transit time (MTT) for passage of contrast material through the brain tissue. Table 3.2 demonstrates the outcome of a computer simulation to assess the magnitude of error for a range of mean and minimum tissue transit times. The results are based on typical arterial time–attenuation data acquired during a CT perfusion study using a 50ml bolus of contrast medium administered with an injection rate of 7ml/s. It can be seen that, provided the minTT is at least 4 seconds, the error is small (i.e. > PS, FE ≈ PS; and for F 2 years) of four patients undergoing SU11248 treatment, two patients continued to show this rebound effect, while two patients no longer had 18FDG tumor uptake, even off treatment, indicating possible different subtypes of GIST in these patients. The successful use of PET and CT scans in evaluating the efficacy of other approved antiangiogenic agents has been similarly investigated in various solid tumors, including bevacizumab and sorafenib.31,73,74 Results of PET scans of 18FDG for thalidomide in a preclinical lung carcinoma study were less conclusive.75 For angiogenesis agents in development, examples where efficacy was evaluated using DCE-MRI include VEGFR-2 tyrosine kinase inhibitor SU5416 in a phase I study in patients with advanced solid tumors,76 and VEGFR tyrosine inhibitor vatalanib (PTK787/ZK 222584) in patients with advanced colorectal cancer and liver metastases.77 Because many antiangiogenic agents demonstrate efficacy at levels well below the maximum tolerated dose (MTD), imaging methodologies may be applied to determine dose–effect relationships at the vascular level. Imaging can help to identify the biologically active dose by quantifying biological effects (i.e. tumor perfusion) and creating corresponding dose–response curves.78 An example of this application was first shown using DCE-MRI in the clinical studies of vatalanib.77,79 Importantly, selection of the optimal biological dose for antiangiogenic agents may increase efficacy and decrease toxicities.

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CONCLUSIONS As antiangiogenic therapy for cancer comes of age, the need to establish validated imaging methodologies for assessing the tumor vasculature and its pharmacological alterations has become paramount. Magnetic resonance and PET imaging are the most common modalities used for angiogenesis imaging in clinical investigations. Quantitative methods to evaluate changes in tumor vascular parameters will allow clinicians to follow the effects of antiangiogenic agents, and to identify individual patients who are responders to specific treatments versus non-responders. As the number of angiogenesis inhibitors available in oncology practice increases, such applications will enable oncologists to select or change the agent(s) that benefit individual patients. Patients who are scheduled for serial follow-up imaging during therapy or while in remission can be assessed for vascular changes indicating progression or recurrence. Present-day imaging technologies are capable of being adapted for such purposes, although well-designed clinical studies must be conducted to establish and validate specific techniques. Angiogenesis imaging can clearly aid the drug development process, enabling it to become more efficient. Preclinical imaging systems will enable the evaluation of drug candidates, and improve the selection of lead compounds, at the drug discovery stage. The inclusion of angiogenesis imaging methodologies in clinical protocols can help to establish pharmacodynamic activity and dose optimization in early-stage clinical trials. As new therapeutic targets, such as vascular integrins, are identified, specific tracers can be designed to selectively target the same moiety for imaging purposes. Alternatively, the downstream effects of angiogenesis inhibition in tumors can be assessed, for example, by imaging annexin V, a marker of endothelial cell apoptosis. Because the tumor vasculature is heterogeneous and lacks architectural stability, the selective imaging of angiopoietin-1 (required for vascular stabilization) or angiopoietin-2 (required for destabilization), with the use of covalently linked monoclonal antibodies to paramagnetic particles, may be employed to localize and evaluate the state of the tumor vasculature. Such applications of angiogenesis imaging will require a coordinated effort between drug and imaging researchers. Finally, antiangiogenic agents may eventually play a significant role in cancer chemoprevention, or long-term maintenance therapy during clinical remission. In this setting, non-invasive imaging of angiogenesis may be useful as a screening system for incipient cancers, or for monitoring patients for early signs of recurrence. Clearly, the future of

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radiological investigation and technology development for imaging angiogenesis is compelling and bright.

ACKNOWLEDGMENTS The authors would like to acknowledge Vincent W Li, MD and Colleen Carney of the Angiogenesis Foundation; and Univ.-Prof. Dr med. Moritz A Konerding of the Institute of Anatomy and Cell Biology, Johannes Gutenberg-University Mainz, Germany.

REFERENCES 1. Folkman J. Tumor angiogenesis: therapeutic implications. N Engl J Med 1971; 285: 1182–6. 2. Boehm T, Folkman J, Browder T et al. Antiangiogenic therapy of experimental cancer does not induce acquired drug resistance. Nature 1997; 390: 404–7. 3. Folkman J. Incipient angiogenesis. J Natl Cancer Inst 2000; 92: 94–5. 4. Li CY, Shan S, Huang Q et al. Initial stages of tumor cell-induced angiogenesis: evaluation via skin window chambers in rodent models. J Natl Cancer Inst 2000; 92: 143–7. 5. Holash J, Maisonpierre PC, Compton D et al. Vessel cooption, regression, and growth in tumors mediated by angiopoietins and VEGF. Science 1999; 284: 1994–8. 6. Chang YS, di Tomaso E, McDonald DM et al. Mosaic blood vessels in tumors: frequency of cancer cells in contact with flowing blood. Proc Natl Acad Sci USA 2000; 97: 14608–13. 7. Black WC, Welch HG. Advances in diagnostic imaging and overestimations of disease prevalence and the benefits of therapy. N Engl J Med 1993; 328: 1237–43. 8. Folkman J. Endogenous angiogenesis inhibitors. APMIS 2004; 112: 496–507. 9. Hanahan D, Folkman J. Patterns and emerging mechanisms of the angiogenic switch during tumorigenesis. Cell 1996; 86: 353–64. 10. Im E and Kazlauskas A. Regulating angiogenesis at the level of PtdIns-4,5-P2. EMBO J 2006; 25: 2075–82. 11. Folkman J. Role of angiogenesis in tumor growth and metastasis. Semin Oncol 2002; 29: 15–18. 12. Folkman J and Klagsbrun M. Angiogenic factors. Science 1987; 235: 442–7. 13. Milkiewicz M, Ispanovic E, Doyle JL et al. Regulators of angiogenesis and strategies for their therapeutic manipulation. Int J Biochem Cell Biol 2006; 38: 333–57. 14. Sund M, Hamano Y, Sugimoto H et al. Function of endogenous inhibitors of angiogenesis as endothelium-specific tumor suppressors. Proc Natl Acad Sci USA 2005; 102: 2934–9. 15. Folkman J. Angiogenesis. Annu Rev Med 2006; 57: 1–18. 16. Folkman J and Haudenschild C. Angiogenesis in vitro. Nature 1980; 288: 551–6.

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17. Folkman J and Haudenschild C. Angiogenesis by capillary endothelial cells in culture. Trans Ophthalmol Soc UK 1980; 100: 346–53. 18. Li WW. Tumor angiogenesis: molecular pathology, therapeutic targeting, and imaging. Acad Radiol 2000; 7: 800–11. 19. Arbab AS, Pandit SD, Anderson SA et al. Magnetic resonance imaging and confocal microscopy studies of magnetically labeled endothelial progenitor cells trafficking to sites of tumor angiogenesis. Stem Cells 2006; 24: 671–8. 20. Asahara T, Masuda H, Takahashi T et al. Bone marrow origin of endothelial progenitor cells responsible for postnatal vasculogenesis in physiological and pathological neovascularization. Circ Res 1999; 85: 221–8. 21. Li B, Sharpe EE, Maupin AB et al. VEGF and PlGF promote adult vasculogenesis by enhancing EPC recruitment and vessel formation at the site of tumor neovascularization. FASEB J 2006; 20: 1495–7. 22. Dvorak HF. Angiogenesis: update 2005. J Thromb Haemost 2005; 3: 1835–42. 23. Konerding MA, Fait E, Gaumann A. 3D microvascular architecture of precancerous lesions and invasive carcinomas of the colon. Br J Cancer. 2001; 84: 1354–62. 24. Raghunand N, Gatenby RA and Gillies RJ. Microenvironmental and cellular consequences of altered blood flow in tumours. Br J Radiol 2003; 76 (Spec No 1): S11–22. 25. McDonald DM and Baluk P. Significance of blood vessel leakiness in cancer. Cancer Res 2002; 62: 5381–5. 26. Hashizume H, Baluk P, Morikawa S et al. Openings between defective endothelial cells explain tumor vessel leakiness. Am J Pathol 2000; 156: 1363–80. 27. McDonald DM and Choyke PL. Imaging of angiogenesis: from microscope to clinic. Nat Med 2003; 9: 713–25. 28. Jain RK. Transport of molecules, particles, and cells in solid tumors. Annu Rev Biomed Eng 1999; 1: 241–63. 29. Endrich B, Reinhold HS, Gross JF et al. Tissue perfusion inhomogeneity during early tumor growth in rats. J Natl Cancer Inst 1979; 62: 387–95. 30. Hurwitz H, Fehrenbacher L, Novotny W et al. Bevacizumab plus irinotecan, fluorouracil, and leucovorin for metastatic colorectal cancer. N Engl J Med 2004; 350: 2335–42. 31. Willett CG, Boucher Y, di Tomaso E et al. Direct evidence that the VEGFspecific antibody bevacizumab has antivascular effects in human rectal cancer. Nat Med 2004; 10: 145–7. 32. Jain RK. Normalization of tumor vasculature: an emerging concept in antiangiogenic therapy. Science 2005; 307: 58–62. 33. de Gramont A and Van Cutsem E. Investigating the potential of bevacizumab in other indications: metastatic renal cell, non-small cell lung, pancreatic and breast cancer. Oncology 2005; 69 (Suppl 3): 46–56. 34. Monk BJ, Han E, Josephs-Cowan CA et al. Salvage bevacizumab (rhuMAB VEGF)-based therapy after multiple prior cytotoxic regimens in advanced refractory epithelial ovarian cancer. Gynecol Oncol 2006; 102: 140–4. 35. Morgensztern D and Govindan R. Clinical trials of antiangiogenic therapy in non-small cell lung cancer: focus on bevacizumab and ZD6474. Expert Rev Anticancer Ther 2006; 6: 545–51. 36. Adnane L, Trail PA, Taylor I et al. Sorafenib (BAY 43–9006, Nexavar(®)), a dual-action inhibitor that targets RAF/MEK/ERK pathway in tumor cells and

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37.

38.

39. 40.

41.

42.

43. 44.

45. 46. 47. 48. 49.

50. 51.

52. 53. 54. 55.

85

tyrosine kinases VEGFR/PDGFR in tumor vasculature. Methods Enzymol 2005; 407: 597–612. Eisen T, Bukowski RM, Staehler M et al. Randomized Phase III trial of sorafenib in advanced renal call carcinoma (RCC): impact on survival. J Clin Oncol 2006; 24 (18 Suppl): 4524 (abstr). Ratain MJ, Eisen T, Stadler WM et al. Phase II placebo-controlled randomized discontinuation trial of sorafenib in patients with metastatic renal cell carcinoma. J Clin Oncol 2006; 24: 2505–12. Motzer RJ, Rini BI, Bukowski RM et al. Sunitinib in patients with metastatic renal cell carcinoma. JAMA 2006; 295: 2516–24. Motzer RJ, Hoosen S, Bello CL et al. Sunitinib malate for the treatment of solid tumours: a review of current clinical data. Expert Opin Investig Drugs 2006; 15: 553–61. Lundberg LG, Hellstrom-Lindberg E, Kanter-Lewensohn L et al. Angiogenesis in relation to clinical stage, apoptosis and prognostic score in myelodysplastic syndromes. Leuk Res 2006; 30: 247–53. Rajkumar SV, Blood E, Vesole D et al. Phase III clinical trial of thalidomide plus dexamethasone compared with dexamethasone alone in newly diagnosed multiple myeloma: a clinical trial coordinated by the Eastern Cooperative Oncology Group. J Clin Oncol 2006; 24: 431–6. List A, Kurtin S, Roe DJ et al. Efficacy of lenalidomide in myelodysplastic syndromes. N Engl J Med 2005; 352: 549–57. Richardson PG, Blood E, Mitsiades CS et al. A randomized phase 2 study of lenalidomide therapy for patients with relapsed or relapsed and refractory multiple myeloma. Blood 2006; 108: 3458–64. El-Deiry WS, Sigman CC and Kelloff GJ. Imaging and oncologic drug development. J Clin Oncol 2006; 24: 3261–73. Miller JC, Pien HH, Sahani D et al. Imaging angiogenesis: applications and potential for drug development. J Natl Cancer Inst 2005; 97: 172–87. Barentsz J, Takahashi S, Oyen W et al. Commonly used imaging techniques for diagnosis and staging. J Clin Oncol 2006; 24: 3234–44. Atri M. New technologies and directed agents for applications of cancer imaging. J Clin Oncol 2006; 24: 3299–308. Gee MS, Saunders HM, Lee JC et al. Doppler ultrasound imaging detects changes in tumor perfusion during antivascular therapy associated with vascular anatomic alterations. Cancer Res 2001; 61: 2974–82. Miles KA, Charnsangavej C, Lee FT et al. Application of CT in the investigation of angiogenesis in oncology. Acad Radiol 2000; 7: 840–50. Phongkitkarun S, Kobayashi S, Kan Z et al. Quantification of angiogenesis by functional computed tomography in a Matrigel model in rats. Acad Radiol 2004; 11: 573–82. Wang JH, Min PQ, Wang PJ et al. Dynamic CT evaluation of tumor vascularity in renal cell carcinoma. AJR Am J Roentgenol 2006; 186: 1423–30. Brasch RC, Li KC, Husband JE et al. In vivo monitoring of tumor angiogenesis with MR imaging. Acad Radiol 2000; 7: 812–23. Sorensen AG. Magnetic resonance as a cancer imaging biomarker. J Clin Oncol 2006; 24: 3274–81. Preda A, Novikov V, Moglich M et al. MRI monitoring of Avastin antiangiogenesis therapy using B22956/1, a new blood pool contrast agent, in an

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57.

58. 59.

60. 61. 62.

63.

64.

65. 66. 67. 68.

69. 70.

71.

72.

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experimental model of human cancer. J Magn Reson Imaging 2004; 20: 865–73. Brubaker LM, Bullitt E, Yin C et al. Magnetic resonance angiography visualization of abnormal tumor vasculature in genetically engineered mice. Cancer Res 2005; 65: 8218–23. Bullitt E, Ewend MG, Aylward S et al. Abnormal vessel tortuosity as a marker of treatment response of malignant gliomas: preliminary report. Technol Cancer Res Treat 2004; 3: 577–84. Bullitt E, Zeng D, Gerig G et al. Vessel tortuosity and brain tumor malignancy: a blinded study. Acad Radiol 2005; 12: 1232–40. Parikh AH, Smith JK, Ewend MG et al. Correlation of MR perfusion imaging and vessel tortuosity parameters in assessment of intracranial neoplasms. Technol Cancer Res Treat 2004; 3: 585–90. Blankenberg FG, Eckelman WC, Strauss HW et al. Role of radionuclide imaging in trials of antiangiogenic therapy. Acad Radiol 2000; 7: 851–67. Weber WA. Positron emission tomography as an imaging biomarker. J Clin Oncol 2006; 24: 3282–92. Cher LM, Murone C, Lawrentschuk N et al. Correlation of hypoxic cell fraction and angiogenesis with glucose metabolic rate in gliomas using 18F-fluoromisonidazole, 18F-FDG PET, and immunohistochemical studies. J Nucl Med 2006; 47: 410–18. Beer AJ, Haubner R, Sarbia M et al. Positron emission tomography using [18F]Galacto-RGD identifies the level of integrin alpha(v)beta3 expression in man. Clin Cancer Res 2006; 12: 3942–9. Cai W, Zhang X, Wu Y et al. A thiol-reactive 18F-labeling agent, N-[2-(4–18Ffluorobenzamido)ethyl]maleimide, and synthesis of RGD peptide-based tracer for PET imaging of {alpha}v{beta}3 integrin expression. J Nucl Med 2006; 47: 1172–80. Chen X, Park R, Khankaldyyan V et al. Longitudinal microPET imaging of brain tumor growth with F-18-labeled RGD peptide. Mol Imaging Biol 2006; 8: 9–15. Ferrara KW, Merritt CR, Burns PN et al. Evaluation of tumor angiogenesis with US: imaging, Doppler, and contrast agents. Acad Radiol 2000; 7: 824–39. Chen JC, Chen Y, Lin JH et al. Resveratrol suppresses angiogenesis in gliomas: evaluation by color Doppler ultrasound. Anticancer Res 2006; 26: 1237–45. Iordanescu I, Becker C, Zetter B et al. Tumor vascularity: evaluation in a murine model with contrast-enhanced color Doppler US effect of angiogenesis inhibitors. Radiology 2002; 222: 460–7. Shigeno K, Igawa M, Shiina H et al. Transrectal colour Doppler ultrasonography for quantifying angiogenesis in prostate cancer. BJU Int 2003; 91: 223–6. Marzola P, Degrassi A, Calderan L et al. Early antiangiogenic activity of SU11248 evaluated in vivo by dynamic contrast-enhanced magnetic resonance imaging in an experimental model of colon carcinoma. Clin Cancer Res 2005; 11: 5827–32. Raut C, Van Den Abbeele A, Ramaiya N et al. Patterns of PET response after long-term sunitinib therapy in patients with imatinib resistant gastrointestinal stromal tumors. J Clin Oncol 2006; 24 (18 Suppl): 9547(abstr). Scott AM, Mitchel P and O’Keefe GJ. Tumor perfusion during treatment with SU11248 (sunitinib malate) in patients with advanced malignancies as assessed by (oxygen-15)-water. Presented at 4th International Symposium on Targeted Therapies, 16–18 March, 2006, Amsterdam, The Netherlands.

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73. Bruckner H, Hrehovovich V and Degregorio P. Laboratory based low dose combination chemotherapy + bevacizumab for recurrent refractory and unresectable gastric cancer. J Clin Oncol 2006; 24 (18 Suppl): 14133(abstr). 74. Clark JW, Eder JP, Ryan D et al. Safety and pharmacokinetics of the dual action Raf kinase and vascular endothelial growth factor receptor inhibitor, BAY 43–9006, in patients with advanced, refractory solid tumors. Clin Cancer Res 2005; 11: 5472–80. 75. Kim DH, Choe YS, Jung KH et al. Synthesis and evaluation of 4-[(18)F]fluorothalidomide for the in vivo studies of angiogenesis. Nucl Med Biol 2006; 33: 255–62. 76. Dowlati A, Robertson K, Radivoyevitch T et al. Novel phase I dose de-escalation design trial to determine the biological modulatory dose of the antiangiogenic agent SU5416. Clin Cancer Res 2005; 11: 7938–44. 77. Morgan B, Thomas AL, Drevs J et al. Dynamic contrast-enhanced magnetic resonance imaging as a biomarker for the pharmacological response of PTK787/ZK 222584, an inhibitor of the vascular endothelial growth factor receptor tyrosine kinases, in patients with advanced colorectal cancer and liver metastases: results from two phase I studies. J Clin Oncol 2003; 21: 3955–64. 78. Padhani AR and Neeman M. Challenges for imaging angiogenesis. Br J Radiol 2001; 74: 886–90. 79. Rehman S and Jayson GC. Molecular imaging of antiangiogenic agents. Oncologist 2005; 10: 92–103.

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6 Tumors of the brain, head, and neck

Part A: CT perfusion imaging in cerebral neoplasms Karim Samji and Kenneth A Miles

INTRODUCTION Despite the advent of magnetic resonance (MR), conventional computed tomography (CT) remains a routine technique in the management of patients with cerebral neoplasms, not only for diagnosis and staging but also in treatment planning and the evaluation of treated patients for suspected recurrence. The success of these anatomical imaging modalities is based on the enhancement of neoplastic brain tissue with intravenously administered contrast material. An intact blood–brain barrier normally excludes the contrast medium from the brain, as its microvessel architecture allows only for the passage of small molecules through its tight junctions and narrow intracellular gaps. In contrast, tumor microvessels have incomplete basement membranes that are abnormally leaky to circulating molecules and contrast agents. The rate of transendothelial diffusion of contrast material is a reflection of the integrity of the microvessel wall.1 Furthermore, contrast material within an increased density of tumor vessels can augment this extravascular contrast enhancement. Although conventional imaging is capable of delineating areas of increased contrast enhancement, the eye cannot differentiate between the intravascular component and the extravascular component of contrast enhancement. The use of CT perfusion to evaluate these components of contrast enhancement separately has the potential to improve the

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delineation of tumors and to provide physiological information about the status of the tumor vasculature, which in turns reflects tumor biology and aggression.2 Although such information may also be available from pathological examination of tumor tissue, in the case of cerebral tumors, biopsy is highly invasive with a risk of hemorrhage and infection, and is limited by sampling error. Hence, the role of imaging in cerebral neoplasms has begun to shift to provide information on tumor physiology as well as anatomy. CT perfusion is one of several techniques that have been developed to perform functional imaging in order to overcome the inherent limitations of anatomical imaging. By considering contrast agents as physiological indicators, it is possible to calculate values for tissue perfusion, including blood flow, blood volume, and capillary permeability and leakage.3 This may, in turn, provide invaluable information that will aid with diagnosis, treatment, and follow-up in patients with cerebral neoplasms. The assessment of the vascular permeability of cerebral tumors was one of the first reported applications of CT perfusion (Chapter 1). The use of CT perfusion to study cerebral tumors is also a natural extension to the technique’s wider application in the assessment of cerebrovascular disease.4 The brain is largely stationary during image acquisition, and therefore movement artifacts are considerably less likely than for other body regions. However, the image thresholds that are commonly used to remove cerebral blood vessels when performing CT perfusion for patients suffering stroke may not be appropriate for tumor imaging, because these thresholds can also eliminate highly vascularized tumors such as meningiomas. The study of cerebral tumors with CT perfusion is essentially analogous to the application of MR in this clinical context. Contrastenhanced dynamic MR imaging techniques have proven their value in the detection, grading, and treatment monitoring of cerebral neoplasms.1,5–8 However, CT perfusion has some advantages over MR, including lower cost and greater availability. Quantification is also simpler than with MR, as the relationship between signal and contrast agent concentration for CT is linear, and quantitative analysis software is available commercially.9 CT perfusion measurements also demonstrate greater reproducibility.10 By using existing technologies and contrast agents, CT perfusion can be readily incorporated into routine conventional CT examinations, and has the potential to be combined with positron emission tomography (PET) when performed using integrated PET–CT systems.

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MENINGIOMA Meningiomas are the most common non-glial primary brain tumors, accounting for approximately 14–20% of all brain tumors in adults.11,12 Meningiomas have characteristic findings on conventional CT imaging. Hence, their differentiation from intra-axial tumors is relatively easy. The typical meningioma is a homogeneous, hemispheric, markedly enhancing extra-axial mass located over the cerebral convexity, in the parasagittal region, or arising from the sphenoid wing.13 However, atypical features such as large meningeal cysts, circular contrast enhancement, intratumoral hemorrhage, marked peritumoral edema, and various metaplastic changes (including fatty transformation) are encountered in up to 15% of patients and can be particularly misleading.13,14 As compared to normal cerebral tissues, meningiomas show markedly increased values of blood flow, blood volume, and vascular permeability on CT perfusion15–17 (Figure 6.1). The first reported study investigating the use of functional CT in the diagnosis of meningiomas was performed by Jinkins and Nuri Sener.15 They imaged a total of 11 patients with histologically proven benign meningiomas with a contrast-enhanced dynamic CT protocol, and constructed dynamic perfusion curves based on the relative changes in CT number versus time. Every patient imaged demonstrated intratumoral neovascularity during the stage of maximal arterial perfusion, reflective of early extravasation of contrast medium into the tumor.15 The brain tissue abutting upon the tumor also demonstrated varying perfusion patterns. The cortex overlying white matter edema revealed relative hyperperfusion, a phenomenon that may reflect autoregulation loss and secondary cerebral ‘luxury’ perfusion.15 Hypoperfusion of the cerebrum interposed between the tumor and a rigid structure such as the calvarium was shown on the dynamic perfusion curves, compatible with the theory of compressive cerebral hypoperfusion.15 In a CT perfusion study of six patients with intracranial meningioma undergoing brachytherapy, Bondestam et al. reported perfusion values of 231 ± 58 and 224 ± 54 ml/min/100 g in the tumor center and periphery respectively.16 Cheong et al. reported CT perfusion values from tumor and normal brain in a series of six patients with intracranial meningioma.17 These researchers compared values for perfusion, fractional volume of tracer distribution within the intravascular and extravascular spaces, and the permeability surface area (PS) product obtained by three different kinetic models. Absolute values for these parameters depended on the model used. Although the distributed

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b

500 ml/min/100 ml

0 ml/min/ml

c 20 ml/100 ml

0 ml/100 ml

d

50 ml/min/100 ml

0 ml/min/100 ml

Figure 6.1 Contrast-enhanced computed tomography (CT) (a), CT perfusion (b), blood volume (c), and vascular permeability image (d) from a patient with a right frontal meningioma. Tumor values for perfusion (94 ml/min/100 ml), relative blood volume (7.2 ml/100 ml), and permeability (18.9 ml/min/100 ml) are considerably greater than in normal cerebral tissues

parameter model was considered more reliable, there were significant linear correlations between estimates obtained from all kinetic models, and normal values from compartmental analysis more closely approximated equivalent values reported using other techniques.17 Normal cerebral tissues yielded PS values close to zero, whereas the values in meningioma were substantially higher due to a disrupted blood–brain barrier (Table 6.1). Tissue perfusion and the fractional intravascular volume of tracer (i.e. relative blood volume) were also increased in meningioma in comparison to normal brain tissue. Hence, the authors concluded that enhancement of meningioma on dynamic contrastenhanced CT could be based on a combination of increased extravasation of tracer, high perfusion, and dense vasculature.17

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Table 6.1 Computed tomography (CT) perfusion values for normal cerebral tissues and meningioma as reported by Cheong et al. using compartmental analysis17 Perfusion (ml/min/100 g) Normal gray matter Normal white matter Meningioma

58.4 ± 12.5 38.8 ± 15.5 137 ± 33.5

Relative blood volume (ml/100 g) 1.74 ± 0.64 1.09 ± 0.29 10.7 ± 7.12

Permeability (ml/min/100 g) 0.17 ± 0.15 0.08 ± 0.12 52.4 ± 59.3

GLIOMA Gliomas are astrocytic, oligodendroglial, and ependemal in origin, and account for 70% of all brain tumors.18 The most common (65%) and most malignant histological subtype is the glioblastoma, and the 5-year survival rate for patients with glioblastoma is less than 3%.18 The introduction of CT and MR imaging in recent decades has significantly improved the care of patients with intracranial gliomas. Novel imaging techniques can add to the radiologist’s diagnostic armamentarium, and allow more precise anatomical localization of the tumor as well as provide a means for assessing tumor grade and measuring its progression and response to treatment. Perfusion imaging of brain tumors has been shown to provide valuable information about cerebral gliomas.1,2,5,6,8,19–21 Specifically, dynamic imaging-generated maps of cerebral blood volume (CBV) demonstrated a statistically significant increase in CBV in glioma when compared to normal brain tissue.5,8 In addition, disruptions in the integrity of the blood–brain barrier are noted, as evidenced by an increased PS in glioma when compared to normal brain tissue.2,21,22 Nabavi et al. and Eastwood and Provenzale have also documented their finding of increased cerebral blood flow (CBF) in cerebral gliomas in published case reports.19,20 Although cerebral gliomas exhibit increased perfusion and blood volume, the values are often only slightly higher than in normal cerebral tissues2,19,20 (Figures 6.2 and 6.3). For example, the cases reported by Nabavi et al. and Eastwood and Provenzale yield relative blood volumes of 5.5 and 2.3 ml/100 g respectively.19,20 On the other hand, these tumors are readily seen on CT images of vascular permeability which can sometimes reveal areas of tumor not detected by conventional contrastenhanced CT (Figure 6.3). The grading of gliomas is of significant clinical importance, as therapeutic options for these tumors differ considerably depending on grade.

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b

Figure 6.2 Conventional contrast-enhanced CT (a) and CT perfusion (b) images of a patient with a cerebral glioma (arrow) obtained during stereotactic imaging. Tumor perfusion values are close to those of cerebral cortex

High grade gliomas are usually treated with adjuvant radiation therapy or chemotherapy after resection, whereas this is not the case for low grade gliomas.6 Conventional contrast-enhanced CT can provide only a limited amount of information concerning the grade of glioma. Thus, the current standard for tumor grading is histopathological assessment, but this is complicated by the heterogeneous nature of gliomas, and histological samples obtained at biopsy may be subject to sampling error.8 Hence, there is a need for a more accurate and less invasive method of tumor grading. In a study of 22 patients, Ding et al. assessed the ability of CT perfusion to grade cerebral glioma.21 Both CBV and PS were strongly correlated with tumor grade, and the differences in CBV and PS were statistically significant between low- and high-grade glioma.21 In addition, receiver operating characteristic curves revealed better diagnostic performance for PS in comparison to CBV for determining glioma grade.21 These findings are analogous to those reported previously using MR.1,5,6,8

CEREBRAL LYMPHOMA Primary central nervous system lymphoma exhibits a biological behavior that is different from that of other primary brain tumors, leading to differences in treatment regimen. The appearances of cerebral lymphoma

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b

C

Figure 6.3 Conventional contrast-enhanced CT (a), BBB-permeability image (b) and blood volume image (c) from a patient with recurrent left frontal glioma. Note the second focus of tumor (arrow) seen on the BBB-permeability image which is not seen on conventional CT. (Note the normally permeable choroids plexuses are seen as areas of high permeability posteriorly.) (Reproduced with permission from reference 23)

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on conventional CT can also differ from other tumors, with most cases showing homogeneous contrast enhancement. Warnke et al. have used CT perfusion to study seven patients with cerebral lymphoma.24 As in the case of other tumors, vascular permeability was markedly increased compared to normal cerebral tissue (2.95 ± 1.06 ml/min/100 g), and higher than in other cerebral tumors assessed using the same technique. However, the relative blood volume of tumor was comparable to that of normal brain (2.7 ± 2.4 ml/100 g), and tumor perfusion values, measured in this study by xenon CT, were also close to those for normal white matter (43.2 ± 10.5 ml/min/100 g).

METASTATIC BRAIN DISEASE CT perfusion is potentially of value in the diagnosis, management, and post-therapeutic monitoring of metastatic brain disease.25,26 Small metastases are frequently missed with contrast-enhanced CT.27 However, the discovery of multiple, small, asymptomatic lesions may direct more patients towards surgical resection and radiation treatment. This approach has been shown to control intracranial disease, and may improve survival and quality of life.27,28 CT perfusion findings in patients with cerebral metastases have been reported in two cases by Roberts et al.25 In common with other cerebral tumors, the vascular permeability was significantly raised, up to more than 40 ml/min/100 g in a case of metastatic rectal cancer. However, values for tumor perfusion and relative blood volume were much more variable, in one case close to values in normal cerebral tissues. Although this technique may be of value in therapeutic monitoring, further studies are needed to determine the diagnostic accuracy of CT perfusion as compared to conventional CT for this indication, and whether there are any long-term benefits from adopting such a protocol.

CT PERFUSION TO DIFFERENTIATE BETWEEN BRAIN TUMOR AND CEREBRAL INFARCTION It is occasionally difficult to differentiate between brain tumor and cerebral infarction during the initial presentation. Multiple investigations, such as magnetic resonance imaging (MRI) and fluorodeoxyglucose (FDG)-PET, may be adopted in such circumstances to establish a firm diagnosis. Cases reported by Keith et al. and Lee et al. illustrate the potential for CT perfusion to provide a readily available and low-cost method to differentiate between these two pathologies (Figure 6.4).29,30

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CBF

Figure 6.4 Conventional CT and perfusion CT images (cerebral blood volume (CBV) and cerebral blood flow (CBF)) from a 56-year-old man presenting with a sudden onset of expressive dysphasia followed by focal right-sided motor seizure activity. The conventional CT demonstrates a hypodense area in the left hemisphere (arrow) which was initially considered to be an early cerebral infarct. However, CT perfusion images demonstrate a slight increase in blood flow in the corresponding area, implying low-grade tumor. (Reproduced with permission from reference 29)

Cerebral tumors typically exhibit CBF and CBV values that are similar to or higher than those obtained from normal brain tissue. In contrast, nonhemorrhagic stroke is readily identified on CT perfusion as an area of reduced CBF.29 CBV may be normal in the presence of reversible ischemia, reflecting preservation of vascular autoregulation, whereas irreversible infarction is characterized by matched reductions in CBF and CBV.3 However, images of vascular permeability are less likely to differentiate cerebral tumor and infarction, as values are increased above normal in both pathologies.

THERAPEUTIC PLANNING AND MONITORING Radiation planning The management of malignant cerebral neoplasms requires aggressive treatment modalities including surgery and radiotherapy, but the prognosis of these tumors still remains poor. In order to maximize the radiation dose to the tumor and minimize the damage to normal surrounding tissue, an accurate and reliable identification of viable tissue margins needs to be obtained.31 Anatomic images may not sufficiently identify eloquent cortex, and may misjudge the distance from potential treatment margins. Incorporation of physiological imaging into the planning process may improve tumor targeting by more accurate target definition and reduction of the total target volume. Furthermore, there is interest in

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b

Figure 6.5 Tumor target volumes constructed by a radiation oncologist on the basis of conventional CT (a) and permeability CT (b) images. The improved delineation of the tumor on the permeability image has resulted in a change in target volume

using physiological imaging to identify the most active areas of tumor which can then be selected for more intense radiotherapy. Conventional CT images are widely used as the basis for defining target volumes for radiotherapy treatment of cerebral tumors. Even when other functional imaging modalities are incorporated into the planning process, a CT image is usually also required to provide a map of X-ray attenuation. CT perfusion can be readily appended to the conventional CT performed in such circumstances, and the ability for CT-derived images of vascular permeability to improve the delineation of cerebral tumors (Figure 6.3) highlights the potential for CT perfusion to contribute to the definition of target volumes. Furthermore, the association between permeability on CT and tumor grade21 suggests the potential for permeability maps to be used as a basis for intensity modulation. A pilot study by Reardon et al. has shown that CT-derived images of vascular permeability can potentially impact on tumor target volumes (Figure 6.5). Two radiation oncologists constructed target volumes for six cerebral tumors using conventional CT and blood–brain barrier permeability (BBBP) CT images. Target volumes constructed on BBBP images tended to be smaller (change in size –43% to +33%, median –19%, p = 0.06) and were displaced by 1.6% to 24% (median 11.5%) of the mean target volume diameter (Reardon, personal communication). Further prospective studies are required to assess the impact of this approach on radiotherapy treatment efficacy and toxicity.

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THERAPEUTIC MONITORING Conventional CT and MR imaging are routinely used in the follow-up of patients after radiation treatment or chemotherapy for cerebral neoplasms. In the light of the limitations of RECIST (Response Evaluation Criteria in Solid Tumors) and other anatomically based response criteria, there is an increasing interest in the application of imaging techniques that assess tumor physiology for the evaluation of tumor response to therapy (Chapter 12). In the case of radiotherapy for cerebral tumors, the situation is further complicated by difficulties in differentiating between radiation necrosis and recurrent neoplastic growth. CT perfusion has been successfully used to demonstrate effects of both chemotherapy and radiotherapy upon cerebral tumors. Changes in tumor vascular permeability have been shown in response to dexamethasone32 and the bradykinin analog RMP-7.22 Radiotherapy response has been studied in small series of patients with meningioma undergoing brachytherapy.16 From initial values of 231 ml/min/100 ml, perfusion in the center of the tumor fell significantly at 3 months (by 41%) and 1 year (by 68%) following the implantation of iodine seeds, whereas perfusion in the tumor periphery fell little. Millar et al. related the changes in CT perfusion parameters in normal brain to symptoms of toxicity following whole-brain irradiation in 14 patients with cerebral metastases.26 After an initial 19% increase, PS values fell to baseline values at day 5. Changes in mean transit time and blood volume correlated with headache and nausea scores, respectively. Both radiation necrosis and recurrent tumor can present as an enhancing brain lesion following radiotherapy. Although no systematic studies of the ability for CT perfusion to distinguish these pathologies systematic has been performed, the potential role for perfusion imaging in this context is suggested by the MR perfusion study of Sugahara et al.34 The ratio of CBV in the lesion compared to that in normal brain tended to be higher in recurrent tumor than in radiation necrosis (median values: 2.60 vs. 1.37). Initial experience with CT perfusion has found very similar results with equivalent CBV ratios of 2.54 and 1.17. A cut-off value of 1.4 gave a sensitivity of 88.5% and specificity 80% for the diagnosis of recurrence versus necrosis.

CONCLUSION CT perfusion is emerging as a viable functional imaging modality of value as an adjunct to conventional CT in the diagnosis, treatment

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planning, and follow-up of cerebral neoplasms. Although constrained by the need to use ionizing radiation, the potential for adverse reaction to contrast agent, and limited anatomic coverage, CT perfusion may prove to have advantages over MR imaging in the assessment of tumor angiogenesis. The technique is fast and readily available on most modern spiral CT scanners equipped with the appropriate software, and can be easily incorporated into conventional CT examinations performed as part of the clinical management for many patients with cerebral tumors.

Part B : CT perfusion in head and neck cancer Robert Hermans

INTRODUCTION Beginning with the observations of Gray et al.,35 oxygen is known to be a powerful radiosensitizer: oxygen enhances the formation of free radicals or draws existing free radicals into chain reactions, producing new damaging free radicals; another mechanism postulated is that many irradiation-induced chemical changes are being blocked by the presence of oxygen. An imbalance between oxygen supply and consumption, largely resulting from the presence of inadequate and heterogeneous vascular networks, leads to chronic tumor hypoxia.36 Tumor hypoxia is not only a chronic phenomenon, related to vascular density, in cells which are situated far from the vessels,37 but also an acute cyclic phenomenon as part of a dynamic process in which the vessels periodically open and close. In some tumors, a high interstitial pressure may limit the diffusion of oxygen towards the cells. In the second half of the 20th century, studies showed the clinically relevant effect of tumor hypoxia on the response to radiation therapy.36 Also, in head and neck cancer, several studies found a significantly worse response to irradiation in tumors with a hypoxic subvolume.37–9 Recent laboratory and clinical data have shown that hypoxia is also associated with a more malignant phenotype, affecting genomic stability, apoptosis, angiogenesis, and metastasis.40 Direct quantification of tumor oxygenation can be expected to be of important prognostic value. Tumor oxygenation has been measured

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invasively using oxygen-sensitive needle electrodes in animal tumors and in certain human tumors. This technique can be used in head and neck tumors,39 but many primary tumors in this region are deeply seated, difficult to reach, and close to critical anatomic structures. There is a need for a non-invasive method to measure tumor oxygenation, not only as a predictor of outcome, but also to select patients for concomitant radiosensitizing therapy to overcome the hypoxia effect. For example, it has been reported that the oxygenation of head and neck tumors improves during carbogen breathing41 or hyperbaric oxygenation.41 Increased oxygenation of tumors treated with carbogen and nicotinamide has been demonstrated in patients. Promising results have been obtained in non-randomized clinical studies using this combination in conjunction with accelerated irradiation.43,44 Perfusion can be defined as the blood flow through a tissue of interest per unit of volume. Tumor perfusion and tumoral oxygen concentration are factors which are usually strongly linked, although tumor oxygenation also depends on oxygen consumption by the tumor cells. The oxygen availability or oxygen supply is the amount of oxygen carried by the blood to a given tissue per unit of time; it is the product of the perfusion rate and the arterial oxygen concentration. The cross-sectional images obtained with CT provide detailed morphologic information. Besides anatomical analysis, functional analysis of the images is also possible.

CT PERFUSION IMAGING IN HEAD AND NECK CANCER Few groups have investigated the possibilities of dynamic CT to obtain measurements of blood flow in head and neck cancer. The data reported by Hermans et al.45–47 were obtained by using the ‘gradient method’.48–50 Perfusion rates in primary head and neck cancer, as measured by dynamic CT, were shown to be obtainable with good intra- and interobserver reproducibility.46 The mean CT-determined perfusion value, measured in 105 patients, was 88.8 ml/min/100 g (median 83.5; standard deviation (SD) 46.5).47 Brix et al.51 reported their findings in six patients suffering head and neck cancer. These authors used an extension of the gradient method, taking into account also the bidirectional diffusion of the contrast medium across the capillary wall by using a two-compartmental model; this should allow a more comprehensive characterization of tissue microcirculation. A mean regional blood flow of 45 ± 16 ml/min/100 g was measured.

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Using a deconvolution-based CT perfusion method based on linear system theory as described by Jaschke et al.52, average values of tissue blood volume, blood flow, mean transit time, and capillary permeability surface area product have been reported in head and neck cancer;53 using this method, the average perfusion rate measured in 14 patients was 126.2 ml/min/100 g (SD 76.5). Several problems exist when such methods are applied to dynamic CT: the transit time is difficult to obtain from the tissue time–density curve, iodinated contrast agents do not remain purely intravascular, and the latter part of the time–density curve is difficult to analyze due to recirculation of the contrast medium and other artifacts.49

USE OF CT PERFUSION AS PREDICTOR OF LOCAL CONTROL AFTER RADIOTHERAPY The aim of a study reported by Hermans et al.47 was to test the hypothesis that the outcome of patients is affected by the tumor perfusion rate. Multivariate analysis confirmed the value of CT-determined tumor perfusion as an independent predictor of local control in head and neck cancer, treated by definitive radiotherapy, with or without adjuvant chemotherapy. Patients with a low perfusion value showed a statistically significantly higher local failure rate than those with a high perfusion value (Figure 6.6). Presumably, this is linked to more extensive and/or a higher degree of hypoxia in low-perfused tumors, as suggested by others.54 The perfusion rate was also found to be independent of tumor volume. CT-determined primary tumor volume has been shown to be an important predictor of local control for several head and neck cancer sites, including glottic,55 supraglottic,56 hypopharyngeal,57 and nasopharyngeal cancer.58,59 As perfusion rate is independent of tumor volume, it can be considered an additional parameter helping to predict the outcome of the patient after treatment. The perfusion rate was found to be independent of the T-classification.47 Local control was significantly different according to the median perfusion rate in the subgroups T3 and T4 (Figures 6.7 and 6.8). Particularly in the T4-group, the difference in local outcome between those patients with high and low perfusion values is very pronounced. This opens perspectives to use CT perfusion as a tool to select patients suffering advanced head and neck cancer who may benefit from concomitant treatment during irradiation. The practical advantages of this method are the low extra burden to patients, the ease of implementing it on existing CT machines, and

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1 < 83.5 ml/min/100 g

0.9

> 83.5 ml/min/100 g 0.8

Local control

0.7 0.6 0.5 0.4 0.3 0.2 0.1

p < 0.05

0 0

6

12

18

24

30

36

42

Time (months)

Figure 6.6 Local control over time in 105 patients suffering head and neck cancer. The patients are stratified into two groups according to the median primary tumor perfusion value, as determined by dynamic CT using the gradient method. (Reproduced with permission from reference 47)

1 < 83.5 ml/min/100 g > 83.5 ml/min/100 g

0.9 0.8

Local control

0.7 0.6 0.5 0.4 0.3 0.2 p < 0.05

0.1 0 0

6

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Time (months)

Figure 6.7 Local control versus perfusion rate (patients classified as T3 (n = 39)) plotted over time after start of radiotherapy. (Reproduced with permission from reference 47)

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1 < 83.5 ml/min/100 g > 83.5 ml/min/100 g

0.9 0.8

Local control

0.7 0.6 0.5 0.4 0.3 0.2 p < 0.05

0.1 0 0

6

12 Time (months)

18

24

Figure 6.8 Local control versus perfusion rate (patients classified as T4 (n = 34)) plotted over time after start of radiotherapy. (Reproduced with permission from reference 47)

the low cost compared to other non-invasive and invasive methods to estimate tumor perfusion or oxygenation. However, CT perfusion also has a number of shortcomings. An important limitation is that only one level through the tumor can be examined; therefore, the obtained results may not be representative for the entire tumor. Averaging the perfusion rate in a large region of interest (ROI) may also not provide representative results, as a few hypoxic clonogenic cells in a small low-perfusion area may determine the long-term outcome in spite of a high overall perfusion rate. Furthermore, it may prove difficult to anatomically define hypoxic subvolumes, of interest in intensity-modulated radiation therapy.60 However, the recent technical developments in computed tomography, using multidetector technology, allow simultaneous examination of several tumor levels;61 combined with advances in software analysis tools, parametric quantification of perfusion in the entire tumor volume now becomes feasible (Figure 6.9). Nevertheless, small movements of the soft tissue structures in the neck, such as caused by arterial pulsations, breathing, or swallowing, may occur, both in the axial plane and in the long axis of the patient, rendering them difficult to compensate. Such tissue movements may make a parametric analysis difficult to realize in all patients. The presented technique is not well suited to examine the known temporal heterogeneity of tumor perfusion, as several hours are needed before the baseline tissue density is restored through renal clearance of

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Figure 6.9 Time frame of a dynamic series obtained during the intravenous injection of a contrast agent bolus, using a four-row multidectector CT machine. A pixel-by-pixel analysis of the dynamic image series was performed. The perfusion data are color-coded and mapped on the anatomical images. The perfusion rate shows a heterogeneous distribution throughout the tumor (calculated using Body CT Perfusion; Siemens Medical Solutions, Erlangen, Germany)

the contrast medium; temporal fluctuations in tumor perfusion therefore cannot be estimated. Until now, reported perfusion values were estimated in the primary tumor location only. From a functional point of view, obtaining control of the primary tumor by (chemo)radiotherapy is more important than controlling the adenopathies. Residual neck disease after irradiation can be surgically treated, causing some morbidity, but usually less severe than surgical resection of the primary tumor.62

CONCLUSION The perfusion rate of head and neck tumors can be determined by dynamic CT, during a routine CT study, with little supplementary

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burden for the patient. This parameter has significant predictive value for local outcome after irradiation with curative intent, and therefore may be considered a useful tool to select patients for concomitant treatment in advanced head and neck cancer.

REFERENCES 1. Roberts HC, Roberts PL, Brasch RC, Dillon WP. Quantitative measurements of microvascular permeability in human brain tumors achieved using dynamic contrast-enhanced MR imaging: correlation with histologic grade. AJNR Am J Neuroradiol 2000; 21: 891–9. 2. Leggett DAC, Miles KA, Kelley BB. Blood-brain barrier and blood volume imaging of cerebral glioma using functional CT: a pictorial review. Eur J Radiol 1999; 30: 185–90. 3. Miles KA. Brain perfusion: computed tomography applications. Neuroradiology 2004; 46: S194–S200. 4. Miles KA, Eastwood JD, Konig M, eds. Multidetector Computed Tomography in Cerebrovascular Disease: CT Perfusion Imaging. Abingdon, UK: informa Healthcare, 2007. 5. Jackson A, Kassner A, Annesley-Williams D et al. Abnormalities in the recirculation phase of contrast agent bolus passage in cerebral gliomas: comparison with relative blood volume and tumor grade. AJNR Am J Neuroradiol 2002; 23: 7–14. 6. Law M, Yang S, Babb JS et al. Comparison of cerebral blood volume and vascular permeability from dynamic susceptibility contrast-enhanced perfusion MR imaging with glioma grade. AJNR Am J Neuroradiol 2004; 25: 746–55. 7. Provenzale JM, Mukundan S., Dewhirst M. The role of blood-brain barrier permeability in brain tumor imaging and therapeutics. AJR Am J Roentgenol 2005; 185: 763–7. 8. Aronen HJ, Gazit IE, Louis DN et al. Cerebral blood volume maps of gliomas: comparison with tumor grade and histologic findings. Radiology 1994; 191: 41–51. 9. Miles, KA, Charnsangavej C, Lee FT et al. Application of CT in the investigation of angiogenesis in oncology. Acad Radiol 2000; 7: 840–50. 10. Miles KA, Wintermark M. Comparison with other techniques. In: Miles KA, Eastwood JD, Konig M, eds. Multidetector Computed Tomography in Cerebrovascular Disease: CT Perfusion Imaging. Abingdon, UK: Informa Healthcare, 2007; 159–70. 11. Rachlin JR. Etiology and biology of meningiomas. In: Al-Mefty O, ed. Meningiomas. New York: Raven Press, 1991; 27–35. 12. Russel DS, Rubenstein LJ. Pathology of Tumors of the Central Nervous System, 4th edn. London: Edward Arnold, 1995: 66–91. 13. Buetow MP, Buetow PC, Smirniotopoulos JG. Typical, atypical and misleading features in meningioma. Radiographics 1991; 11: 1087–106. 14. Hakyemez B, Yildirim N, Gokalp G, Erdogan C, Parlak M. The contribution of diffusion-weighted MR imaging to distinguish typical from atypical meningiomas. Neuroradiology 2006; 48: 513–20. 15. Jinkins JR, Nuri Sener R. The characteristics of cerebral meningiomas and surrounding tissues on dynamic CT. Neuroradiology 1991; 33: 499–506.

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16. Bondestam S, Halavaara JT, Jaaskelainen JE et al. Perfusion CT of the brain in the assessment of flow alterations during brachytherapy of meningioma. Acta Radiol 1999; 40: 469–73. 17. Cheong LHD, Lim CCT, Koh TS Dynamic contrast-enhanced CT of intracranial meningioma: comparison of distributed and compartmental tracer kinetic models – initial results. Radiology 2004; 232: 921–30. 18. Ohgaki H, Kleihues P. Epidemiology and etiology of gliomas. Acta Neuropathol 2005; 109: 93–108. 19. Nabavi DG, Cenic A, Craen RA et al. CT assessment of cerebral perfusion: experimental validation and initial clinical experience. Radiology 1999; 213: 141–9. 20. Eastwood JD, Provenzale JM. Cerebral blood flow, blood volume, and vascular permeability of cerebral glioma assessed with dynamic CT perfusion imaging. Neuroradiology 2003; 45: 373–6. 21. Ding B., Ling HW, Chen KM, Jiang H, Zhu YB. Comparison of cerebral blood volume and permeability in preoperative grading of intracranial glioma using CT perfusion imaging. Neuroradiology 2006; 48: 773–81. 22. Ford JM, Miles KA, Hayball MP et al. A simplified technique for measurement of blood-brain barrier permeability using computed tomography: preliminary results of the effect of RMP-7. In: Faulkner K, Carey B, Crellin A, Harrison RM, eds. Quantitative Imaging in Oncology. Proceedings of the 19th LH Gray Conference. London: British Institute of Radiology, 1996: 1–3. 23. Miles KA, Dawson P. CT permeability imaging: blood-brain barrier imaging. In: Miles K, Blomley M, Dawson P, eds. Functional Computed Tomography. Oxford: ISIS Medical Media, 1997: 157–65. 24. Warnke PC, Timmer J, Ostertag CB, Kopitzki K. Capillary physiology and drug delivery in central nervous system lymphomas. Ann Neurol 2005; 57: 136–9. 25. Roberts HC, Roberts TP, Lee TY, Dillon WP. Dynamic, contrast-enhanced CT of human brain tumors: quantitative assessment of blood volume, blood flow, and microvascular permeability: report of two cases. AJNR Am J Neuroradiol 2002; 23: 828–32. 26. Millar BM, Purdie TG, Yeung I et al. Assessing perfusion changes during whole brain irradiation for patients with cerebral metastases. J Neurooncol 2005; 71: 281–6. 27. Young RJ, Sills AK, Brem S. Neuroimaging of metastatic brain disease. Neurosurgery 2005; 57 (Suppl 4): 10–23. 28. Sheehan J, Niranjan A, Flickinger JC, Kondziolka D, Lunsford LD. The expanding role of neurosurgeons in the management of brain metastases. Surg Neurol 2004; 62: 32–40. 29. Keith CJ, Griffiths M, Peterson B, Anderson RJ, Miles KA. Computed tomography perfusion imaging in acute stroke. Australas Radiol 2002; 46: 221–30. 30. Lee R, Cheung RTF, Hung KN. Use of CT perfusion to differentiate between brain tumor and cerebral infarction. Cerebrovasc Dis 2004; 18: 77–83. 31. Gross MW, Weber WA, Feldmann HJ et al. The value of F-18-fluorodeoxyglucose PET for the 3-D radiation treatment planning of malignant gliomas. Int J Radiat Oncol Biol Phys 1998; 41: 989–95. 32. Yeung WT, Lee TY, Del Maestro RF et al. Effect of steroids on iopamidol blood-brain barrier transfer constant and plasma volume in brain tumors measured with X-ray computed tomography. J Neurooncol 1994; 18: 53–60.

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33. Jain R, Elika S, Searpace L, Schultz L, Mikkelsen T, Pakel S. Perfusion CT: Initial experience in differentiating recurrent brain tumors from radiation effects/ radiation necrosis. European Radiology 2007; 17: 291. 34. Sugahara T, Korogi Y, Tomiguchi S et al. Posttherapeutic intraaxial brain tumor: the value of perfusion-sensitive contrast-enhanced MR imaging for differentiating tumor recurrence from non-neoplastic contrast-enhancing tissue. Am J Neuroradiol 2000; 21: 901–9. 35. Gray LH, Conger AD, Ebert M et al. The concentration of oxygen dissolved in tissues at the time of irradiation as a factor in radiotherapy. Br J Radiol 1953; 26: 638–48. 36. Vaupel P, Kelleher DK, Hockel M. Oxygen status of malignant tumors: pathogenesis of hypoxia and significance for tumor therapy. Semin Oncol 2001; 28 (Suppl 8): S29–35. 37. Dunst J, Stadler P, Becker A et al. Tumor volume and tumor hypoxia in head and neck cancers. The amount of the hypoxic volume is important. Strahlenther Onkol 2003; 179: 521–6. 38. Brizel DM, Dodge RK, Clough RW et al. Oxygenation of head and neck cancer: changes during radiotherapy and impact on treatment outcome. Radiother Oncol 1999; 53: 113–17. 39. Nordsmark M, Overgaard J. Tumor hypoxia is independent of hemoglobin and prognostic for loco-regional tumor control after primary radiotherapy in advanced head and neck cancer. Acta Oncol 2004; 43: 396–406. 40. Wouters BG, Weppler SA, Koritzinsky M et al. Hypoxia as a target for combined modality treatments. Eur J Cancer 2002; 38: 240–57. 41. Martin L, Lartigau E, Weeger P et al. Changes in the oxygenation of head and neck tumors during carbogen breathing. Radiother Oncol 1993; 27: 123–30. 42. Becker A, Kuhnt T, Liedtke H et al. Oxygenation measurements in head and neck cancers during hyperbaric oxygenation. Strahlenther Onkol 2002; 178: 105–8. 43. Kaanders JH, Pop LA, Marres HA et al. Accelerated radiotherapy with carbogen and nicotinamide (ARCON) for laryngeal cancer. Radiother Oncol 1998; 48: 115–22. 44. Kaanders JH, Pop LA, Marres HA et al. ARCON: experience in 215 patients with advanced head-and-neck cancer. Int J Radiat Oncol Biol Phys 2002; 52: 769–78. 45. Hermans R, Lambin P, Van den Bogaert et al. Non-invasive tumor perfusion measurement by dynamic CT: preliminary results. Radiother Oncol 1997; 44: 159–62. 46. Hermans R., Lambin P, Van der Goten A et al. Tumoral perfusion as measured by dynamic computed tomography in head and neck carcinoma. Radiother Oncol 1999; 53: 105–11. 47. Hermans R, Meijerink M, Van den Bogaert W et al. Tumor perfusion rate determined non-invasively by dynamic computed tomography predicts outcome in head and neck cancer after radiotherapy. Int J Radiat Oncol Biol Phys 2003; 57: 1351–6. 48. Mullani NA, Gould KL. First-pass measurements of regional blood flow with external detectors. J Nucl Med 1983; 24: 577–81. 49. Blomley MJK, Coulden R, Bufkin C et al. Contrast bolus dynamic computed tomography for the measurement of solid organ perfusion. Invest Radiol 1993; 28: S72–7.

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50. Blomley MJK, Coulden R, Dawson P et al. Liver perfusion studied with ultrafast CT. J Comput Assist Tomogr 1995; 19: 424–33. 51. Brix G, Bahner ML, Hoffmann U et al. Regional blood flow, capillary permeability, and compartmental volumes: measurement with dynamic CT – initial experience. Radiology 1999; 210: 269–76. 52. Jaschke W, Gould RG, Assimakopoulos PA et al. Flow measurements with a high-speed computed tomography scanner. Med Phys 1987; 14: 238–43. 53. Gandhi D, Hoeffner EG, Carlos RC et al. Computed tomography perfusion of squamous cell carcinoma of the upper aerodigestive tract. Initial results. J Comput Assist Tomogr 2003; 27:687–93. 54. Lartigau E, Le Ridant AM, Lambin P et al. Oxygenation of head and neck tumors. Cancer 1993; 71:2319–25. 55. Hermans R, Van den Bogaert W, Rijnders A et al. Predicting the local outcome of glottic cancer treated by definitive radiation therapy: value of computed tomography determined tumor parameters. Radiother Oncol 1999; 50: 39–46. 56. Mancuso AA, Mukherji SK, Schmalfuss I et al. Preradiotherapy computed tomography as a predictor of local control in supraglottic carcinoma. J Clin Oncol 1999; 17: 631–7. 57. Pameijer FA, Mancuso AA, Mendenhall WM et al. Evaluation of pretreatment computed tomography as a predictor of local control in T1/T2 pyriform sinus carcinoma treated with definitive radiotherapy. Head Neck 1998; 20: 159–68. 58. Chua DT, Sham JS, Kwong DL et al. Volumetric analysis of tumor extent in nasopharyngeal carcinoma and correlation with treatment outcome. Int J Radiat Oncology Biol Phys 1997; 39: 711–19. 59. Kim JH, Lee JK. Prognostic value of tumor volume in nasopharyngeal carcinoma. Yonsei Med J 2005; 30: 221–7. 60. Chao KSC, Bosch WR, Mutic S et al. A novel approach to overcome hypoxic tumor resistance: Cu-ATSM-guided intensity-modulated radiation therapy. Int J Radiat Oncol Biol Phys 2001; 49: 1171–82. 61. Römer W, Muresan L, Adamietz B et al. Biological characterization and response monitoring in head and neck cancer therapy using multislice perfusion CT. Eur Radiol 2002; 12 (Suppl 1): S193 (abstr). 62. Million RR, Cassisi NJ, Mancuso AA et al. Management of the neck for squamous cell carcinoma. In: Million RR, Cassisi NJ, eds. Management of Head and Neck Cancer: a Multidisciplinary Approach. Philadelphia, PA: JB Lippincott Company, 1994: 125.

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7 CT perfusion applications in lung cancer Kwun M Fong, Rayleen V Bowman, Ian A Yang, and Kenneth A Miles

INTRODUCTION Lung cancer is the most devastating, yet preventable, cause of premature cancer mortality and morbidity in the Western world. Smoking prevention will make the most impact on lung cancer. Nonetheless, improvements to the medical and surgical management of lung cancer have gradually helped to refine the diagnosis, staging, and optimal treatments, including multimodality therapy. Amongst the hurdles that remain are the lack of effective secondary prevention, tools for effective early diagnosis, and cost-effective new treatments aimed at molecular targets. Recently, it has become possible to perform dynamic contrastenhanced computed tomography (CT) for lung nodules and suspected lung cancers with relatively simple modifications to standard protocols. The intent is to capitalize on the vascular and perfusion differences that exist between benign and malignant nodules. There are now substantial data to support the notion that dynamic contrast-enhanced CT provides quantitative information about blood flow patterns of solitary pulmonary nodules (SPNs), and is potentially a useful diagnostic between method for differentiating between SPNs of differing pathologies. Indeed, it has already been utilized in a large-scale CT screening study for lung cancer. Nonetheless, to be used in widespread clinical practice, there needs to be standardization of an accepted protocol, and standardized measurements used to ensure generalizability. Future technical modifications are likely to make this technique even more attractive, especially when combined with independent functional imaging modalities, whilst taking advantage of the morphological structural and spatial detail afforded by modern multidetector CT scanners.

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PATHOLOGY OF LUNG CANCER There are a large number of malignant tumors that affect the lungs, ranging from metastatic malignancies to the most common type, bronchogenic carcinoma. Bronchogenic cancers can also metastasize to the lungs, as well as other sites; but the lung is also a common destination for the metastatic spread of a wide variety of other human malignancies. Bronchogenic carcinoma (lung cancer) is the highest ranking cause of cancer-associated mortality in the world. Historically more prevalent in men, bronchogenic carcinoma is currently causing higher mortality among women living in several Western countries than is breast or any other cancer. Non-small-cell lung cancer accounts for approximately 80–85% of primary lung cancer, and small-cell cancer accounts for most of the remainder. The two types differ in both biological characteristics and clinical behavior.

Presentation Lung cancers can present in a large number of ways. A frequently encountered clinical situation is the presentation of a patient with or without symptoms who is found to have a pulmonary nodule on chest radiography or on thoracic CT undertaken for another reason, such as diagnosis of suspected pulmonary embolism. For this common clinical scenario where lung cancer is a diagnostic possibility, the challenge is first to ascertain the presence or absence of malignancy. If malignancy is confirmed, the clinician has to determine the origin of the cancer, and finally the extent or stage of disease so that therapy may be appropriately individualized. Therefore, tools to help decide the likelihood of malignancy and the subsequent need for invasive investigations for pulmonary lesions are essential for the clinician who manages patients with suspected lung cancer.

Increasing detection of pulmonary nodules in clinical practice The increasingly frequent use of sensitive techniques such as CT scanning has led to more recognition of pulmonary nodules. Low-dose helical CT scanning is several times more sensitive than conventional chest radiography for detecting pulmonary nodules, and could come into routine practice as a screening tool if current randomized trial data show it to be effective in reducing lung cancer mortality. The potential impact of these sensitive imaging tools on clinical practice is illustrated

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by large screening studies in some parts of the world, which show noncalcified nodules in the majority of screenees.1 Therefore, if low-dose CT comes into routine use, there will be an exponential increase in the number of nodules that require clinical evaluation. As most identified pulmonary nodules (defined as an intraparenchymal lung lesion less than 3 cm in diameter and not associated with atelectasis or lymphadenopthy2 turn out not to be malignant, there is a pressing need for strategies to efficiently triage pulmonary nodules so that those requiring treatment can be identified, while avoiding the expense, risk, and inconvenience of unnecessary invasive investigations.

CONTRAST-ENHANCED CT CT measurement of nodule enhancement with iodinated contrast media is an example of so-called dynamic or functional imaging. The physiological basis is the difference in vascularity and vasculature of neoplastic compared to benign nodules.3 Investigators have exploited these differences with a number of distinct modalities to show that lung cancers enhance more than benign lung lesions. Apart from contrast-enhanced CT, differences in tumor vascularity have been demonstrated with angiography,4 contrast-enhanced tomography,5 fluorodeoxyglucosepositron emission tomography (FDG-PET),6 Doppler ultrasound,7 and magnetic resonance imaging.8

TECHNICAL CONSIDERATIONS: MEASUREMENT OF CONTRAST ENHANCEMENT AND PERFUSION With the advent of spiral CT, there has been a rapid profusion of research protocols aimed at distinguishing differences in vascularity between malignant and benign tumors. In addition, there have also been a variety of measurements utilized for quantifying the results from contrast-enhanced nodule scanning. Simple measurements of tissue attenuation9 or enhancement10 can be obtained, as can absolute measures of tissue perfusion.11 Peak enhancement is a measure of the maximum increase in density after contrast administration, and is determined by not only tumor perfusion but also blood volume and capillary permeability. The degree to which peak enhancement approximates perfusion is dependent upon the transit time of contrast material through the tumor circulation: the longer is the transit time, the closer is the dependence of peak enhancement on perfusion.

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Transit time is in turn affected by tissue blood volume and vascular permeability. All three parameters, perfusion, blood volume, and permeability, tend to be increased in malignancy, resulting in greater enhancement. The use of peak enhancement measures can be extended to include an evaluation of the washout of contrast material by incorporating an additional acquisition at 15 minutes.12 A typical protocol for measuring pulmonary nodule enhancement from a series of spiral acquisitions is illustrated in Box 7.1.13 Enhancement can be determined by subtracting the baseline density value in a region of interest (ROI) constructed within the nodule from the equivalent value in the ROI on an image obtained at peak contrast enhancement (Figure 7.1a). A number of factors must be considered, to enable such techniques to produce consistent results. First, the degree of enhancement for a given dose of iodine is dependent upon weight, and thus

Box 7.1 Technique for quantifying contrast enhancement within lung nodules: repeated spiral (helical) acquisitions (based on reference 13) Contraindications: Usual contraindications for contrast-enhanced CT. System calibration (preferable but not essential): 1. Position a phantom containing tubes of contrast material (300–370 mg/ml) diluted in normal saline at ratios of 1:20, 1:35, 1:50, 1:100, and 1:200 so that the tubes are aligned along the central z-axis of the CT system. 2. Acquire and reconstruct images as for the patient protocols below. 3. On reconstructed images of the phantom, place regions of interest (ROIs) over each tube containing contrast material. 4. Plot attenuation measurements from each tube against the corresponding iodine concentration and determine the iodine calibration factor from the gradient of a linear least-squares fit of the five points. Image acquisition: 1. Pre-contrast-enhancement images to locate the nodule: 3 mm collimation, pitch 1:1, 120 kVp, 280 mA. 2. Contrast-enhanced images; z-axis cluster of at least 15 mm acquired at 1, 2, 3, and 4 minutes following 420 mg iodine/kg injected at 600 mg/minute (e.g. 300 mg/ml at 2 ml/s); 3 mm collimation, pitch 1:1, 120 kVp, 280 mA. Suspended shallow inspiration. Image analysis: 1. Reconstruct images at 2 mm intervals. 2. Place a circular or oval ROI within the nodule as displayed on mediastinal windows, ROI size approximately 70% of nodule long and short axis diameters. 3. Determine enhancement by subtracing baseline nodule attenuation from attenuation values on subsequent enhanced images. 4. If system calibration performed, use calibration factor to convert enhancement to mg iodine. Interpretation: Enhancement greater than 15 HU (approximately 0.55 mg iodine/ml) indicates possible malignancy.

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a

b

115

c

Enhancement = 30 HU

Figure 7.1 Assessment of likely malignancy within a solitary pulmonary nodule. (a) Measurement of contrast enhancement. (b) Parametric image of contrast enhancement image to evaluate irregularity of enhancement. (c) Corresponding fluorodeoxyglucose-positron emission tomography (FDG-PET) image.

contrast material should be administered on a dose-by-weight basis. Alternatively, enhancement values should be corrected for body weight. Second, most studies adopting this methodology fail to consider the necessity for calibrating the CT system for sensitivity to iodine. It is well recognized that reducing the tube voltage will increase the sensitivity of CT systems to iodine. However, it is less well appreciated that, despite routine quality control measures, significant differences in iodine sensitivity can exist between different CT systems, and that the sensitivity of a single CT system can change significantly with time.14 Hence, 15 HU of enhancement on one CT system could be equivalent to 17.6 HU on the same system 45 weeks later, or to 18.7 HU on another system.14 Regular calibration of CT systems using a purpose-built phantom (Figure 7.2) would overcome this problem of variability, and enable contrast enhancement to be more accurately expressed in terms of iodine concentration. Even with these corrections, enhancement measures remain dependent upon cardiac output.11 Parametric images of peak enhancement can also be generated to allow an assessment of enhancement heterogeneity (Figure 7.1b). Malignant nodules are more likely to exhibit irregular enhancement. Similarly, it is also possible to calibrate such images so that they are quantified as the standardized enhancement value (SEV) or standardized perfusion value (SPV) (Box 7.2). For the SEV, enhancement values are normalized to the enhancement expected if contrast is evenly diluted within the patient volume.11 Similarly, the SPV relates tissue perfusion to average whole-body perfusion, thereby allowing comparison of various perfusion measurements.11 The derivation of these parameters is analogous to the standardized uptake value (SUV) used in FDG-PET, and allows

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a T 1

5

2

4 3

b DATA SPECTRUM CORPORATION

V

V

V

Figure 7.2 Phantom for calibrating computed tomography (CT) systems for sensitivity to iodine. (a) End plan-view and (b) overall view. Contrast material at various dilutions is introduced into cylinders of diameter 25 mm and length 50 mm located at positions 1–5 as follows: (1) 1:50, (2) 1:35, (3) 1:20, (4) 1:200, (5) 1:100. Three small venting plugs (V) allow release of air when filling the phantom with water. (Reproduced with permission from reference 14)

more direct comparison of the results of the two modalities.11 Generation of such images is aided by the selection of appropriate thresholds to subtract normal lung tissue and blood vessels (see Chapter 4). By using volumetric acquisitions, it is possible to correct for respiratory motion by selecting corresponding images from the baseline and peak enhancement datasets. Alternatively, perfusion of the nodule can be calculated from the density–time curve of the tissue of interest adjusted for arterial input obtained from the density–time curve within the thoracic aorta. Perfusion measurements are less affected by contrast dose, patient weight, and cardiac output, but require dedicated image acquisition and more complex calculations.11 However, image misregistration due to respiratory motion and beam hardening artifacts from high-density contrast

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Box 7.2 Protocol for generating consistent CT images of peak enhancement within lung masses Aim: To generate images of peak enhancement within lung nodules whilst minimizing inconsistencies due to motion and/or segmentation of the tumor. The resulting images will allow assessment of regional variations in tumor peak enhancement. 1. Create a region of interest (ROI) encompassing the pulmonary nodule displayed with mediastinal windows. 2. Use image thresholds of −50 HU to 100 HU to eliminate from analysis any pixels containing air or calcium. 3. Exclude large blood vessels from the ROI by eliminating pixels that are enhanced by more than 100 HU following contrast material. 4. Display the changes in X-ray attenuation within the ROI following administration of contrast material as a time-attenuation curve and identify the time of maximal tumor enhancement. Only the image at this time point and the baseline image are retained for further analysis. 5. If several images have been acquired at each time, compensate for respiratory motion by selecting from each data set the images that correspond most closely in anatomical position. 6. Generate parametric image displaying peak tumor enhancement (and/or standardized perfusion value) by subtracting the baseline image from the image in which the attenuation within tumor ROI was maximal.

material in the venous system can significantly impair perfusion images of lung lesions.11 Furthermore, lung tumors may be supplied by either the pulmonary or the bronchial circulation. Thus, when using deconvolution analysis, the choice of blood vessel for the input function is an important consideration when undertaking CT perfusion of lung lesions, because significant errors can occur if an inappropriate input vessel is selected.

EVALUATION OF PULMONARY NODULES Based on the differences in blood flow between benign and malignant nodules, contrast-enhanced CT has been proposed as a useful tool for evaluating indeterminate pulmonary nodules.15 Prior to this, Littleton et al.5 had demonstrated the potential for using contrast enhancements to aid in the diagnosis of lung cancer in 45 patients with pulmonary masses using conventional tri-spiral tomograms. Their results suggested that contrast enhancement of lung masses could be measured on sectional images, and set the scene for exploiting the power of CT imaging.

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Improving on this time-consuming densitometric measurement of film tomograms, Swensen and colleagues initially reported a preliminary study of a contrast-enhanced thin-collimation CT technique in evaluating solitary pulmonary nodules in 52 patients.16 All malignant nodules had enhanced by 20 HU or more within the first 2 minutes after contrast injection, compared with only one benign nodule. Since then, multiple subsequent investigations of the technique in evaluating pulmonary nodules have been reported (Table 7.1). Overall, more malignant tumors enhance following contrast material (median 46.5 HU, range 11–110 HU) compared with granulomas and benign lesions.10 Setting 20 HU as the threshold for a positive test, this prospective study reported by Swensen et al. found that the sensitivity was 98% and the specificity 73%. From another perspective, enhancement of less than 15 HU in a nodule appeared to be strongly predictive of benignity. In a subset of 24 nodules, CT contrast enhancement was found to correlate with the degree of central immunohistochemical staining for an antibody to factor VIII-associated antigen. In a different population of 32 solitary pulmonary nodules, including lung cancer, tuberculomas, and hamartomas, Yamashita et al. found that all lung cancers, but only one benign lesion, a hamartoma, showed

Table 7.1 Summary of studies using contrast enhancement to evaluate solitary pulmonary nodules

Study Swensen et al.13 Yamashita et al.9 Zhang and Kono17

No. of patients

Criteria > 15 HU 20–60 HU > 20 HU > 0.2 ml/min SPV ≥ 1.6

Petkovska et al.18 Jeong et al.12

29 107

Multispiral Multispiral Single location Single location Multispiral Multispiral

Yi et al.19

119

Multispiral

42

Multispiral

Miles et al.11

Christensen et al.20

356 32 65

Technique

11

SPV, standardized perfusion value

> 15 HU ≥ 25 HU and washout 3–31 HU (> 25 HU only) ≥ 25 HU and washout 3–31 HU (> 25 HU only) > 15 HU

Sensitivity (%)

Specificity (%)

98 88 95

58 36 85

100

50

93 94

21 90

(100) 81

(48) 93

(95) 100

(60) 29

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CT contrast enhancement using a protocol that scanned before, and 30 seconds, 2 minutes, and 5 minutes after contrast material administration.9 The time–attenuation curve and the maximum attenuation of a nodule were considered to reflect the vascularity and blood pool of the nodule respectively. Overall, the time–attenuation curve for the 18 lung cancers showed gradual enhancement, whereas the maximum attenuation for these cancers ranged from 25 to 56 HU. Similar findings of peak enhancement after contrast CT in malignant (42 HU) and inflammatory benign (44 HU) nodules compared to noninflammatory nodules (13 HU) in 65 patients were reported by Zhang and Kono.17 These investigators also measured the peak height of time–attenuation curves and ratio of the peak height of the SPN to that of the aorta. SPN-to-aorta ratios in malignant and inflammatory SPNs were significantly higher than in benign SPNs. Perfusion was additionally calculated from the maximum gradient of the time–attenuation curve and the peak height of the aorta. Again, perfusion values in malignant and inflammatory SPNs were significantly higher than those of the benign SPNs. However, no statistically significant differences in the peak height and SPN-to-aorta ratio were found between malignant and inflammatory SPNs. To test the hypothesis that the absence of nodule enhancement, that is 15 HU or less, is predictive of benignity, a large multicenter study of lung nodule CT enhancement was performed by Swensen and his colleagues. Three hundred and fifty-six eligible nodules from an initial 550 patients from seven centres were studied for peak nodule enhancement, and time–attenuation curves.13 As expected, malignant lesions enhanced (median 38.1 HU, range 14.0–165.3 HU) significantly more than granulomas and benign lesions (median 10.0 HU, range −20.0–96.0 HU). In this study, using 15 HU as the threshold, sensitivity was again very high at 98% (167 of 171 malignant nodules) with a specificity of 58% (107 of 185 benign nodules), thus supporting the hypothesis that contrast enhancement of 15 HU or less predicts a benign lesion. Similar results have been obtained in two recent comparisons of nodule enhancement versus FDG-PET. Using 15 HU of enhancement as the diagnostic threshold, Christensen et al.20 reported a sensitivity and specificity for nodule enhancement of 100% and 29%, respectively, whereas Yi et al.19 reported a sensitivity of 95% and specificity of 60% when using net enhancement of greater than or equal to 25 HU.

CT enhancement and pathology of lung nodules In a separate radiologic–pathologic comparison of contrast-enhanced CT in small peripheral lung cancers, Yamashita et al. reported that

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malignant lung carcinomas had maximum attenuation (difference in CT numbers between unenhanced attenuation and maximum enhancement) of between 20 and 89 HU.21 They demonstrated that enhancement correlated with the number of small vessels, and the distribution of elastic fibers in the interstitium. A study of histologically confirmed lung cancers found no significant differences in peak enhancement or perfusion between small-cell lung cancer and non-small-cell lung cancer.22 Perfusion of larger tumors was lower than that of smaller tumors, as was the case for central tumors compared to peripheral tumors. On the other hand, peak enhancement of central and peripheral cancers did not differ significantly. By studying the enhancement characteristics of the cancer compared with the aorta, it was also concluded that some tumors were supplied by both pulmonary and/or bronchial vessels. More recently, Yi and colleagues reported the correlation of dynamic enhanced helical CT and vascular endothelial growth factor (VEGF) immunostaining and microvessel density.23 Using a sensitivity of 10 HU or more for designating malignant nodules, this protocol resulted in a very high sensitivity of 99% (69 of 70) for malignant nodules but a lower specificity of 54% (33 of 61 benign modules), from 131 patients with solitary pulmonary nodules. The protocol involved nine series of images obtained at 20-second intervals for 3 minutes after contrast medium injection. Peak enhancement but not net enhancement was found to positively correlate with microvessel counting using Weidner’s method, after CD31 staining and VEGF immunostaining with a polyclonal antibody.23 The potential impact of intratumoral necrosis on contrast enhancement has been studied by Yamashita et al.24 One of four peripheral lung cancers exhibiting non-cavitary necrosis within the surgically resected specimen was associated with poor enhancement, highlighting this pathological feature as a potential pitfall in the use of nodule enhancement for characterizing lung nodules.

Comparison with FDG-PET scans Several groups have sought to establish the relation between contrastenhanced CT and FDG-PET in lung tumors. In 40 consecutive lung cancers, Tateishi et al. found that contrast enhancement (as measured by peak attenuation) and relative flow (from dynamic contrast-enhanced CT) correlated with the standardized uptake value (SUV) of FDG-PET as well as intratumoral microvessel density revealed by immunoreactivity to a CD34 antibody.25 A study comparing FDG-PET SUV and SPV

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from contrast-enhanced CT in 18 non-small-cell lung cancers showed that the ratio of SUV to SPV and the metabolic–blood flow difference (SUV−SPV) were not altogether consistent, but appeared to vary according to tumor size and stage.26 The apparent uncoupling of tumor blood flow and metabolism in larger tumors can be postulated to reflect a possible metabolic adaptation of larger cancers which have outgrown their blood supply. Two direct comparisons of quantitative contrast-enhanced CT and PET have shown superior diagnostic accuracy for FDG-PET in the characterization of pulmonary nodules.19,20 However, using enhancement criteria alone, each study found the sensitivity, and hence negative predictive value, to be comparable for the two techniques. Thus, in view of the greater availability and lower cost of contrast-enhanced CT, it is possible to envisage a diagnostic strategy which incorporates nodule contrast-enhancement in the selection of patients for FDG-PET (Figure 7.3). Nodules which demonstrate low enhancement could be watched, whilst nodules that enhance would undergo FDG-PET. A decision tree analysis has outlined the potential cost-effectiveness of this approach.27 In the Australian setting, this strategy can be estimated to save $117 per patient compared to investigation with conventional CT with PET, and

Solitary pulmonary nodule

Thin section CT

Benign features

Indeterminate

Nodule enhanced CT

Low enhancement

2 year CT follow-up for stability

High enhancement

FDG PET

Negative

Positive

Biopsy/ resection

Figure 7.3 Strategy for the cost-effective investigation of pulmonary nodules with conventional CT, nodule-enhanced CT, and FDG-PET.

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would remain more cost-effective provided that the prior probability of malignancy is less than 80%.

POTENTIAL LIMITATIONS Variations in technique To date, there does not appear to be any consensus as to a standardized protocol, including the amount of contrast material and flow rate to be used, the frequency of data recording, the scanning time, and the scan interval. Each of these factors has the potential to affect the pattern and quantity of measurable enhancement.28

Enhancing benign lesions Evidence including that from a multicenter prospective study indicates that the absence of contrast enhancement predicts the benign etiology of a pulmonary nodule. In contrast, not all enhancing nodules are malignant. Some are inflammatory nodules, and others are intrapulmonary lymph nodes.29,30

Small nodules Most if not all studies report contrast enhancement in pulmonary nodules of at least 5 or 6 mm in diameter.3,16,23 There is limited knowledge of the value of this technique in very small lesions.

Tumoral heterogeneity Yamashita et al. noted that some lung cancers demonstrate inhomogeneous patterns of contrast enhancement at CT, some correlating to necrosis, fibrosis, or perhaps differentiation.21 This implies that necrotic cancers may not demonstrate the high levels of enhancement seen with non-necrotic tumors (Figure 7.4).24 Indeed some experts recommend that the use of contrast-enhanced CT be restricted to pulmonary nodules with diameters 2–2.5 cm or less, as they are less likely to have substantial necrosis, and thus be falsely negative.3,26 Furthermore, the clinical impact of smaller lesions is different, as they are more likely to be benign, and are generally more difficult to biopsy successfully either bronchoscopically or via a transthoracic percutaneous approach.

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a

b

123

50HU

0HU

Figure 7.4 Large non-small-cell lung cancer with low-level and heterogeneous enhancement implying necrosis. (a) Conventional contrast-enhanced CT; (b) maximum-enhancement image.

Potential technical artifacts Potential factors that may result in artifacts include beam hardening artifact in lesions localized directly to large central vessels, and motion artifact from longer breath-holds. For instance, in the large multicenter study, a minority of nodules were reported as technically inadequate because of respiratory misregistration.13

Radiation dose Many contrast CT scans are utilizing tube voltages of 80 kV in an attempt to reduce radiation exposure. This voltage also has the advantage of increasing the sensitivity to iodine-based contrast agents. However, for measures of nodule enhancement, new diagnostic thresholds will need to be established for reduced tube voltages. It has been estimated using International Commission on Radiation Protection (ICRP) recommendations and the CT-Expo program for CT dose evaluation that there is an effective dose of about 1.3 mSv.22

FUTURE DEVELOPMENTS Refinements Heterogeneous enhancement is more commonly a feature of malignant nodules.18 Although heterogeneity can be assessed visually, this image

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feature is also amenable to quantification through computer analysis. With the exponential increase in computing power, such future refinements are likely to be rapidly applied to this technique by various investigators. Preliminary data indicate that computer-aided diagnosis (CAD) appears to be able to improve the determination of malignant from benign nodules based on quantitative features extracted from volumetric thin-section CT image data acquired before and after the injection of contrast medium.31 For instance, it is possible that visual semiquantitative and quantitative characterization of contrast enhancement patterns may potentially improve the discrimination between benign and malignant nodules.18 Another group has suggested that analyzing combined wash-in and washout contrast-enhanced characteristics with multidetector row CT can improve the specificity of contrast-enhanced CT from 48% to 90% with only a slight fall in sensitivity from 100% to 94%.12 Similar results were found when using the same criteria in a subsequent comparison with FDG-PET (sensitivity: 81%, specificity: 93%).19

Prognostication and treatment monitoring A meta-analysis has confirmed that tumor microvessel count is a poor prognostic factor for survival in surgically treated non-small-cell lung cancer.32 As contrast enhancement correlates with tumor microvessel count,22 it is possible that contrast enhancement may prove to be a useful preoperative prognostic factor. Indeed, Shim et al. have shown that higher enhancement in non-small-cell lung cancer is predictive of advanced nodal status (see Chapter 10).33 However, this hypothesis requires further testing in prospective clinical trials. Contrast CT has the potential to help predict and/or monitor the response to treatment (see Chapter 12). As proof of principle, Kiessling et al. demonstrated a reduction in tumor perfusion following chemotherapy.22 Whilst this may potentially help to distinguish between residual tumor and post-treatment scar, it remains to be tested and validated in appropriately designed and powered prospective studies. Choi et al. reported that the treatment of small-cell lung cancer also resulted in lower enhancement, but found that the pretreatment enhancement level could predict the likely response.34

Whole tumor quantitation Until recently, contrast-enhanced CT measurements have been performed with multidetector row CT limited to a single tumor level comprising

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contiguous transverse sections (maximum coverage of 4 cm using 64-detector row scanners).35 This means that spatial heterogeneity within the tumor vasculature may render CT perfusion measurements prone to potential misrepresentation. To overcome this, a novel and reproducible helical acquisition technique has recently been described, utilizing Patlak analysis, which allows the vascular status of the entire tumor to be analyzed.35 If further validated, this improvement has the potential to reliably assess whole tumors rather than arbitrary single tumor sections.

CONCLUSIONS Conventional CT imaging is now a routine diagnostic study for evaluating suspected lung cancers and pulmonary nodules, staging, and following lung cancers after treatment. There have been significant advances in the field of functional imaging including techniques such as FDG-PET scans, contrast-enhanced CT, and magnetic resonance imaging (MRI).8 Contrast-enhanced CT offers improved accuracy in the prediction of malignancy in lung nodules, particularly when combined with morphological analysis. There are variations to the technique, but in general the results are generally concordant between techniques. Furthermore, some concordance with alternative, independent imaging methods for demonstrating tumor blood flow has been demonstrated. Already, contrast-enhanced CT has been used in a large-scale, low-dose CT screening study.36 A study of repeated yearly spiral CT and selective use of PET in a large cohort of high-risk volunteers enrolled 1035 individuals aged 50 years or older who had smoked for 20 packyears or more. All subjects underwent annual low-dose CT, with or without PET, for 5 years. Lesions up to 5 mm were deemed non-suspicious, and low-dose CT was repeated after 12 months (year 2). Spiral thin-section CT limited to the area of interest and three-dimensional analysis were used for non-calcified lesions greater than 5 mm, with assessment of contrast enhancement in nodular lesions that had a density of more than 0 HU. Positively enhancing (threshold of > 30 HU), PET-positive, and non-calcified uncertain lesions of 20 mm or larger were recommended for biopsy. The development of standardized measures and terminology for enhancement and perfusion should allow cross-comparison between studies and, more important, across diagnostic centers. CT has some advantages compared to other techniques, including broad availability

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compared to MRI or PET, worldwide expertise and familiarity with CT, and relative simplicity and cost. Moreover, there is some evidence from decision tree analysis that contrast-enhanced CT may be a cost-effective approach for the evaluation of solitary pulmonary nodules.27 The challenge is to agree on a simple universally accepted protocol, to test this protocol in large-scale studies to confirm its discriminatory power for malignant and benign nodules, and to implement its use for the day to day evaluation of suspicious pulmonary nodules. In the future, there will be much interest in exploiting the pattern of contrast enhancement in combined PET–CT examinations, in addition to the accepted benefit delivered by CT, such as anatomic correlation and attenuation correction for PET.37 The potential of the technique is likely to be magnified in the future by advanced computer software and hardware developments. Moreover, it may be that dynamic contrast-enhanced CT will become a useful adaptation of conventional fusion PET–CT imaging for the assessment of lung nodules and lung cancers. There may also be a therapeutic use in the characterization of lung cancer vascularity to predict the response of tumors to the new vascular targeting and antiangiogenic agents that are reaching the stage of large clinical trials, especially if whole-tumor measurements can be simply obtained.

REFERENCES 1. Swensen SJ, Jett JR, Sloan JA et al. Screening for lung cancer with low-dose spiral computed tomography. Am J Respir Crit Care Med 2002; 165: 508–13. 2. Tuddenham WJ. Glossary of terms for thoracic radiology: recommendations of the Nomenclature Committee of the Fleischner Society. AJR Am J Roentgenol 1984; 143: 509–17. 3. Swensen SJ. Functional CT: lung nodule evaluation. Radiographics 2000; 20: 1178–81. 4. Viamonte M Jr. Angiographic evaluation of lung neoplasms. Radiol Clin North Am 1965; 3: 529–42. 5. Littleton JT, Durizch ML, Moeller G, Herbert DE. Pulmonary masses: contrast enhancement. Radiology 1990; 177: 861–71. 6. Patz EF Jr, Lowe VJ, Hoffman JM et al. Focal pulmonary abnormalities: evaluation with F-18 fluorodeoxyglucose PET scanning. Radiology 1993; 188: 487–90. 7. Yuan A, Chang DB, Yu CJ et al. Color Doppler sonography of benign and malignant pulmonary masses. AJR Am J Roentgenol 1994; 163: 545–9. 8. Guckel C, Schnabel K, Deimling M, Steinbrich W. Solitary pulmonary nodules: MR evaluation of enhancement patterns with contrast-enhanced dynamic snapshot gradient-echo imaging. Radiology 1996; 200: 681–6. 9. Yamashita K, Matsunobe S, Tsuda T et al. Solitary pulmonary nodule: preliminary study of evaluation with incremental dynamic CT. Radiology 1995; 194: 399–405.

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10. Swensen SJ, Brown LR, Colby TV, Weaver AL, Midthun DE. Lung nodule enhancement at CT: prospective findings. Radiology 1996; 201: 447–55. 11. Miles KA, Griffiths MR, Fuentes MA. Standardized perfusion value: universal CT contrast enhancement scale that correlates with FDG PET in lung nodules. Radiology 2001; 220: 548–53. 12. Jeong YJ, Lee KS, Jeong SY et al. Solitary pulmonary nodule: characterization with combined wash-in and washout features at dynamic multi-detector row CT. Radiology 2005; 237: 675–83. 13. Swensen SJ, Viggiano RW, Midthun DE et al. Lung nodule enhancement at CT: multicenter study. Radiology 2000; 214: 73–80. 14. Miles KA, Young H, Chica SL, Esser PD. Quantitative contrast-enhanced computed tomography: is there a need for system calibration? Eur Radiol 2007; 17: 919–26. 15. Erasmus JJ, Connolly JE, McAdams HP, Roggli VL. Solitary pulmonary nodules: Part I. Morphologic evaluation for differentiation of benign and malignant lesions. Radiographics 2000; 20: 43–58. 16. Swensen SJ, Morin RL, Schueler BA et al. Solitary pulmonary nodule: CT evaluation of enhancement with iodinated contrast material—a preliminary report. Radiology 1992; 182: 343–7. 17. Zhang M, Kono M. Solitary pulmonary nodules: evaluation of blood flow patterns with dynamic CT. Radiology 1997; 205: 471–8. 18. Petkovska I, Shah SK, McNitt-Gray MF et al. Pulmonary nodule characterization: a comparison of conventional with quantitative and visual semi-quantitative analyses using contrast enhancement maps. Eur J Radiol 2006; 59: 244–52. 19. Yi CA, Lee KS, Kim BT et al. Tissue characterization of solitary pulmonary nodule: comparative study between helical dynamic CT and integrated PET/CT. J Nucl Med 2006; 47: 443–50. 20. Christensen JA, Nathan MA, Mullan BP et al. Characterization of the solitary pulmonary nodule: 18F-FDG PET versus nodule-enhancement CT. AJR Am J Roentgenol 2006; 187: 1361–7. 21. Yamashita K, Matsunobe S, Takahashi R et al. Small peripheral lung carcinoma evaluated with incremental dynamic CT: radiologic-pathologic correlation. Radiology 1995; 196: 401–8. 22. Kiessling F, Boese J, Corvinus C et al. Perfusion CT in patients with advanced bronchial carcinomas: a novel chance for characterization and treatment monitoring? Eur Radiol 2004; 14: 1226–33. 23. Yi CA, Lee KS, Kim EA et al. Solitary pulmonary nodules: dynamic enhanced multi-detector row CT study and comparison with vascular endothelial growth factor and microvessel density. Radiology 2004; 233: 191–9. 24. Yamashita K, Matsunobe S, Tsuda T et al. Intratumoral necrosis of lung carcinoma: a potential diagnostic pitfall in incremental dynamic computed tomography analysis of solitary pulmonary nodules? J Thorac Imaging 1997; 12: 181–7. 25. Tateishi U, Nishihara H, Tsukamoto E et al. Lung tumors evaluated with FDGPET and dynamic CT: the relationship between vascular density and glucose metabolism. J Comput Assist Tomogr 2002; 26: 185–90. 26. Miles KA, Griffiths MR, Keith CJ. Blood flow-metabolic relationships are dependent on tumour size in non-small cell lung cancer: a study using quantitative contrast-enhanced computer tomography and positron emission tomography. Eur J Nucl Med Mol Imaging 2006; 33: 22–8.

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27. Comber LA, Keith CJ, Griffiths M, Miles KA. Solitary pulmonary nodules: impact of quantitative contrast-enhanced CT on the cost-effectiveness of FDG-PET. Clin Radiol 2003; 58: 706–11. 28. Marten K, Grabbe E. The challenge of the solitary pulmonary nodule: diagnostic assessment with multislice spiral CT. Clin Imaging 2003; 27: 156–61. 29. Diederich S, Theegarten D, Stamatis G, Luthen R. Solitary pulmonary nodule with growth and contrast enhancement at CT: inflammatory pseudotumour as an unusual benign cause. Br J Radiol 2006; 79: 76–8. 30. Matsuki M, Noma S, Kuroda Y et al. Thin-section CT features of intrapulmonary lymph nodes. J Comput Assist Tomogr 2001; 25: 753–6. 31. Shah SK, McNitt-Gray MF, Rogers SR et al. Computer aided characterization of the solitary pulmonary nodule using volumetric and contrast enhancement features. Acad Radiol 2005; 12: 1310–19. 32. Meert AP, Paesmans M, Martin B et al. The role of microvessel density on the survival of patients with lung cancer: a systematic review of the literature with meta-analysis. Br J Cancer 2002; 87: 694–701. 33. Shim SS, Lee KS, Chung MJ et al. Do hemodynamic studies of stage T1 lung cancer enable the prediction of hilar or mediastinal nodal metastasis? AJR Am J Roentgenol 2006; 186: 981–8. 34. Choi J-B, Park C-K, Park DW et al. Does contrast enhancement on CT suggest tumor response for chemotherapy in small cell carcinoma of the lung? J Comput Assist Tomogr 2002; 26: 797–800. 35. Ng QS, Goh V, Fichte H et al. Lung cancer perfusion at multi-detector row CT: reproducibility of whole tumor quantitative measurements. Radiology 2006; 239: 547–53. 36. Pastorino U, Bellomi M, Landoni C et al. Early lung-cancer detection with spiral CT and positron emission tomography in heavy smokers: 2-year results. Lancet 2003; 362: 593–7. 37. Antoch G, Stattaus J, Nemat AT et al. Non-small cell lung cancer: dual-modality PET/CT in preoperative staging. Radiology 2003; 229: 526–33.

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8 Tumors of the gastro-intestinal tract Part A: Rectal cancer Massimo Bellomi and Giuseppe Petralia

BACKGROUND Although screening programs and recent advances in rectal cancer treatment such as total mesorectal excision (TME) and neoadjuvant chemoradiation therapy, have improved survival both in Europe and in the United States,1 the American Cancer Society estimates that there will be about 40 340 new cases of rectal cancer in 2005 in the United States,2 and only 50% of these patients will be alive in 2010.3 Innovative treatments are required to improve these outcomes. Some researchers have selected angiogenesis as a promising target for novel therapies4–6 for rectal cancer, since high values of tumor microvessel density (MVD) have been correlated with poor outcome,7,8 and high vascular endothelial growth factor (VEGF) expression in this malignancy has been associated with disease progression and poor survival.9,10 An in-vivo marker of rectal cancer angiogenesis is, then, required for therapeutic monitoring of new antiangiogenic trials, since morphologic imaging, based on size criteria, may not be suitable for monitoring the effects of antiangiogenesis drugs.11 Furthermore, angiogenesis assessment may be extremely useful for diagnosis, risk stratification, and conventional chemo- and radiation therapy monitoring in rectal cancer patients. Computed Tomography Perfusion imaging (CTP) has shown potential as a functional imaging tool for angiogenesis assessment.12–15 It is an easy examination, which can be performed with conventional scanners and

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conventional contrast medium using commercially available software, and it has shown encouraging results in rectal cancer assessment.16–18

TECHNIQUE CTP of rectal cancer is a potentially time-consuming examination if standardized protocols for patient preparation, image acquisition, and contrast medium administration are not observed. In the authors’ experience, the estimated time for CTP of the rectum, from patient preparation to discharge, is 12–15 minutes, if protocols are strictly observed.

Patient preparation Adequate bowel preparation is required for accurate rectal cancer imaging: 24 h before the CTP examination a diet with low amounts of fiber is observed and a cleansing enema is performed, combined with oral laxative administration. No oral contrast is administered to the patient. Once on the computed tomography (CT) bed, a 24-Fr Foley catheter is positioned as low as possible in the rectum, positioning the inflated balloon against the inner margin of the anal sphincter, and a 2-liter water enema is administered for homogeneous, reproducible and persistent rectal wall distention and visualization. Non-contrast-enhanced CT of the pelvis (2.5 mm slice thickness) is performed to locate the rectal cancer. One milliliter of hyoscin butylbromide is administered immediately before contrast medium injection and dynamic scanning, in order to avoid movement artifacts due to bowel peristalsis.

Image acquisition protocol A comprehensive CTP study, including both first-pass and delayed imaging, may be performed for studying rectal cancer, since the rectum is not affected by respiratory or other relevant motions. The matrix detector coverage, using a 16-slice GE CT (LightSpeed®; General Electric Medical Systems, Milwaukee, WI, USA), is 20 mm, and in cine mode it can be used to obtain 4 × 5-mm or 2 × 10-mm slices. Slices are selected to include the maximum visible tumor area; this is the most critical step of the procedure, since the only parameter for this decision is the thickness of the rectal lesion. The cine-scan is generally targeted at the thickest section of the tumor, although the highest blood flow in the tumoral bed is expected to be peripheral, while poor blood supply, hypoxia, and apoptosis more frequently occur at the center of the tumor. The areas with

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the highest blood supply cannot be chosen prior to performing CTP, and sampling is one of the main factors differentiating CTP from the pathological evaluation of tumor vascularization, since the pathologist selects the area with the highest vascularization to evaluate MVD. First-pass imaging requires high-frequency sequences (at least one scan/s) up to 50–60 s after initiation of contrast medium injection, when the contrast medium is predominantly intravascular. Reliable assessment of microvessel blood volume (BV) and accurate determination of functional parameters such as blood flow (BF) and mean transit time (MTT) can be obtained from the first-pass imaging. Delayed imaging of rectal cancer, to evaluate contrast medium leakage into extravascular space, requires scanning up to 2–10 min, at a lower frequency than first-pass imaging; these series allow microvessel permeability to be computed with reliable and reproducible accuracy.19–21 The correct scanning protocol, including both first-pass and delayed imaging, may be designed as follows: 8–10 s scanning delay from the start of contrast medium intravenous injection (contrast medium is still in the pulmonary circuit, and the first 10 seconds are not relevant for tumor perfusion), followed by continuous scanning at high time resolution (1 s/scan) for 45–50 s and at low time resolution (10–15 s/scan) up to 3–4 minutes. For dose reduction, 100 kVp and 200–220 mAs should be used; however, for oncological applications the dose might not be relevant, since many rectal cancer patients undergo radiation therapy. High spatial resolution with thin slices is not required for perfusion imaging; on the contrary, thick slices (5–10 mm) are preferred since they reduce image noise, which affects perfusion measurements. The acquisition protocol for a 16-slice scanner should include two adjacent 10-mm slices or four 5-mm slices. Spiral acquisitions increase volume coverage, but will produce unacceptable time resolution for the perfusion imaging of rectal cancer. The image acquisition protocol is summarized in Table 8.1.

Contrast medium administration The great advantage of CTP imaging is the use of conventional contrast medium, the same as in routine clinical activity, with concentrations ranging from 300 to 370 mg/ml; the higher is the concentration, the better is the tissue enhancement achieved.21 Similarly, the higher is the injection rate, the better is the tissue enhancement and, hence, are the perfusion measurements. A bolus of contrast medium ranging from 40 to 70 ml21 at high flow rate (4–7 ml/s) is required for accurate first-pass measurements. Contrast medium is administered via an 18–20-gauge cannula in the antecubital vein.

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Table 8.1

Aquisition protocol 4 × 5 mm or 2 × 10 mm 100 200–220 Delay: 8–10 s First pass: 45–50 s (one scan/s) Delayed phase: 120–180 s (1 scan/10–15 s) 300–370 mg/ml 40–70 ml 4-7 ml/s 18–20-gauge in the antecubital vein

Slice thickness kVp mAs Dynamic scans

Contrast medium

DATA AND IMAGE ANALYSIS The rationale for CT perfusion imaging is the repeated CT scanning of the same volume, in order to obtain CT attenuation (HU, Hounsfield units) curves over time (Figure 8.1). Several kinetic models have been applied a

219 5 200 180

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Figure 8.1 cont’d rectal cancer and normal rectal wall (b), muscle and lymph node (c). They reflect CT attenuation over time.

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by different authors to the time–density curves obtained to obtain perfusion measurements; compartmental analysis and deconvolution are the most widely used for commercial application to body tumors. Both have been largely validated22,23 on body tumors, and can be used for rectal cancer CT perfusion imaging without any particular disadvantage, since rectal anatomy is favorable, not influenced by respiratory or other relevant motions (after hyoscin butylbromide administration). Several dedicated software programs have been produced by different vendors for data and image processing. The first step in perfusion analysis is the accurate selection of arterial input. Small arteries will produce partial-volume effects; stenosis and circulation abnormalities will also affect data. For correct arterial input placement in rectal cancer perfusion imaging, the external iliac artery is recommended; the contralateral artery may serve for partial-volume averaging.20,21 CT perfusion software then generates a functional map for each computed perfusion parameter, representing on a color scale the different values obtained in the different parts of the CT image. A region of interest (ROI) can be now placed on the rectal cancer and in other regions of the CT image, to compute the perfusion values relative to the ROI area; manually drawn ROIs are recommended for rectal cancer in order to accurately include tumor margins, which are expected to have elevated angiogenesis. Other ROIs can be placed on the normal rectal wall, for comparison with rectal cancer values, and in adjacent organs (Figure 8.2). ROIs placed on gluteus muscles, for example, may serve as controls in perfusion measurements during therapy; changes of rectal cancer perfusion parameters, expected to vary significantly after therapy,17 may be compared to changes of muscle perfusion parameters, which are expected to have constant perfusion values during therapy. ROIs placed in adjacent organs, such as the prostate, lymph nodes, bones, etc., may assess their involvement, with additional information for rectal cancer imaging.

CURRENT EXPERIENCE AND CLINICAL APPLICATION CT perfusion has already been used for rectal cancer assessment,16–18,24 with encouraging performances for diagnosis, risk stratification, and therapy monitoring. Tumor angiogenesis, which is the growth of new vessels from preexisting ones, results in an increased vascular bed, which may be reflected in increased values of blood volume measured by CTP. Angiogenesis also stimulates the opening of arteriovenous shunts, which have very

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e

Figure 8.2 A 63-year-old patient with T3N1 rectal cancer. Regions of interest (ROIs) are manually drawn on the original CT image (a): on rectal cancer (lesion), normal rectal wall (normal wall), left external iliac artery (artery), mesorectal pathologic lymph node (lymph node), and right gluteus muscle (muscle). Functional maps of blood flow (b), blood volume (c), mean transit time (d) and permeability surface area (e) are generated.

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low resistance to flow and may result in an increased blood flow at CTP, with a reduction of the mean transit time. Contrast medium extravasation into extravascular space is expected to be high in rectal cancer, due to the hyperpermeable nature of capillary endothelial membranes of newly developed tumor vessels. Significant differences have been reported between the perfusion parameters of rectal cancer and those of the normal rectal wall. A study of 15 patients17 showed significantly higher blood flow and lower mean transit time in non-mucinous rectal adenocarcinoma, compared with the values in the normal rectal wall; in the authors’ experience with a 25-patient cohort, BF, BV, and PS were significantly higher in rectal cancer, compared to the normal rectal wall.25 Patients with rectal cancer undergoing neoadjuvant chemoradiation therapy have different responses to treatment,26 but currently there are no clinically useful predictors of response based on standard pathological assessment and immunocytochemistry.27 In the authors’ experience, with the same 25-patient cohort blood flow and blood volume of rectal cancers before neoadjuvant chemoradiation therapy were significantly lower in non-responders than in responding patients ( p = 0.03 and p = 0.003, respectively); rectal cancers with low blood flow and blood volume may have less effective chemotherapeutic drug delivery and lower oxygenation, with a related lower radiosensitivity, and thus a poor response, as compared to those with high blood flow and blood volume. Other authors17 have related high blood flow and short mean transit time, which may reflect high intrinsic angiogenic activity in the tumor or a secondary response to tissue hypoxia,28,29 to poor response. However, CTP of rectal cancer shows potential for the prediction of response to therapy; if further studies confirm these preliminary results, CTP will play an important role in rectal cancer risk stratification, for the best treatment selection. CTP of rectal cancer may also play a role in therapy monitoring, with a major impact on overall patient management, since it may solve problems related to morphologic imaging (Figures 8.3 and 8.4). The assessment of rectal cancer response to chemo- or radiation therapy with morphologic imaging is mainly based on size criteria, with a time lag between changes in tumor physiology induced by therapy, i.e. tumor metabolism and perfusion, and changes in tumor morphology detectable by imaging, which occur later. CTP detected significant changes in rectal cancer perfusion after neoadjuvant chemoradiation therapy,17 and is expected to detect early changes in rectal cancer perfusion during therapy. This may potentially influence the course and duration of therapy, which can be adjusted while treatment is still being administered,

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c Figure 8.3 A 56-year-old woman with T3N1 rectal cancer, before neoadjuvant chemoradiation therapy. Original CT image (a) and functional maps of blood flow (b) and blood volume (c).

customizing it to patient response. In addition, it might be possible to predict the outcome of therapy at an early stage, and potentially avoid unproductive and expensive treatments for some patients, or switch them to a different therapy. CTP of rectal cancer may play an even bigger role in the monitoring of novel antiangiogenic therapies, which are increasingly being adopted by different institutions for colorectal cancer treatment.4–6 Newly developed tumor vessels in the process of angiogenesis are too small to be resolved using current diagnostic imaging techniques, which are not reliable for monitoring changes in tumor vasculature following anti-angiogenic treatment. MVD assessment for such purpose is limited for serial therapy monitoring in human patients, since it is an invasive technique, requiring tissue sampling: furthermore, MVD counts may not reflect the efficacy of the treatment, because it does not independently measure vascular inhibition, but rather reflect many different changes occurring in tumor vasculature over time.29 On the contrary, changes of tumor microvasculature occurring in the process

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c Figure 8.4 The same woman as Figure 8.3, after neoadjuvant chemoradiation therapy. Original CT image demonstrates tumor size reduction (a). Functional maps did not show any significant difference in blood flow (b) and blood volume (c) between tumor and normal rectal wall: pathologic stage revealed absence of tumor (T0), after neoadjuvant treatment.

of angiogenesis can be reliably assessed by CTP, with a reported precision ranging from 14% to 24%, for BF, BV, MTT and PS.19 Therefore, CTP is suitable for monitoring changes occurring in tumor neovasculature, following anti-angiogenic therapy; particularly, it is extremely useful to detect early physiological changes occurring after therapy, avoiding the time-lag of conventional imaging, which produces only morphologic assessment of tumor volume changes following therapy, which will occur later.31,32 In a study by Willett et al.,33 CTP has been used in conjunction with other surrogate markers of angiogenesis, such as MVD, interstitial fluid pressure (IFP), 18-fluorodeoxyglucose (FDG) uptake, and systemic response (VEGF level in blood, number of circulating endothelial cells (CECs) and progenitor cells), for the assessment of changes in rectal cancer physiology induced by a single infusion of the VEGF-specific antibody bevacizumab. CTP showed significant changes after therapy, with results comparable to those using

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MVD, IFP, and assessment of the number of CECs and progenitor cells/stem cells. There is, therefore, great interest in the integration of CTP into trials of antiangiogenic therapy for rectal cancer. Furthermore, CTP presents additional advantages, compared to other functional imaging techniques. Radionuclide techniques are limited by poor spatial resolution and shortcomings in the assessment of anatomic details; positron emission tomography (PET) remains an expensive imaging modality because of the use of short-lived isotopes that require cyclotron production and radiochemistry facilities on-site.34 Magnetic resonance (MR) perfusion protocols may not be so easily understood by radiographers, radiologists, clinicians, and research scientists as those of CTP; furthermore, MR is more expensive and not so widely available as CT.

Part B: Other gastrointestinal tumors Kenneth A Miles

Cancer of the pancreas CT perfusion is well suited to the measurement of pancreatic perfusion. The fixed retroperitoneal position of the pancreas reduces the likelihood of motion artefacts and the high spatial resolution means that the pancreas can be reliably separated from the many adjacent vascular structures that can adversely affect other perfusion imaging techniques such as O-15 water positron emission tomography (PET).35 The first CT perfusion study of the pancreas was reported in 1995,36 describing a range of normal perfusion values between 125 and 166 ml/min/100 ml along with findings of reduced perfusion in diabetes and increased perfusion in an islet cell tumor (Figure 8.5). A later study by Tsushima et al37 reported reductions in normal pancreatic perfusion with increasing age with values as low as 55.4 ml/min/100 ml. Abe et al have reported CT perfusion values from 8 pancreatic adenocarcinomas and one gastrin secreting islet cell tumor.38 Adenocarcinomas demonstrated perfusion values lower than normal pancreas (median: 34.7 ml/min/100ml; range 22.1–50.0 ml/min/100 ml) whereas perfusion values for the islet cell tumor were greater than normal pancreas (196.2 ml/min/100ml) in keeping with the case previously reported by Miles et al.36 Perfusion values also correlated closely with those derived

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a

b

Figure 8.5 Conventional CT (a) and perfusion image (b) of a pancreatic islet cell tumor. Note that perfusion in the tumor periphery is greater than in the adjacent normal pancreas but perfusion is reduced in the center of the tumor. (Reproduced with permission from reference 37)

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using xenon CT39. Although using qualitative analysis only, Zhongqiu et al. correlated the degree enhancement of pancreatic tumors relative to surrounding normal tissue against histopathological features.40 Lower relative enhancement was found in moderately or poorly differentiated tumors, reflecting lower levels of perfusion. The role of CT perfusion in pancreatic imaging remains to be established. Potential applications include the monitoring of anti-cancer therapy and the identification of sub-populations of patients in whom anti-cancer therapy is more likely to be effective.39

Primary liver tumors CT perfusion can be used to evaluate changes in hepatic perfusion associated with hepatic cirrhosis.41 From time to time, such studies will also depict hepatocellular carcinomas, reflecting the increased incidence of this tumor in chronic liver disease (Figure 8.6). However, there have been no systematic studies of CT perfusion in primary liver tumors. Based on small series,37,42 primary liver tumors tend to exhibit perfusion values greater than normal hepatic arterial perfusion. The mean perfusion of two hepatocellular carcinomas was 65 ml/min/100 ml, considerably lower than the mean perfusion from eight cases of focal nodular hyperplasia at 136 ml/min/100 ml, highlighting a potential role for CT perfusion in lesion characterization.

a

Figure 8.6

Conventional CT (a),

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Figure 8.6 cont’d hepatic arterial (b), and hepatic portal (c) perfusion images in a patient with hepatic cirrhosis and a hepatocellular carcinoma in the right lobe. The tumor demonstrates high arterial perfusion. Portal perfusion is reduced in the remaining liver reflecting portal hypertension. (Reproduced with permission from reference 37)

REFERENCES 1. Birgisson H, Talback M, Gunnarsson U et al. Improved survival in cancer of the colon and rectum in Sweden. Eur J Surg Oncol 2005; 31: 845–53. 2. American Cancer Society. Cancer reference information. 2005. Available at: http://www.cancer.org/docroot/CRI/CRI_0.asp

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3. Tepper JE, O’Connell M, Niedzwiecki D et al. Adjuvant therapy in rectal cancer: analysis of stage, sex, and local control – final report of intergroup 0114. J Clin Oncol 2002; 20: 1744–50. 4. O’Connell MJ. Current status of adjuvant therapy for colorectal cancer. Oncology 2004; 18: 751–5. 5. Zhu AX, Willett CG. Combined modality treatment for rectal cancer. Semin Oncol 2005; 32: 103–12. 6. Hobday TJ. An overview of approaches to adjuvant therapy for colorectal cancer in the United States. Clin Colorectal Cancer 2005; 5: S11–18. 7. Takebayashi Y, Aklyama S, Yamada K et al. Angiogenesis as an unfavorable prognostic factor in human colorectal carcinoma. Cancer 1996; 78: 226–31. 8. Li C, Gardy R, Seon BK et al. Both high intratumoral microvessel density determined using CD105 antibody and elevated plasma levels of CD105 in colorectal cancer patients correlate with poor prognosis. Br J Cancer 2003; 88: 1424–31. 9. Cascinu S, Graziano F, Catalano V et al. An analysis of p53, BAX and vascular endothelial growth factor expression in node-positive rectal cancer. Relationships with tumour recurrence and event-free survival of patients treated with adjuvant chemoradiation. Br J Cancer 2002; 86: 744–9. 10. Theodoropoulos GE, Lazaris AC, Theodoropoulos VE et al. Hypoxia, angiogenesis and apoptosis markers in locally advanced rectal cancer. Int J Colorectal Dis 2006; 21: 248–57. 11. Li WW. Tumor angiogenesis: molecular pathology, therapeutic targeting, and imaging. Acad Radiol 2000; 7: 800–11. 12. Miles KA, Charnsangavej C, Lee FT et al. Application of CT in the investigation of angiogenesis in oncology. Acad Radiol 2000; 7: 840–50. 13. Tateishi U, Nishihara H, Watanabe S et al. Tumor angiogenesis and dynamic CT in lung adenocarcinoma: radiologic-pathologic correlation. J Comput Assist Tomogr 2001; 25: 23–7. 14. Tateishi U, Kusumoto M, Nishihara H et al. Contrast-enhanced dynamic computed tomography for the evaluation of tumor angiogenesis in patients with lung carcinoma. Cancer 2002; 95: 835–42. 15. Miles KA. Tumour angiogenesis and its relation to contrast enhancement on computed tomography: a review. Eur J Radiol 1999; 30: 198–205. 16. Harvey C, Dooher A, Morgan J et al. Imaging of tumour therapy responses by dynamic CT. Eur J Radiol 1999; 30: 221–6. 17. Sahani DV, Kalva SP, Hamberg LM et al. Assessing tumor perfusion and treatment response in rectal cancer with multisection CT: initial observations. Radiology 2005; 234: 785–92. 18. Goh V, Halligan S, Hugill JA, Gartner L, Bartram CI. Quantitative colorectal cancer perfusion measurement using dynamic contrast-enhanced multidetectorrow computed tomography: effect of acquisition time and implications for protocols. J Comput Assist Tomog 2005; 29: 59–63. 19. Purdie TG, Henderson E, Lee TY. Functional CT imaging of angiogenesis in rabbit VX2 soft tissue. Phys Med Biol 2001; 46: 3161–75. 20. Lee TY, Purdie TG, Stewart E. CT imaging of angiogenesis. Q J Nucl Med 2003; 47: 171–187. 21. Miles KA. Perfusion CT for the assessment of tumour vascularity: which protocol? Br J Radiol 2003; 76: S36–42.

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22. Miles KA, Hayball MP, Dixon AK. Functional images of hepatic perfusion obtained with dynamic CT. Radiology 1993; 188: 405–11. 23. Nabavi DG, Cenic A, Dool J et al. Quantitative assessment of cerebral hemodynamics using CT: stability, accuracy, and precision studies in dogs. J Comput Assist Tomogr 1999; 23: 506–15. 24. Harvey C, Morgan J, Blomley M et al. Tumor responses to radiation therapy: use of dynamic contrast material-enhanced CT to monitor functional and anatomical indices. Acad Radiol 2002; 9: S215–19. 25. Bellomi M, Petralia G, Sonzongni A, Zampino MG, Rocca A. CT perfusion for the monitoring of neo-adjuvant chemoradiation therapy in rectal carcinoma – initial experience. Radiology 2007; in press. 26. Rodel C, Martus P, Papadoupolos T et al. Prognostic significance of tumor regression after preoperative chemoradiotherapy for rectal cancer. J Clin Oncol 2005; 23: 8688–96. 27. Smith FM, Reynolds JV, Miller N et al. Pathological and molecular predictors of the response of rectal cancer to neoadjuvant radiochemotherapy. Eur J Surg Oncol 2006; 32: 55–64. 28. Leek RD, Landers RJ, Harris AL et al. Necrosis correlates with high vascular density and focal macrophage infiltration in invasive carcinoma of the breast. Br J Cancer 1999; 79: 991–5. 29. Wheeler RH, Ziessman HA, Medvec BR et al. Tumor blood flow and systemic shunting in patients receiving intra-arterial chemotherapy for head and neck cancer. Cancer Res 1986; 46: 4200–4. 30. Hlatky L, Hahnfeldt P, Folkman J. Clinical application of antiangiogenic therapy: microvessel density, what it does and doesn’t tell us. J Natl Cancer Inst 2002; 94: 883–93. 31. Miles KA, Leggett DAC, Kelley BB et al. In-vivo assessment of neovascularisation of liver metastases using perfusion CT. Br J Radiol 1998; 71: 276–81. 32. Mooteri S, Rubin D, Leurgans S et al. Tumor angiogenesis in primary and metastatic colorectal cancers. Dis Colon Rectum 1996; 39: 1073–80. 33. Willett CG, Boucher Y, di Tomaso E et al. Direct evidence that the VEGFspecific antibody bevacizumab has antivascular effects in human rectal cancer. Nat Med 2004; 10: 145–7. 34. Blankenberg FG, Eckelman WC, Strauss HW et al. Role of radionuclide imaging in trials of antiangiogenic therapy. Acad Radiol 2000; 7: 851–67. 35. Bacharach SL, Libutti SK, Carrasquillo JA. Measuring tumor blood flow with H(2)(15)O: practical considerations. Nucl Med Biol 2000; 27: 671–6. 36. Miles KA, Hayball MP, Dixon AK. Measurement of human pancreatic perfusion using dynamic computed tomography with perfusion imaging. Br J Radiol 1995; 68: 471–5. 37. Miles K, Blomley M. Applications of perfusion CT. In: Miles K, Blomley M, Dawson P, eds. Functional Computed Tomography. Oxford: ISIS Medical Media, 1997: 89–116. 38. Tsushima Y, Kusano S. Age-dependent decline in parenchymal perfusion in the normal human pancreas: measurement by computed tomography. Pancreas 1998; 17: 148–52. 39. Abe H, Murakami T, Kubota M et al. Quantitative tissue blood flow evaluation of pancreatic tumor: comparison between xenon CT technique and perfusion CT technique based on deconvolution analysis. Radiat Med 2005; 23: 364–370.

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40. Zhongqiu W, Guangming L, Jieshou L et al. The comparative study of tumor angiogenesis and CT enhancement in pancreatic carcinoma. Eur J Radiol 2004; 49: 274–80. 41. Van Beers BE, Leconte I, Materne R et al. Hepatic perfusion parameters in chronic liver disease: dynamic CT measurements correlated with disease severity. AJR Am J Roentgenol 2001; 176: 667–73. 42. Groell R, Kugler C, Aschauer M et al. Quantitative perfusion parameters of focal nodular hyperplasia and normal liver parenchyma as determined by electron beam tomography. 1995; 68: 1185–93.

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9 Tumors of the urogenital tract Elizabeth P Ives and Ethan J Halpern

For the 2007 calendar year, it is estimated that there will be 218 890 new prostate cases and 27050 prostate cancer deaths in the United States.1 The prostate is the leading site of new cancer diagnoses among US men and the second leading cause of cancer deaths. For an American male, there is a 1 in 6 lifetime probability of developing prostate cancer. Traditional diagnostic imaging methods have a limited role in the diagnosis, staging, and management of prostate cancer. The role of imaging in the prostate is limited by poor discrimination of prostate cancer from adjacent benign tissue, and by benign hypertrophic changes within the prostate. Technological innovations in imaging have the potential to improve the detection of prostate cancer with more accurate biopsy techniques, better local staging, more precise focusing of therapy, and closer post-treatment follow-up. Although computed tomography (CT) currently has a small role in the diagnosis and staging of prostate disease,2 numerous reports indicate the frequent use of CT by clinicians for evaluation of prostate cancer patients.3,4 Recent improvements in multidetector CT technology permit quantitative perfusion imaging of the prostate, and may presage a new role for CT in the detection, staging, and management of prostate cancer. Screening for prostate cancer is a controversial topic. Prostate specific antigen (PSA) serum levels and the digital rectal examination are the current accepted standard of care for prostate cancer screening. Transrectal ultrasound (TRUS) is not accepted as a screening method in an unselected population because of the poor sensitivity and specificity of sonography for prostate cancer. The accepted application for TRUS is to guide a biopsy procedure of the prostate in a patient with an abnormal serum PSA or digital rectal examination. Even if ultrasoundguided biopsy can improve detection, it provides little information about the extent of disease, because location and quantity of malignancy on biopsy often do not correlate with actual cancer volume.5

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The metabolic changes that allow neoplastic cells to develop into a solid mass have been investigated extensively. Hypoxia from tumor growth is thought to lead to angiogenesis factors which increase microvascularity and allow the growth of solid tumors.6 Prostate cancer demonstrates significantly higher microvessel density compared with benign tissue.7 As with other organ systems, the microvessel density of prostate cancer is useful in predicting the aggressiveness of the tumor. It has been suggested that quantitative microvessel density can serve as a prognostic marker for prostate cancer.8 Recent studies have exploited the alterations in perfusion in neoplastic prostate tissue to develop imaging techniques for the detection, localization, and management of prostate cancer. Perfusion imaging of the prostate has been evaluated primarily with ultrasound and magnetic resonance (MR) technologies. There is relatively little literature on the application of CT perfusion imaging to the prostate. Perfusion imaging with sonography is a key focus of research, since ultrasound is used routinely to guide biopsy. Conventional Doppler ultrasound has shown mixed results when using color and/or power Doppler to target malignant tissue for biopsy.9 Microbubble contrast agents have been proposed as a method to improve the detection of prostate cancer.10 Gas encapsulated microbubbles remain within the vasculature for several minutes, and may be used as a contrast agent during an ultrasound examination. Microbubbles show parenchymal organ enhancement, and can be used to analyze the prostate blood flow patterns.11 Several studies show this technique to increase the sensitivity of core needle biopsy in detecting cancer.12–14 Studies using time–intensity data from microbubble contrast sonography suggest that a quick time to peak enhancement may be predictive of prostate malignancy.15 MR imaging (MRI) may improve the local staging of prostate cancer,16 and may be useful for targeted biopsy in patients with suspected cancer and negative prior biopsy.17 In current clinical practice, however, MR does not reliably alter staging and therapy based on traditional clinical staging criteria. MR is primarily utilized to evaluate for disease extension beyond the prostate. Several studies have demonstrated that prostate cancer tissue enhances earlier than normal tissue when using a dynamic contrast-enhanced MRI sequence.18–20 The normal appearance of the prostate on CT is a smooth contoured structure at the base of the pelvis bordered by the pubic symphysis, bladder, and rectum. After the administration of intravenous iodinated contrast, the transition zone of the prostate enhances brightly in the inner gland (Figure 9.1). This enhancement can appear heterogeneous with benign hyperplasia (Figure 9.2). The outer gland enhances to

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Figure 9.1 Contrast-enhanced computed tomography (CT) of the prostate demonstrates enhancement of the transition zone in this normal prostate (arrows)

a

b

Figure 9.2 Patient with enlarged prostate secondary to benign prostatic hyperplasia. The pre-contrast image (a) demonstrates an enlarged gland. After contrast administration there is heterogeneous enhancement of the hypertrophied transition zone that occupies most of the prostate (b)

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a lesser degree and appears homogeneous in the normal prostate. Outer gland tissue can often be distinguished clearly from inner gland tissue based upon this enhancement pattern. Capsular enhancement appears as a ring around the prostate gland.21 Prostate cancer is generally not visible with CT imaging. Prostate cancer will not be detected on a CT scan performed without an intravenous contrast agent unless there is significant contour irregularity or bulging (Figure 9.3). In a minority of patients it is possible to detect prostate cancer based upon contrast enhancement (Figure 9.4). Based upon recent advances in helical CT technology, it is possible to obtain color-coded quantitative perfusion images corresponding to conventional contrast-enhanced CT images (Figure 9.5). The earliest published clinical study related to detection of prostate cancer with CT perfusion used a single-detector helical CT with arterial phase imaging to investigate 35 patients who had undergone TRUS biopsy.22 Twenty-five of these patients were diagnosed with prostate cancer based upon the biopsy. Ten patients were suspected of having prostate cancer based on examination and PSA but had a negative biopsy.

Figure 9.3 Contour abnormality along the right side of the prostate corresponds to an exophytic prostate cancer at the right mid-gland (arrow). Note that this area of cancer does not enhance to a greater degree than other unaffected parts of the prostate

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Figure 9.4 High-grade prostate cancer. Pre-contrast image (a) demonstrates a normal homogeneous appearance of the prostate. After contrast administration (b) there is focal enhancement of the cancer along the right side of the prostate (arrow)

a

b

Figure 9.5 Normal CT and quantitative perfusion images. Images through the mid-gland level of the prostate in a patient with a low-volume tumor in the apex of the gland. Both the contrast-enhanced CT (a) and the quantitative perfusion (b) demonstrate a normal, symmetric appearance

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Areas of the prostate that demonstrated asymmetric focal or diffuse contrast enhancement in the peripheral zone were considered positive by CT. The authors report identifying 22 of 25 patients (88%) with proven prostate cancer based upon increased arterial phase enhancement, presumably related to increased perfusion. In the same 25 patients, 102 peripheral zone cancer sites were found on biopsy. CT identified increased enhancement in 59/102 (58%) of the positive peripheral zone sites. The study noted that patients with higher PSA levels (> 10 ng/ml) and higher Gleason scores (> 5) had a greater degree of enhancement and size of nodular changes than other patients. While the authors concede that these results do not warrant routine use of CT for screening, they did propose the use of CT in selected patient populations such as those with rising PSA and negative biopsies, those with a rising PSA following abdominal perineal resection, and patients with routine CT pelvic examinations on which abnormal focal contrast enhancement in the peripheral zone is observed. CT might be used in these populations to guide a biopsy procedure of the prostate. Based upon the reported sensitivity of CT perfusion for prostate cancer using a single-detector helical scanner with qualitative assessment of CT perfusion, the authors of this chapter performed a CT study at Thomas Jefferson University with a 16-slice multidetector helical scanner using quantitative CT perfusion.23 Ten patients with biopsyproven prostate cancer underwent contrast-enhanced pelvic CT prior to radical prostatectomy. CT evaluation of the prostate was repeated at 10-second intervals during the first minute after administration of intravenous contrast. Six regions of interest (ROIs) were defined on the CT images in a sextant distribution pattern (base, mid, and apex along the right and left peripheral zones). Perfusion data were computed based upon the maximum slope of enhancement in the prostate ROI relative to the CT density of an arterial segment ROI.24 Perfusion data were correlated with whole-mount prostatectomy specimens, and were analyzed for correlation with the presence of malignancy, Gleason score, and percentage of tumor within the region on pathology. Notably, only one of ten subjects demonstrated visible peripheral gland enhancement (Figure 9.6). This patient demonstrated a Gleason 10 tumor with high volume and a significant correlation between perfusion and pathology results. Despite a lack of visible enhancement, one other subject had a significant correlation between perfusion and pathology. This subject also demonstrated aggressive (Gleason 8), high-volume (90% in at least one region) disease (Figure 9.7). The remaining eight subjects with prostate cancer showed neither visible enhancement nor significant correlation of perfusion and pathology (Figure 9.8).

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a

b

Figure 9.6 High-volume, Gleason 10 prostate cancer. The contrast-enhanced image (a) demonstrates bilateral capsular enhancement, but more substantial enhancement along the right mid-gland within the region of interest marked T1, as compared to the left mid-gland. Quantitative perfusion (b) demonstrates increased flow along the right mid-gland (arrow), corresponding to the site of tumor

Based upon the study performed at Thomas Jefferson University, we suggest that CT perfusion may have a role in the evaluation of high volume, high grade prostate cancer, but does not have a role in the detection or staging of most prostate cancer. Despite the disparate conclusions of the two CT studies reported in this chapter, both studies suggest that high volume, aggressive prostate cancer does have increased perfusion and may enhance on CT. One group has published several reports related to the imaging of prostate tumor response to radiation therapy on functional CT.25,26 These investigators evaluated subjects with biopsy-proven prostate cancer.

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a

b

Figure 9.7 High-volume, Gleason 8 prostate cancer. The contrast-enhanced image (a) demonstrates no apparent asymmetry in the enhancement pattern. The quantitative perfusion image (b) demonstrates greater perfusion in the right mid-gland, corresponding to the site of tumor (arrow)

Patients underwent functional CT prior to radiation therapy (RT) and again at 1–2 weeks and 6–12 weeks post-treatment. ROIs drawn on the entire prostate gland and on a large artery in the same location at each time-point were used to produce time–attenuation curves. The prostate perfusion, mean contrast agent clearance per unit volume, and the mean fractional vascular volumes were calculated and compared over time. The results demonstrated significant increases in all three parameters between the pre-RT scan and the 1–2-week post-therapy timepoint. No significant change in perfusion was shown between the 1–2-week time-point and the 6–12-week time-point.

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a

b

Figure 9.8 Images of the prostate apex in a patient with a high-volume, Gleason 6 tumor in the right apex. Contrast-enhanced CT (a) demonstrates symmetric enhancement of the prostate, and quantitative perfusion (b) demonstrates no difference in perfusion between the malignancy on the right and benign tissue on the left side

Post-RT studies evaluated changes in CT perfusion of the prostate in response to radiation treatment. The results demonstrated early hyperemia of the gland, a known response of tissue to radiation. Since prostate cancer may also appear hyperemic, the authors could not determine whether prostate cancer perfusion responded differently to radiation therapy as compared with normal tissue. PSA values would still be necessary to follow disease response, and cannot be replaced by CT perfusion. The hyperemic response was still evident at 6–12 weeks, and gave no indication of the overall effect of RT on the disease process. Further studies are needed to clarify the clinical utility of CT perfusion after therapy for prostate cancer. Based upon currently available clinical studies, there is no clear-cut clinical indication for CT perfusion in the evaluation of prostate cancer. Unlike many solid tumors which tend to grow as a solitary round mass,

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prostate cancer is commonly multifocal, is often infiltrative throughout the prostate, and may grow in an oblong or irregular shape along the capsule of the prostate gland.27,28 These histologic features of prostate cancer growth limit our ability to visualize prostate cancer with conventional imaging of the prostate by ultrasound, CT, and MRI. These same histologic features are likely to complicate the results of CT perfusion. Future research is required to determine whether a more sensitive perfusion technique may improve the detection and staging of prostate cancer.

PERFUSION IMAGING OF OTHER TUMORS OF THE UROGENITAL TRACT Kenneth A Miles

Renal cancer CT perfusion studies of renal cancer were first reported in 1994.29 Renal tumors are typically highly vascularized, but CT perfusion images frequently show high perfusion peripherally with lower perfusion in a central area of cystic change or necrosis29,30 (Figure 9.9). Metastatic lesions from renal cancer are also frequently hypervascular (Figure 9.10).

Figure 9.9 CT perfusion image of renal cancer. (Reproduced with permission from reference 31)

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a

b

Figure 9.10 Hepatic arterial (a) and portal (b) perfusion images of a hepatic metastasis from renal cancer. Arterial perfusion values are very high, whereas there is virtually no portal perfusion in the lesion. Note also abnormal arterial and portal perfusion values in the adjacent liver tissue that is apparently normal on conventional CT images. (Reproduced with permission from reference 31)

Due to the mobility of the kidneys, particular care must be taken to control respiratory motion when generating CT perfusion images of renal tumors. The recent dynamic contrast-enhanced CT study of 24 renal cancers by Wang et al. reports a range of enhancement parameters which demonstrate significant correlations with microvessel density.30 The rapid cycle time (one image every 4.9 s) allows further analysis of the

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12 10

SPV

8 6 4 2 0 50

100

150

200

MVD (/mm2)

Figure 9.11 Correlation between standardized perfusion value (SPV) and microvessel density (MVD) based on the results of Wang et al.30 r = 0.48, p < 0.02

reported enhancement parameters to derive estimated perfusion values (slope method) and standardized perfusion values (SPVs).32 Using an iodine calibration factor of 21.32 HU/mg iodine,33 the medians (ranges) for tumor perfusion and SPV were 285 (47–1140) ml/min/100 ml and 2.0 (0.2–11) ml/min/100 ml respectively. The SPV was also found to correlate significantly with microvessel density (r = 0.48, p < 0.02; Figure 9.11), whereas no significant correlation was seen with estimated perfusion (r = 0.23, p = 0.28). Because the SPV reflects tumor perfusion normalized to cardiac output,32 SPV values are more likely to reflect microvessel density than are perfusion values, which are affected by central circulatory parameters. The earlier study of 40 renal tumours by Jinzaki et al. also found a significant correlation between tumor enhancement at 35 s and microvessel density.34 These researchers additionally reported significantly higher enhancement in clear-cell tumors as compared to other types of renal cancer (Table 9.1). Clear-cell tumors all showed enhancement of greater than 100 HU (3.3 HU/g iodine), whereas the remaining tumors enhanced below this threshold.

Cervical cancer There has been a single but thorough study of CT perfusion in cancer of the uterine cervix.35 The study group comprised 32 patients with tumor stages ranging between T1b and T3b. Tumors demonstrated moderately high vascularization. Based on deconvolution analysis, mean values for perfusion, blood volume, and permeability were 95.5 ml/min/100 g,

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Table 9.1 Peak enhancement values (35 s or 180 s) and corresponding perfusion/ cardiac output for various types of renal cancer (RCC) based on reference 34 Tumor type Clear-cell RCC Chromophobe RCC Papillary RCC Oncocytoma Metanephric adenoma

Peak enhancement (HU) 165.0 ± 45.8 91.6 ± 1.6 62.5 ± 7.0 94.2 ± 22.3 92.5 ± 7.0

Perfusion/cardiac output (%/100 ml)* 2.58 ± 0.72 1.43 ± 0.03 0.98 ± 0.11 1.47 ± 0.35 1.45 ± 0.11

*Assumes an iodine calibration factor of 21.32 HU/mg iodine as reported by Miles et al.33

21.6 ml/100 g, and 18.0 ml/min/100 g respectively. Blood volume and permeability values were significantly higher in patients with enlarged locoregional lymph nodes on magnetic resonance imaging, suggesting a link between intensity of angiogenesis and propensity for tumor metastasis. Perfusion values demonstrated a moderate positive correlation with measurements of tumor oxygenation. Tumor hypoxia is known to be an adverse prognostic factor for cervical cancer. Although further validation is required, this study has highlighted the potential for CT perfusion to act as a less invasive prognostic marker than the polarographic needle electrode system required to measure tumor oxygen tension.

REFERENCES 1. Jemal A, Siegel R, Ward E et al. Cancer statistics, 2007. CA Cancer J Clin 2007; 57: 43–66. 2. Levran A, Gonzalez, JA, Diokno AC et al. Are pelvic computed tomography, bone scan, and pelvic lymphadenectomy necessary in the staging of prostatic cancer? Br J Urol 1995; 75: 778–81. 3. Kindrick AV, Grossfeld GD, Stier DM et al. Use of imaging tests for staging newly diagnosed prostate cancer: trends from the CAPSURE database. Urology 1998; 160: 2102–6. 4. Saigal CS, Pashos CL, Henning JM, Litwin MS. Variations in use of imaging in a national sample of men with early-stage prostate cancer. Urology 2002; 59: 400–4. 5. Gardner TA, Lemer ML, Schlegel RS et al. Microfocal prostate cancer: biopsy cancer volume does not predict actual tumour volume. Br J Urol 1998; 81: 839–43. 6. Dang CV, Semenza GL. Oncogenic alterations of metabolism. Trends Biochem Sci 1999; 24: 68–72. 7. Bigler SA, Deering RE, Brawer MK. Comparison of microscopic vascularity in benign and malignant prostate tissue. Human Pathol 1993; 24: 220–6.

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8. Brawer MK. Quantitative microvessel density: a staging and prognostic marker for human prostatic carcinoma. Cancer 1996; 78: 345–9. 9. Halpern EJ, Strup SE. Using gray scale, color and power Doppler sonography to detect prostatic cancer. AJR Am J Roentgenol 2000; 174: 623–7. 10. Wijkstra H, Wink MH, de la Rosette JJ. Contrast specific imaging in the detection and localization of prostate cancer. World J Urol 2004; 22: 346–50. 11. Aarnink RG, Beerlage HP, de la Rosette JJ et al. Contrast angiosonography: a technology to improve Doppler ultrasound examinations of the prostate. Eur Urol 1999; 35: 9–20. 12. Halpern EJ, Verkh L, Forsberg F et al. Initial experience with contrast-enhanced sonography of the prostate. AJR Am J Roentgenol 2000; 174: 1575–80. 13. Halpern EJ, Rosenberg M, Gomella LG. Prostate cancer: contrast enhanced US for detection. Radiology 2001; 219: 219–25. 14. Halpern EJ, Ramey JR, Strup SE et al. Detection of prostate cancer with contrast enhanced sonography using intermittent harmonic imaging. Cancer 2005; 104: 2372–83. 15. Goossen TE, de la Rosette JJ, Hulsbergen-van de Kaa CA, van Leenders GJ, Wijkstra H. The value of dynamic contrast enhanced power Doppler ultrasound imaging in the localization of prostate cancer. Eur Urol 2003; 43: 124–31. 16. Claus FG, Hricak H, Hattery RR. Pretreatment evaluation of prostate cancer: role of MR imaging and 1H MR spectroscopy. Radiographics 2004; 24 (Suppl 1): S167–80. 17. Prando A, Kurhanewicz J, Borges AP, Oliveira EM Jr, Figueiredo E. Prostatic biopsy directed with endorectal MR spectroscopic imaging findings in patients with elevated prostate specific antigen levels and prior negative biopsy findings: early experience. Radiology 2005; 236: 903–10. 18. Brown G, Macvicar DA, Ayton V et al. The role of intravenous contrast enhancement in magnetic resonance imaging of prostatic carcinoma. Clin Radiol 1995; 50: 601–6. 19. Jager GJ, Ruijter E, van de Kaa CA et al. Dynamic TurboFLASH subtraction technique for contrast-enhanced MR imaging of the prostate: correlation with histopathologic results. Radiology 1997; 203: 645–51. 20. Engelbrecht MR, Huisman HJ, Laheij R et al. Discrimination of prostate cancer from normal peripheral zone and central gland tissue by using dynamic contrast-enhanced MR imaging. Radiology 2003; 229: 248–54. 21. Halpern EJ, Cochlin DL, Goldberg BB. Imaging of the Prostate. New York: Martin Dunitz, 2002. 22. Prando A, Wallace S. Helical CT of prostate cancer: early clinical experience. AJR Am J Roentgenol 2000; 175: 343–6. 23. Ives EP, Burke MA, Edmonds PR et al. Quantitative CT perfusion of prostate cancer: correlation with whole mount pathology. Clin Prostate Cancer 2005; 4: 109–12. 24. Miles KA. Measurement of tissue perfusion by dynamic computed tomography. Br J Radiol 1991; 64: 409–12. 25. Harvey C, Dooher A, Morgan J et al. Imaging of tumour therapy responses by dynamic CT. Eur J Radiol 1999; 30: 221–6. 26. Harvey CJ, Blomley MJ, Dawson P et al. Functional CT imaging of the acute hyperemic response to radiation therapy of the prostate gland: early experience. J Comput Assist Tomogr 2001; 25: 43–9.

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27. Byar DP, Mostofi FK. Carcinoma of the prostate: prognostic evaluation of certain pathologic features in 208 radical prostatectomies. Cancer 1972; 30: 5–13. 28. McNeal JE, Redwine EA, Freiha FS, Stamey TRA. Zonal distribution of prostatic adenocarcinoma. Correlation with histologic pattern and direction of spread. Am J Surg Pathol 1988; 12: 897–906. 29. Miles KA, Hayball MP, Dixon AK. Functional imaging of changes in human intra-renal perfusion using quantitative dynamic computed tomography. Invest Radiol 1994; 29: 911–14. 30. Wang JH, Min PQ, Wang PJ et al. Dynamic CT evaluation of tumor vascularity in renal cell carcinoma. AJR Am J Roentgenol 2006; 186: 1423–30. 31. Miles K, Blomley M. Applications of perfusion CT. In: Miles K, Blomley M, Dawson P, eds. Functional Computed Tomography. Oxford: ISIS Medical Media, 1997: 89–116. 32. Miles KA, Griffiths MR, Fuentes MA. Standardized perfusion value: universal CT contrast enhancement scale that correlates with FDG PET in lung nodules. Radiology 2001; 220: 548–53. 33. Miles KA, Young H, Chica SL, Esser PD. Quantitative contrast-enhanced computed tomography: is there a need for system calibration? Eur Radiol 2007; 17: 919–26. 34. Jinzaki M, Tanimoto A, Mukai M et al. Double-phase helical CT of small renal parenchymal neoplasms: correlation with pathologic findings and tumor angiogenesis. J Comput Assist Tomogr 2000; 24: 835–42. 35. Haider MA, Milosevic M, Fyles A et al. Assessment of the tumor microenvironment in cervix cancer using dynamic contrast enhanced CT, interstitial fluid pressure and oxygen measurements. Int J Radiat Oncol Biol Phys 2005; 62: 1100–7.

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10 CT perfusion of lymph nodes Kenneth A Miles

INTRODUCTION The assessment of lymph nodes is an essential component of the application of multidetector computed tomography (CT) in oncology, both in the detection of metastatic disease within the locoregional lymph nodes draining a tumor, and in the assessment of lymphoma. The main criterion used in conventional CT assessments comprises a measurement of nodal size, although other morphological features such as shape and texture, and the appearances of nearby nodes, may also contribute. However, the limitations of this morphological approach are well recognized. For example, lymph nodes that contain tumor may be smaller than the cut-off size used for the diagnosis of involvement. On the other hand, lymph nodes may be enlarged yet be reactive rather than contain tumor cells. Lymph nodes may also remain enlarged despite successful treatment (see Figure 10.5). Furthermore, interobserver variation in the measurement of lymph node size creates significant potential for misclassification.1 In view of these limitations, a range of functional imaging techniques has been explored as a means to improve the accuracy of diagnosis of lymph node involvement by tumor, most notably the use of fluorodeoxyglucose-positron emission tomography (FDG-PET) using integrated PET–CT systems. However, the functional information obtainable with CT perfusion has the potential to provide similar benefits but with greater simplicity and availability than with PET-CT. When located within body regions that are less susceptible to respiratory motion, for example the retroperitoneum and lung, lymph nodes are frequently well suited for reliable measurement of enhancement or perfusion (Box 10.1). However, measurements may be difficult in small lymph nodes (i.e. ≤ 5 mm). Early in the first pass of contrast medium,

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Box 10.1 ●

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less prone to respiratory motion e.g. mediastinum, retroperitoneum measurements may be difficult in small nodes (< 5 mm) potential artifacts when adjacent to large vessels assessment of lymph nodes or primary tumor.

high vascular contrast density can also produce beam hardening artifacts within lymph nodes that lie close to major vessels. Although the application of CT perfusion to the assessment of tumor within lymph nodes is, as yet, underdeveloped, this chapter reviews the currently available data supporting the use of this technique, both in the diagnosis of lymph node metastases and in the assessment of lymphoma.

LYMPH NODE METASTASES Three studies have shown the potential for CT perfusion to improve the performance of CT in the diagnosis of lymph node metastases (Table 10.1). All three studies had pathological confirmation of nodal status and used measures of lymph node enhancement which could be shown to reflect perfusion relative to cardiac output (see Chapter 3). The first of these studies considered the staging of 58 patients with gastric cancer.2 A helical data acquisition was commenced at 40 seconds following a 100-ml bolus of contrast material with an iodine concentration of 300 mg/ml injected at 3 ml/s. The tube voltage for image acquisition was 120 kVp. When a nodal attenuation of 100 HU was used as

Table 10.1 Comparison of the diagnostic performances of size (single node ≥ 10 mm), enhancement, and combined criteria in the diagnosis of nodal metastases

Primary tumor

Authors

Stomach Breast Lung (T1)

Fukuya et al.2 Yuen et al.3 Shim et al.4

Size criteria Sens. Spec. (%) (%) 33 15 27

78 100 90

Enhancement criteria Sens. Spec. (%) (%) 64 78 65*

*Enhancement of primary tumor; Sens., sensitivity; Spec., specificity

93 52 76*

Combined criteria Sens. Spec. (%) (%) 85 89 88*

88 93 90*

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the threshold to distinguish metastasis-positive and metastasis-negative nodes, the sensitivity was 64% and specificity 93%. This diagnostic performance was superior to that obtained using size criteria, but was further improved when size and enhancement criteria were used in combination. As no pre-contrast images were obtained, true enhancement measures were not available. However, assuming a mean baseline nodal attenuation of 25 HU as observed by Yuen et al.,3 this enhancement threshold can be approximated to 75 HU or 2.5 HU/g iodine injected. Yuen et al. studied the enhancement of the sentinel axillary node in 107 Japanese women with breast cancer ranging from cancer-in-situ to stage T4.3 Images were acquired pre-contrast and at 1, 3, and 8 minutes after a 27-g load of iodinated contrast material injected over 45 s. A higher tube voltage of 135 kVp was used, and thus a lesser degree of enhancement would be expected for a given iodine load compared to 120 kVp (Chapter 3). There were significant differences in the average enhancement curves observed in metastasis-positive and metastasisnegative nodes (Figure 10.1). Tumor-bearing nodes enhanced to a greater degree and reached their peak value earlier than benign nodes. Enhancement criteria produced higher sensitivity but lower specificity for the diagnosis of metastasis than did unidimensional measurements of a single node. The threshold enhancement value used for diagnosis was a two-fold increase over baseline measured at 1 minute after injection. This degree of enhancement approximated to 50 HU or 1.85 HU/g iodine injected. Combined size–enhancement criteria produced even better diagnostic performance, but a comparable performance could also be achieved by a more complex morphological analysis that included the size of adjacent axillary nodes.

Enhancement (HU)

120

No metastasis Metastasis

80

40

0 0 −40

2

4

6

8

10

Time (min)

Figure 10.1 Mean ± standard deviation enhancement values in sentinel axillary lymph nodes with and without metastases. (Adapted from reference 3)

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Shim et al. adopted a different approach to the use of hemodynamic studies for the prediction of nodal metastasis.4 Rather than measure enhancement of the regional lymph nodes, these researchers measured enhancement within the primary lung tumor itself (Figure 10.2). Histological evidence for an association between intensity of angiogenesis and nodal metastasis in non-small-cell lung cancer provided the basis for this methodology.5 Using a tube voltage of 120 kVp, volumetric image acquisitions were performed every 20 s for 3 minutes following a 36-g load of iodinated contrast material injected over 40 s. The study group comprised 84 T1 tumors. Enhancement criteria produced higher sensitivity but lower specificity for the diagnosis of metastasis than did conventional morphological assessment of nodal status (Table 10.1). The threshold enhancement value used for diagnosis was 60 HU, equivalent to 1.67 HU/g iodine injected. Once again, a combination of size and enhancement criteria produced the best diagnostic performance.

a

b

c

d 40

HU

30 20 10 0 1b

2b

3b

4b

−10 Time (s) T1

n1

Figure 10.2 Conventional computed tomography (CT) (a) and maximal enhancement image (b) of a T1 tumor non-small-cell lung cancer (arrow) with enlarged ipsilateral hilar and mediastinal lymph nodes (chevrons). The time– density curves following a 50-ml bolus of contrast material with iodine concentration 300 mg/ml (c) demonstrate a maximum enhancement of 34 HU within the primary tumor (red curve). This degree of enhancement is equivalent to 2.27 HU/g iodine and lies above the threshold of 1.67 HU/g that predicts nodal metastasis. Hilar and mediastinal metastases are confirmed by fluorodeoxyglucosepositron emission tomography (FDG-PET) (d). Note that the involved nodes (green curve) demonstrate less enhancement than the primary tumor

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40

Primary Nodal mestastasis Threshold

30 Size (mm)

167

20 10 0 0

Time

Figure 10.3 Theoretical growth curves for a primary tumor and nodal metastasis. The double-headed green arrow demarcates a period of time in which enhancement is readily measured in the primary tumor but the lymph node remains normal by conventional size criteria

These three papers are interesting in that, despite different technical approaches to different tumor types, the enhancement thresholds for diagnosis of metastasis are similar, ranging between 1.67 and 2.5 HU/g iodine injected. In each case, a combination of size and enhancement criteria gave the best diagnostic performance. The use of enhancement measures within the primary tumor, as adopted by Shim et al., holds particular potential for the diagnosis of micrometastatic disease within regional lymph nodes. The advantage of this approach is shown in Figure 10.3, which illustrates theoretical tumor growth curves for a primary tumor and nodal metastasis. Assuming that the primary tumor must reach a certain size before metastasizing, the growth curve for the nodal metastasis will lag behind that for the primary tumor. The horizontal line demarcates the 10 mm size criterion typically used for the diagnosis of metastasis. Note that there is a time period in which the primary tumor is greater than 10 mm in size, and therefore suitable for CT perfusion, yet the affected lymph node is still less than 10 mm in size. This period of time offers an opportunity to use measurements of enhancement within the primary tumor to detect nodal metastasis less than 10 mm in size, i.e. when they are normal by size criteria and difficult to measure reliably with CT perfusion.

LYMPHOMA CT remains the mainstay imaging technique in the management of patients with lymphoma, primarily for staging and assessment of treatment response.

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However, there are a number of limitations to conventional CT imaging in lymphoma, which perfusion imaging has the potential to address. The choice of therapy for lymphoma is largely based on the grade of tumor. The grade of lymphoma also carries important prognostic significance. Although lymphoma grade is usually determined from histological examination of lymph nodes, it is well recognized that tumor grade can change from low to high during the natural history of the disease.6 Furthermore, such transformation may occur with a subset of the involved lymph nodes. Thus, in some cases it may be difficult to confirm a change in grade without repeated and/or multiple biopsies. The structural appearances of lymph nodes on CT provide little information concerning tumor grade. However, studies by Ribatti et al.7 have shown that microvessel density increases progressively from low- to high-grade lymphoma. Although no direct correlation has been shown for lymphoma, the correlation between microvessel density and CT perfusion measurements for other tumors suggests a potential role for CT perfusion in assessing lymphoma grade. A preliminary study by Dugdale et al.8 considered the relationship between tumor grade and CT measurements of perfusion and permeability in a mixed population of 39 patients with lymphoma. Of the 21 patients who underwent perfusion measurement, five had low-grade and 16 had intermediate/high-grade lymphoma. Perfusion measurements were higher in patients with intermediate- or high-grade tumor than in those with low-grade disease (56 ml/min/100 ml vs. 46 ml/min/100 ml). Although there was overlap between patient groups, a perfusion value of greater than 50 ml/min/100 ml suggested intermediate- or high-grade tumor (Figure 10.4). Measurements of capillary permeability derived by Patlak analysis in a separate group of 18 patients were unrelated to lymphoma grade. A further constraint to conventional CT in the assessment of patients with lymphoma is that a residual tissue mass is a common finding following treatment (Figure 10.5). A meta-analysis of the performance of conventional CT in detecting active residual disease at completion of chemotherapy, performed as part of a recent health technology assessment in the UK, found low diagnostic specificity of only 45% (95% confidence limits 27–64%).6 Based on decision modeling, this low specificity would result in a CT-based strategy for the management of patients with lymphoma on completion of therapy leading to 36% of patients receiving unnecessary radiotherapy. Furthermore, the average survival benefit and cost per patient for the CT-based strategy would be only marginally better than a hypothetical management strategy in which all patients received consolidation therapy without imaging.10

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a

b

Figure 10.4 Conventional CT (a) and CT perfusion (b) of a patient with a lymphoma of the small bowel mesentery prior to therapy. The perfusion value is above 50 ml/min/100 ml implying high-grade disease

By reflecting angiogenesis, CT perfusion offers a means to assess the activity of lymphoma and response to therapy on functional grounds.9 In view of the now recognized importance of angiogenesis in lymphoma, CT perfusion imaging offers a possible functional technique for the assessment of therapeutic response, not only for conventional therapy but also for the antiangiogenesis agents which are currently being assessed as therapeutic agents for lymphoma. The potential for perfusion imaging to be used as a marker for residual lymphoma activity is further highlighted by the correlation between perfusion and glucose metabolism within residual lymphoma masses that has been demonstrated using [15O] water and FDG-PET.11 The preliminary functional CT study by Dugdale et al.8 also considered the potential for CT measurements of perfusion and permeability

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a

b

c

d

Figure 10.5 Conventional CT (a, b) and corresponding CT perfusion images (c, d) from two patients following treatment for lymphoma. The small nodal masses in (a) and (c) (small arrows) demonstrate high perfusion implying active disease, whereas the large mass (large arrow) in (b) and (d) shows low perfusion implying inactive disease. The disease status was confirmed by FDG-PET. (Adapted from reference 9)

to identify active residual disease (Figure 10.5). Median values of perfusion were higher in patients with active disease (55 vs. 37 ml/min/100 ml), and nodal perfusion below 20 ml/min/100 ml implied inactive disease ( p < 0.03). On the other hand, permeability values were little different between these groups, and showed no correlation with change in activity on serial studies. When using CT perfusion as a tumor response marker (see Chapter 12), it is important to be aware that certain treatments can induce direct effects upon the vascularity of the tumor or adjacent tissues and so mask the therapeutic response. For example, radiotherapy can cause a temporary increase in CT values for both tumor perfusion and vascular permeability.12

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Therefore, the timing of functional imaging for therapeutic monitoring must take these factors into account.

SUMMARY Although further studies are required to fully determine the clinical value of the technique, studies performed to date highlight the potential for CT perfusion to be a useful adjunct to conventional structural CT in the detection of lymph node metastases and the assessment of disease grade and activity in patients with lymphoma.

REFERENCES 1. Guyatt GH, Lefcoe M, Walter S et al. Interobserver variation in the computed tomographic evaluation of mediastinal lymph node size in patients with potentially resectable lung cancer. Canadian Lung Oncology Group. Chest 1995; 107: 116–19. 2. Fukuya T, Honda H, Hayashi T et al. Lymph-node metastases: efficacy for detection with helical CT in patients with gastric cancer. Radiology 1995; 197: 705–11. 3. Yuen S, Yamada K, Goto M, Sawai K, Nishimura T. CT-based evaluation of axillary sentinel lymph node status in breast cancer: value of added contrastenhanced study. Acta Radiol 2004; 45: 730–7. 4. Shim SS, Lee KS, Chung MJ et al. Do hemodynamic studies of stage T1 lung cancer enable the prediction of hilar or mediastinal nodal metastasis? AJR Am J Roentgenol 2006; 186: 981–8. 5. Volm M, Koomagi R, Mattern J. PD-ECGF, bFGF, and VEGF expression in nonsmall cell lung carcinomas and their association with lymph node metastasis. Anticancer Res 1999; 19: 651–5. 6. Bastion Y, Sebban C, Berger F et al. Incidence, predictive factors, and outcome of lymphoma transformation in follicular lymphoma patients. J Clin Oncol 1997; 15: 1587–94. 7. Ribatti D, Vacca A, Nico B et al. Angiogenesis spectrum in the stroma of B-cell non-Hodgkin’s lymphomas. An immunohistochemical and ultrastructural study. Eur J Haematol 1996; 56: 45–53. 8. Dugdale PE, Miles KA, Kelley BB, Bunce IH, Leggett DAC. CT measurements of perfusion and permeability within lymphoma masses: relationship to grade, activity and chemotherapeutic response. J Comput Tomogr 1999; 23: 540–7. 9. Miles KA. Lymphoma and myeloma: monitoring treatment response with [18F]FDG-PET and other functional imaging techniques. ICIS Cancer Imaging Volume 4: S131–5 (posted 3 January 2005). 10. Bradbury I, Bonell E, Boynton J et al. Positron emission tomography (PET) imaging in cancer management. Health Technology Assessment Report 2. Glasgow: Health Technology Board for Scotland, 2002.

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11. Dimitrakopoulou-Strauss A, Strauss LG, Goldschmidt H et al. Evaluation of tumor metabolism and multidrug resistance in patients with treated malignant lymphomas. Eur J Nucl Med 1995; 22: 434–42. 12. Harvey CJ, Blomley MJ, Dawson P et al. Functional CT imaging of the acute hyperemic response to radiation therapy of the prostate gland: early experience. J Comput Assist Tomogr 2001; 25: 43–9.

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11 CT perfusion of liver metastases and early detection of micrometastases Charles A Cuenod, Laure Fournier, Daniel Balvay, and Kenneth A Miles

INTRODUCTION The liver is the second site of metastatic disease,1 and the most common site of metastasis of colorectal carcinoma.2 Eighty percent of patients with colorectal carcinoma recurrence die within 3 years, predominantly from hepatic metastases.3 Despite the availability of various therapeutic options, hepatic metastases remain difficult to eradicate, partly due to their late discovery. Indeed, imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) allow diagnosis of most liver tumors over 1 cm (sensitivity of 55–90%),4 but, for smaller lesions, sensitivity is much lower (under 50%), and microscopic lesions remain occult. Liver metastases are present but not detected in up to one-third of patients who undergo apparently curative excision of primary colorectal carcinoma.5 Therefore, the identification of patients with micrometastatic disease who are at risk of recurrence would have profound implications for prognosis and treatment, since it would allow the implementation of adjuvant chemotherapy at an earlier stage.6 On the other hand, exclusion of micrometastastic disease could obviate potentially morbid chemotherapy in patients without seeds of micrometastasis. Hopes for the earlier detection of liver metastases have been raised by functional imaging suggesting that occult metastases induce changes in liver blood flow similar to those caused by overt metastases,7–11 such as a decrease in the portal perfusion of the liver and an increase

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in arterial perfusion. CT perfusion, therefore, could become a convenient and easy way for early detection of occult liver metastases. In this chapter, we will review the normal microcirculation of the liver and the methods for its quantitative analysis by functional CT. Changes in liver microcirculation induced by the presence of overt and occult metastases will be presented and discussed.

LIVER PERFUSION PHYSIOLOGY The liver has a unique perfusion system with a dual blood supply. More than two-thirds of the blood supply comes from the low-pressure portal vein, and the rest comes from the high-pressure hepatic artery (Figure 11.1). The capillaries of the liver, called ‘sinusoid capillaries’, therefore contain a mixture of portal and arterial blood. The sinusoid capillary system is very dense, representing almost onethird of the volume of the liver parenchyma (whereas in the brain, for example, the blood within the capillaries contributes to only 4% of the volume of tissue). The endothelial cells that line the sinusoids are anatomically and biologically distinct from endothelial cells in

Hepatic veins output

C°(t)

Hepatic artery Ca(t) input 1

CT(t) Liver parenchyma

Slice Portal vein input 2 Cp(t)

Figure 11.1 The liver is supplied more than two-thirds by the portal vein and less than a third by the hepatic artery. A transverse slice through the abdomen allows simultaneous measurement of the attenuation in regions of interst (ROIs) drawn in the portal vein, in the aorta, and in the liver tissue

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other organs. They lack a basement membrane and contain fenestrae, characteristics that facilitate the transport of nutrients and macromolecules between the sinusoids and the hepatic parenchyma. The hepatocytes lining the sinusoids represent most of the volume of the liver parenchyma, and the interstitial space, called the ‘space of Disse’, is almost virtual, representing only 10% of the total liver volume. Specialized pericytes called stellate cells (or Ito cells) are located in the space of Disse and wrap around the walls of the hepatic sinusoids. Regulation of blood flow and vascular resistance in the hepatic microvasculature are intimately linked. Whereas in most organs the site of blood flow regulation occurs at the arteriolar level, in the liver most of the blood flow enters at low pressure through the portal vein, and resistance changes occur in the sinusoid. Stellate cells play a role in the regulation and control of blood flow through the liver based on their anatomic location and contractile characteristics by modulating the sinusoidal caliber in response to several vasoactive endotheliumderived mediators including nitric oxide (NO).12,13 The portal supply varies greatly during the day in relation to bowel activity, with large increases in the postprandial periods. The total hepatic blood supply, however, is finely tuned by the so-called ‘hepatic arterial buffer response’, which is the inverse response of the hepatic artery to changes in portal vein flow.14 These intrinsic regulatory mechanisms based on the local concentration of adenosine tend to maintain total hepatic blood flow at a constant level, allowing an increase in the arterial blood supply to compensate for a decrease in portal supply, and a decrease in arterial blood supply in cases of increased portal supply.15 It is important to note that, in contrast, variations in arterial blood supply cannot be compensated by variations in portal supply.

QUANTITATIVE MEASUREMENT OF LIVER MICROCIRCULATION WITH FUNCTIONAL CT Introduction The specific dual perfusion of the liver makes it more difficult to analyze with contrast-enhanced imaging than the perfusion of other tissues. The enhancement curve of the liver, after the injection of a bolus of contrast agent, is the combination of the enhancement due to the contrast agent flowing in the arterial blood and the contrast agent flowing in the portal blood. Whereas the molecules of contrast agent arrive

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quickly when they are delivered to the liver through the arterial route, they are delayed and diluted by the splanchnic circulation when they are delivered through the portal route. Many strategies have been developed to take advantage of the portal lag to separate and quantify the arterial and portal hepatic perfusions. A comprehensive review of perfusion imaging of the liver has recently been published by Pandharipande et al.16

Different methods for liver perfusion quantification with CT Slope-ratio methods Miles et al.17,18 described liver perfusion imaging using CT in 1993 by generating enhancement curves from regions of interest (ROIs) drawn over the liver, the aorta, and the spleen after a bolus injection of contrast agent. Liver enhancement was resolved into arterial and portal venous components by assuming that maximum splenic enhancement marks the end of the early arterial phase and the beginning of the delayed portal venous phase of liver perfusion. The maximal slopes of the liver time–density curve in each phase were divided by the peak aortic enhancement to calculate both arterial and portal perfusion (Fa, Fp). The hepatic perfusion index (HPI), which is the ratio of the arterial perfusion to the total hepatic perfusion (HPI = arterial perfusion/arterial + portal perfusion) was also calculated. HPI is also known as the ‘Hepatic Arterial Fraction’, see Chapter 2. This technique is simple to implement and can be applied to any segment of the liver, as there is no need to include the portal vein or major portal vessel within the tissue imaged. However, the method underestimates portal hepatic flow for two reasons: first, the downwards slope of the last part of the arterial time–attenuation curve is superimposed on the upwards slope of the arriving portal curve; and second, the maximal slope of the portal venous phase of enhancement is divided by the peak aortic enhancement instead of the peak portal enhancement, which is flattened and diluted after flowing through the splanchnic system. To avoid these limitations, Blomley et al.19,20 modified this approach by subtracting the arterial phase liver enhancement (modeled after splenic enhancement) from the liver enhancement curve to give a more ‘accurate’ portal time–attenuation curve. From this corrected curve, portal perfusion was calculated by dividing the slope of the rise in attenuation during the portal enhancement phase by peak portal venous enhancement itself.

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This ‘corrected approach’, however, has two main limitations. First, it assumes that hepatic arterial and splenic enhancement curves are similar. Such similarity is unlikely in view of the unique microcirculation within the spleen, recognized as the mechanism underlying the transient splenic inhomogeneity seen during contrast-enhanced CT.21 Second, the technique requires a set of slices containing both the portal vein and a part of the spleen to be able to draw ROIs and extract the enhancement curves. The slope-ratio methods have largely been used for the evaluation of hepatic perfusion in metastatic disease.18,22,23 Since these methods take into account only the rising part of the liver enhancement curve, before the venous outflow, they do not give information regarding the hepatic blood volume (HBV) and the mean transit time (MTT) through the sinusoid network. A sharp bolus of contrast medium is essential to avoid underestimation of the arterial and portal perfusion from venous outflow of contrast, which can be considered negligible if the outflow does not begin before the end of the input.

Tracer kinetic modeling To avoid the approximations of the arterial and portal slopes and take into account more of the liver enhancement dynamics, tracer kinetic modeling techniques have been developed24–28 Compartmental model Van Beers and Materne have developed a compartmental model with a dual-input:25,26 dCL(t)/dt = k1aCa(t) + k1pCp(t) − k2CL(t) in which CL, Ca, and Cp are the contrast agent concentrations measured over time respectively within the liver, hepatic artery, and portal vein derived from ROIs, and k1a, k1p, and k2 are the arterial and portal venous inflow and liver outflow rate constants. By fitting measured CL(t), the constants k1a, k1p, and k2 can be estimated, and can be used to calculate hepatic arterial and portal venous perfusion, mean transit time (MTT) of contrast agent through the liver, and contrast agent distribution volume within the liver (Vd). The distribution volume Vd of contrast agent is used instead of the hepatic blood volume, because the small-molecule contrast agent used in CT leaks freely and instantaneously across the sinusoid capillary wall, leading to a distribution volume that associates the hepatic blood volume and part of the extracellular Disse space.

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This compartmental approach assumes that there is an instantaneous mixing of blood in the capillary compartment. Deconvolution model To avoid this assumption in the liver, where the capillary network is complex and the mean transit time long, Cuenod et al. have developed27–29 a specific deconvolution technique. The deconvolution method considers that the time course of the concentration of contrast agent entering a tissue is modulated by a transfer function specific to the tissue. This transfer function can be computed from both the concentration–time curve of contrast agent entering the tissue (input) and the concentration–time curve of contrast in the tissue. From that transfer function, the perfusion parameters of the tissue can be calculated. The deconvolution strategy was introduced into functional CT by Axel in the 1980s.30 The specificity of the liver, for this approach, comes from its dual vascular input. The method is described below.

Deconvolution method for liver perfusion imaging Theory of the deconvolution method (see also Chapter 2) Deconvolution allows the determination of the theoretical impulse response of the tissue, that is, the time course of concentration that an instantaneous input of contrast material (impulse input) would have yielded. Convolution When the contrast agent enters the tissue as a function of time, Ci(t), the time course of the concentration of contrast throughout the tissue depends both on the time course of a theoretical impulse input (instantaneous input) through the tissue, h(t), and on the actual experimental time course of contrast input.30 The concentration–time curve at the venous outflow, Co(t), is the convolution of Ci(t) by h(t): Co(t) = Ci(t) ƒ h(t) Residue function However, since we cannot measure the concentration–time curve at the venous outflow of the tissue and can only measure the concentration–time curve of contrast into the tissue, we have to infer the venous outflow concentration–time curve from the tissue concentration–time curve. To do so, we use the notion of residue function. The integral of h(t) is:

H (t ) =

∫ h (τ ) d τ t

0

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h(t) = dH(t)/dt = -dR(t)/dt

179

Probability density function

h (t)

1.0 H (t)

H(t)= h(t)dt

Cumulative frequency function 1.0 Residue function R (t)

R(t)=1-H(t)

Figure 11.2 Relations between the residue function R(t), the cumulative frequency function H(t), and the probability density function h(t). The contrast agent that progressively leaves the tissue accumulates outside the tissue. At the venous outlet the concentration of contrast agent rises progressively before decreasing to zero. The initial value of R(0) is normalized to one, as well, therefore, as the final value of H(ⴥ) and the area under the curve h(t). (Adapted from reference 31)

where H(t) is the fraction of an impulsive input which has already left the tissue by time t (Figure 11.2). It is called the cumulative frequency function. Its complementary function is called the residue function R(t): R(t) = 1 − H(t) where R(t) is the fraction of the impulsive input remaining within its distribution volume Vd in the tissue at time t.31 The concentration–time curve of a tracer remaining in its volume of distribution within the tissue Cd(t) can be predicted for any type of input function, Ci(t), as the convolution of Ci(t) by R(t): Cd(t) = Ci(t) ƒ R(t) Because the distribution volume Vd of the tracer is within a larger volume of tissue Vt, the concentration of tracer within the tissue is: Ct(t) = Cd(t)⋅Vdt

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where Vdt = Vd/Vt is the fractional dilution volume of the molecule expressed as a percentage of the total volume of tissue Vt. The concentration of tracer in a voxel of tissue Ct(t) is therefore the convolution of Ci(t) by Rt(t), the tissue transfer function (Rt(t) = [Vdt ⋅ R(t)]): Ct(t) = Ci(t) ƒ Rt(t) Estimation of Rt(t), R(t), and h(t) After contrast injection, a deconvolution process can allow the determination of Rt(t), knowing the contrast variation of the tissue Ct(t) and the input function Ci(t). For finite time sampling steps of ∆t = T, the convolution Ct(t) = Ci(t) ƒ Rt(t) can be approximated by the following sum: Ci ( nT ) ⊗ R t ( nT ) = T ∑ kk == 0n−1 Ci ( nT − kT ) ⋅ R t ( kT ) The category of Weibull functions g ( t ) = a ⋅ exp [ − t/b c ] has been chosen to represent the tissue transfer function Rt(t) because its shape is intermediate between a falling exponential function and a square function, resembling the supposed liver curve. A computer program is necessary to minimize the quadratic error between the measured tissue response Ct(nT) at each time nT and the assumed response C*(nT) after convolution of the measured input t Ci(nT) at each time nT: ⎡ ⎛ kT ⎞ c ⎤ C∗t ( nT ) = T ∑ kk==0n−1 ⎡⎣Ci ( nT − kT )⎤⎦ ⋅ a ⋅ exp ⎢− ⎜ ⎟ ⎥ ⎢⎣ ⎝ b ⎠ ⎥⎦ The program yields the value of the three unknown factors a, b, and c, allowing the estimation of Rt(t). Since Rt(t) = Vd ⋅ R(t) and R(0) = 1, then Vd = Rt(0) and R(t) = Rt(t)/Rt(0). When R(t) has been worked out, h(t) can be obtained as its negative derivative h(t) = −

dR ( t ) dt

and the output function Co(t) can be calculated.

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Extraction of microvascular parameters Measurement of the mean transit time The mean transit time (MTT) through the vascular bed can be calculated as the first moment (or the geometric mean) of the calculated Co(t):

∫ tC ( t ) dt MTT = ∫ C ( t ) dt ∞

0 ∞

0

0

0

It can also be calculated as the first moment of the impulse response itself:

∫ th ( t ) dt MTT = ∫ h ( t ) dt ∞

0 ∞ 0

and even more simply, knowing that,

∫ h ( t ) dt = 1 ∞

0

as MTT =

∫ th ( t ) dt. ∞

0

Measurement of the fractional volume of distribution The fractional distribution volume of the tracer Vdt, can be calculated as Rt(0), the initial (maximal) value of Rt(t). As expressed above, the fractional distribution volume of the contrast within the tissue, Vdt, is calculated as the initial (maximal) value of Rt(t): Vd = Rt(0). Measurement of the tissue blood flow with the central volume theorem The blood flow through a unit volume of tissue (Ft = F/volume of the organ) expressed as ml/min/100 ml is measured using the central volume theorem: Ft =

Vdt MTT

Extraction of hepatic perfusion parameters Specifically in the liver, the dual blood supply has to be taken into account for the calculation (Figure 11.1) The respective balance between the arterial input Ca(t) and venous portal input Cp(t) of the liver is expressed as the hepatic perfusion index (HPI), which is the ratio of the arterial blood flow (Fa) over the total hepatic blood flow Ft: Fa HPI = Fa + Fp

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In the liver, arterial and portal blood are mixed in the sinusoidal capillaries, and the tissue concentration–time curve in the liver (referred to as CL) can be expressed as: C t ( t ) = ⎡⎣α Ca ( t ) + (1 − α ) Cp ( t ) ⎤⎦ ⊗ R t ( t ) The computer program has therefore to minimize the quadratic error between the actual tissue response Ct(nT) and the assumed response C *(nT) after convolution of the dual input (Figure 11.3): t ⎡ ⎛ kT ⎞ ⎤ c *t ( nT ) = T ∑ kk == 0n=1 αC a ( nT − kT ) + (1 − α ) Cp ( nT − kT ) ⋅ a ⋅ exp ⎢− ⎜ ⎟ c ⎥ ⎣ ⎝ b⎠ ⎦

)

(

The program yields the value of the four unknown factors α, a, b, and c, allowing the estimation of Rt(t) and α = HPI. Rt(t) allows the calculation of MTT, Vdt, and FT, and HPI allows the calculation of Fa = HPI × FT, and Fp = (1 − HPI) × FT.

80

∆HU

60

40

20

0 0

20

40

60 Time (s)

80

100

Figure 11.3 The time–enhancement curve of the liver, expressed as Hounsfield unit variation (∆HU) over time (continuous line), can be separated by the computer using the deconvolution model into the linear combination of the early and small enhancement curve of the arterial supply (crosses), and the late and strong enhancement curve of the portal supply (stars). The contrast enhancement is obtained by subtracting the mean baseline value from the values measured in the ROIs

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TABLE 11.1 The six main liver perfusion parameters that can be extracted with functional computed tomography (CT) Name

Abbreviation Definition

Unit

Mean transit time

MTT

s

Liver distribution volume

LDV

Total hepatic blood flow Arterial blood flow

FT Fa

Portal blood flow

Fp

Hepatic perfusion index

HPI

Mean time taken by molecules of contrast agent to flow through system Percentage of tissue volume in which the contrast agent distributes itself Total hepatic blood flow FT = Fa + Fp Hepatic blood flow of arterial origin Hepatic blood flow of portal origin Percentage of total blood flow of arterial origin

HPI =

% or ml/100 ml of tissue

ml/min/ml of tissue ml/min/ml of tissue ml/min/ml of tissue %

Fa Fa + Fp

These parameters are obtained by drawing regions of interest (ROIs) on the aorta, the portal vein, and the liver parenchyma. The liver’s ROI has to be drawn as large as possible, avoiding the large vessels. Then, the three ROIs are replicated by the computer on each image of the series to extract the CT attenuation numbers (expressed as Hounsfield units) over time. The time–attenuation curves derived from the aorta Ca(t), the portal vein Cp(t), and the liver Ct(t) can then be used for calculation of the six hepatic perfusion parameters (Table 11.1). When the analysis is run on a pixel-by-pixel basis, it yields parametric maps displaying the value of each parameter in each pixel (Figure 11.4).

FUNCTIONAL DETECTION OF LIVER METASTASES CT perfusion and overt metastases In patients with known metastatic disease, increased arterial perfusion has been shown by Miles et al.18,23,32 and Blomley et al.19 with values around 40–50 ml/min/100 ml in patients with known metastatic disease versus values of 17–19 ml/min/100 ml in healthy control groups (Figure 11.5). Leggett et al.22 also demonstrated increased arterial

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Transit time

50

Permeability

40 HU

30 20 10 0 −10

0

10

20

30

40

time (s) Iiver

Perfusion

Blood volume

HPI

Figure 11.4 By analysis of the liver time–density curve (top left), it is possible to derive parametric images displaying a range of parameters reflecting the hepatic vasculature. (HPI; hepatic perfusion index)

a

b

c

d

Figure 11.5 Conventional portal phase computed tomography (CT) (a) and CT perfusion images of a hepatic metastasis from colorectal cancer. The metastasis exhibits increased arterial perfusion (b) and hepatic perfusion index (c) but low levels of portal perfusion (d). Note that the area of increased arterial perfusion and hepatic perfusion index extends beyond the margins of the metastasis

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TABLE 11.2 Perfusion parameter values (median [range]) in rats with micrometastases and macrometastases compared to control rats

Heamodynamic parameters

Normal livers control rats

Micrometastases normal appearance of liver

Macrometastases

HPI (%) MTT (s) LDV (%) FT (ml/min/ml) Fa (ml/min/ml) Fp (ml/min/ml)

17 [19] 7.93 [4] 46.4 [7.8] 3.23 [1.9] 0.5 [0.7] 3.08 [2.4]

9.2 [23] 9.94 [4.96]* 43.5 [13.9] 2.07 [0.94]* 0.21 [0.44] 2.04 [1.28]*

74.6 [70]** 25.19 [16.17]** 29.5 [14.5]** 0.71 [0.71]** 0.41 [0.25] 0.20 [0.78]**

(Mann–Whitney U test, *p < 0.05, **p < 0.015) (Data from references 27 and 28)

perfusion (> 25 ml/min/100 ml) in nine of 11 patients with overt colorectal metastases examined using the same CT perfusion technique, and a decrease in portal liver perfusion in five of these patients. In the rat, Cuenod et al., using the deconvolution technique, found in visible liver metastases from colon cancer, a large increase in HPI and a decrease in liver perfusion due to a decrease in portal perfusion.27 A decrease in the distribution volume and an increase in MTT were also observed (Table 11.2). Moreover, Tsushima et al.33 showed that patients with liver metastases had abnormal blood flow in apparently normal liver compared to controls, and this difference was not seen in subjects with malignancy but without liver metastases.

Micrometastases Modifications of liver perfusion can be found not only in patients with overt liver metastases, but also in patients bearing occult microscopic disease who reveal liver metastases on follow-up. In a cohort of 80 patients with colon cancer and without overt liver metastases, Platt et al.11 showed, on classic liver CT examination, that liver enhancement 40 seconds post-injection was higher in 22 patients who revealed liver metastases in the following 18-month period. With a threshold of 0.4 for the ratio of the difference between the liver density before injection and that at 40 s over the maximum liver enhancement, the test had a sensitivity of 75% and a specificity of 96%.

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Leggett et al.22 in addition to describing liver perfusion changes in patients with overt metastases as mentioned earlier, also showed a decrease in portal liver perfusion in the three patients who revealed hepatic metastases during the follow-up period, among 16 patients without initial liver metastases. In animals, similar results have been reported by Cuenod et al.27 showing decreased portal perfusion and an increased HPI associated with an increased mean transit time in the livers of rats bearing occult metastases.

Relationship between hepatic CT perfusion and survival Cancer patients entering imaging surveillance programs following successful primary treatment do not represent a uniform population of equal risk of recurrence. The identification of predictive factors that are linked to outcomes may allow the modification of surveillance strategies for subgroups of patients, and the need for research in this area has been highlighted by an expert panel of the American Society of Clinical Oncology.34 Although several laboratory-derived predictive factors have been identified for patients with colorectal cancer,35,36 relatively little attention has been given to biomarkers derived from diagnostic imaging. CT perfusion represents one of the recent developments in physiological imaging that can provide new opportunities for the use of imaging as a biomarker.37 There is preliminary evidence to suggest that CT measurements of hepatic perfusion may relate to patient survival, not only in the presence of visible metastases but also when no overt lesions are depicted by conventional CT (Figure 11.6). In a series of 13 patients with hepatic metastases from a range of primary tumors, Miles et al. found that survival of the patient correlated significantly with arterial perfusion values in the metastasis and adjacent liver.23 A similar association has also been reported from histological studies of tumor angiogenesis in metastatic colorectal cancer.38 In a separate study, the relationship between hepatic CT perfusion and survival was evaluated in a larger series of 80 patients with colorectal cancer.39 Patients were stratified on the basis of conventional and CT perfusion as follows: (1) no visible metastases on CT and HPI < 0.35, (2) no visible metastases on CT and HPI ≥ 0.35 and (c) overt liver metastases on CT. No metastases were visible in 36 patients, and distribution of the modified Dukes classification was A: 5, B: 9, C: 48, D: 18. Stratification of survival risk by CT perfusion appeared to be superior to that based on the Dukes classification (Figure 11.7).

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> 46 months

b

20 10 0

c

40 Enhancement (HU)

30

9 months

40 Enhancement (HU)

Enhancement (HU)

a

31 months

40

30 20 10

30 20 10

0 0

10

20

30

40

0 0

10

Time (s)

20

30

40

0

10

Time (s)

20

30

40

Time (s)

Figure 11.6 Three cases illustrating the relationship between hepatic enhancement and survival. Conventional CT images are displayed above with corresponding time–density curves (TDCs) below. The arrows indicate the separation of arterial and portal phases of enhancement as identified by the time of peak splenic enhancement. The first case (a) survived for more than 46 months following CT, which demonstrated no visible metastases on conventional images. The hepatic TDC is normal with greater enhancement in the portal phase. The second case (b) also had no visible metastases on CT but survived for a shorter time (31 months). The hepatic TDC shows increased enhancement in the arterial phase implying possible micrometastatic disease. The third case (c) had visible metastases and survived only 9 months following CT. Arterial phase enhancement is considerably raised

a

b

0.1

0.8

Survival

Survival

0.8

0.1

0.6 0.4

0 0

12

0.4 Dukes A

HPI < 0.35 no Mets HPI < 0.35 + Mets HPI ≥ 0.35 no Mets HPI ≥ 0.35 + Mets

0.2

0.6

Dukes B Dukes C Dukes D

0.2 0 24

Time (months)

36

0

12

24

36

Time (months)

Figure 11.7 Kaplan–Meier curves for a cohort of 80 patients with colorectal cancer stratified by perfusion CT (a) and Dukes classification (b). The stratification is superior with perfusion CT (blue = no visible metastases (Mets) and HPI < 0.35, red = no visible metastases and HPI ≥ 0.35, yellow = visible metastases and HPI ≥ 0.35, green = visible metastases and HPI < 0.35). (Data from reference 39)

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Other imaging techniques The results found in CT are validated by similar results found previously with functional scintigraphy and duplex Doppler ultrasound, and more recently with MRI. Furthermore, hepatic CT perfusion parameters have been shown to correlate with dynamic liver scintigraphy17 and Doppler ultrasound.40

Dynamic liver scintigraphy It was shown as early as 1987 by quantitative hepatic scintiangiography that the presence of liver metastases induces changes in hepatic arterial and portal venous blood flows in animals and in humans.7–9 Although values of hepatic arterial and portal venous blood flow were difficult to measure and the reproducibility of the computed parameters was quite poor, HPI was achievable, and was shown to increase not only in the presence of overt metastases9 (above a HPI threshold value of 0.37, metastatic disease should be suspected), but also in patients for whom liver metastases appeared subsequently during follow-up,7,41,42 as well as in animals bearing occult metastases. Ballantyne and Cartert pointed out that a single estimation of HPI was not reliable for the identification of patients with overt hepatic metastases,41,43 and suggested, therefore, the use of the rise of HPI on serial studies, which is associated with progression of disease and increases the reliability of the test.

Duplex Doppler ultrasound Similar results have been shown using Duplex Doppler ultrasound.10,44,45 In this technique, the blood flows reaching the liver though the portal vein and the hepatic artery are calculated from the product of the velocities and the cross-sectional areas of the respective vessels. Although there is no significant difference in total liver blood flow in patients with colorectal liver metastases when compared to controls, the ratio of the hepatic arterial flow to the sum of the portal and hepatic arterial flows (Doppler perfusion index, DPI, which is close to HPI for the entire liver) is markedly elevated in patients with overt metastases and elevated in patients with occult metastases. For example, Leen et al.46 showed that the 5-year survival of patients after potentially curative colon surgery for colorectal carcinoma was 91% in patients with normal hepatic Doppler perfusion index values and 29% in patients

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with abnormal hepatic DPI values. The technique, however, depends highly on the skills of the investigators, and is difficult to replicate,47 with overlapping values in groups of patients with and without overt metastases. In an experimental animal model of hepatic metastases, Yarmenitis et al.48 demonstrated that metastases were first encountered on day 4 as small groups of cells in the connective tissue of the porta hepatis and the portal triads, without apparent vascular association, and were associated with a clear elevation of arterial flow and DPI and a subtle reduction in portal flow.

Contrast-enhanced dynamic MRI More recently, Totman et al.49 showed, by using contrast-enhanced MRI with Gd-DTPA (gadopentetic acid), that patients with overt colorectal liver metastases had higher HPI than that of normal controls, but they did not test the method on occult liver metastases.

PHYSIOPATHOLOGY OF MICROCIRCULATION ALTERATIONS IN METASTATIC LIVER All the imaging techniques have shown similar flow alterations in liver bearing visible metastases. The main finding is an increase in HPI (or DPI with Doppler), which appears to be mostly due to a decrease in portal perfusion, and to a lesser extent to an increase in arterial perfusion. Such a relative increase in arterial supply in the environment of liver metastases had already been described in 1954.50 An increase in MTT and a decrease in distribution volume are also found. Surprisingly, significant intrahepatic flow alterations also occur prior to the growth of visible metastases. These hemodynamic changes are similar but more discrete than those found in the presence of large metastases. The precise cause of these changes, however, remains only partly explained. Both biomechanical and molecular mechanisms appear to be implicated. Humoral mediators have been implicated.51 The presence of circulating vasoactive agents has been suggested by changes induced in splanchnic hemodynamics in rats by injecting plasma samples from patients or rats bearing tumors,52,53 but the nature of these mediators is currently not known.

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In vivo videomicroscopy Direct observation using in vivo videomicroscopic techniques has made it possible to study in live animals the flow alterations in hepatic metastases and in the liver during their formation.54–57 Large tumor nodules contain irregularly dilated, tortuous, neoangiogenic vascular networks. Blood flow velocity and direction vary in this network, accounting for the increase of the mean transit time. The nodules induce direct mechanical compression on the surrounding sinusoids, which are narrowed, and resist portal low-pressure flow. The portal venules and the sinusoids are obliterated abruptly at the borders of the growing metastatic foci,55 and most flow is stopped, accounting for the drop in portal flow within the tumor and for the portal hypertension in tumor-bearing liver. The drop in portal flow is compensated by arterial flow58 via the arterial buffer effect, accounting for the increase in HPI as well as the peritumoral rim and the transient segmental enhancements that can be observed during the arterial phase on CT and MRI.59 Moreover, although metastases smaller than 200 µm receive their blood supply via the sinusoids, without hepatic arterial neovascularization,60 when tumors progress to a point beyond which angiogenesis is necessary to sustain growth, neovascularization occurs,61 assisted by vascular endothelial growth factor expression.62 Since significant intrahepatic flow alterations occur in livers before metastases become visible, mechanisms other than extrinsic compression of sinusoids must be implicated. Intravital microscopy during the formation of hepatic metastases showed that several intrahepatic microhemodynamic events occur prior to the establishment of visible metastases:57

• blocked metastatic cells within the sinusoids56 • increased post-sinusoidal leukocyte rolling and adherence, and stasis of leukocytes in the sinusoids

• reduced wall shear rate (velocity gradient perpendicular to the direction of fluid flow) • and, finally, a reduction in blood flow in the hepatic sinusoids. Metastatic cells coming through the portal route are arrested at the inflow side of the microvascular bed due to size restriction.56 Microthrombi form in the portal vessels55,60 and increase resistance in the low-pressure vascular bed, which accounts for the increased MTT and the decreased portal flow. Kruskal et al.57 showed that when compared with normal livers, a reduction in sinusoidal flow velocity and

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flow rate began as early as day 2 after the injection of tumor cells in the portal system of mice that developed hepatic metastases, and they noted a significant decrease in post-sinusoidal leukocyte velocity and an increase in the number of stagnant adherent leukocytes, associated with a decrease in wall shear rate. To resist substantial wall shear stress exerted by blood flow, metastasizing cells have to form adhesive contacts with endothelial cells. The reduced shear rate increases the likelihood of interactions between leukocytes (and tumor cells) and endothelial receptors, favoring formation of metastases. In return, increased leukocyte rolling and adherence narrow the sinusoids and portal venules and compromise flow. Moreover, cell membrane molecules such as the carcinoembryonic antigen, present in certain cancer cells including colon cancer cells, bind to Kupffer cells by specific receptors, and activate them. Activated Kupffer cells can release vasoactive cytokines that activate stellate cells, which impair sinusoidal perfusion by modulating the sinusoidal caliber. The increase in high-pressure arterial flow which partly compensates for the portal flow decrease may be due to the buffer effect, or to humoral factors also secreted by tumoral cells. The low distribution volume of the contrast medium within the tumor suggests that the capillary density is lower than in the normal liver, and accounts for the fact that metastases from colon and breast cancers usually appear to be less enhanced than normal liver after contrast medium injection.55

LIMITATIONS OF CT PERFUSION IN THE LIVER CT perfusion imaging of the liver has the potential to improve detection and characterization of liver diseases; however, several challenges remain. The main limitations result from motion in the abdomen.63 Although the arterial input function is easily measurable in the abdominal aorta, the portal input function is much more difficult to obtain due to breathing artifacts. A vessel tracking strategy, using imaging processing techniques, can greatly improve the shape of the portal enhancement curve and therefore the model adjustment (unpublished data). Respiratory motion also makes it difficult to analyze nodules in the liver. Two strategies can be applied to avoid this limitation: respiration gating and compensation for respiratory misregistration.64 Respiration gating, however, implies a low acquisition rate, which may compromise the separation of the arterial and the portal contributions to the liver perfusion.

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Retrospective motion correction is difficult to achieve in the liver due to elastic deformation of the organ during the respiration cycle. Therefore, rigid three-dimensional (3D) motion correction algorithms, which are very efficient in the brain, must be replaced by more complex, non-rigid algorithms. These will be easier to run in large volumes acquired with millimetric slices obtained with the newer multidetector CT systems. Another limitation comes from the high number of parameters that are required to model the dual liver microcirculation if the capillary leak is to be calculated in tumor nodules. In liver perfusion models, measurements are performed during the first pass of the contrast (avoiding the capillary leak), or are based on the hypothesis that the leak in the interstitial space of Disse is almost immediate due to fenestration of the sinusoids (giving a hepatic distribution volume instead of a hepatic blood volume), or that the space of Disse is negligible (accounting for less than 10% of the total liver volume). Such hypotheses, however, do not hold in the case of liver nodules or during chronic liver diseases, which modify the sinusoid permeability and the interstitial volume. Therefore, more complicated models are needed.65,66 Finally, since the liver is approximately 15 cm high, it is not possible to cover the entire organ even with the larger CT detectors during a single sequential acquisition. Two strategies can be developed to elude this limitation: the patient can be moved during the dynamic acquisition, or two or more consecutive dynamic acquisitions can be performed. These strategies, however, have to be validated.

CONCLUSION Functional CT of liver perfusion is a sensitive and specific technique, which can yield objective and quantitative physiological parameters and allow repeated longitudinal studies. However, continued improvements and more validation studies are needed to be able to use it on a routine basis. Many cancer patients undergo CT imaging surveillance of the liver for early identification of tumor recurrence following primary treatment. For colorectal cancer, intensifying follow-up in this way is associated with reduced mortality,67 and the American Society of Clinical Oncology now recommends annual CT of the chest and abdomen for 3 years after primary therapy in patients at higher risk of recurrence.34 CT perfusion could be readily incorporated into such surveillance programs. The ability to identify a high risk of metastatic liver involvement in a

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frequent disease such as colorectal cancer may help in the decision whether to use adjuvant chemotherapy, and may avoid unnecessary treatments in patients who are at low risk of liver metastases.

REFERENCES 1. Paley MR, Ros P. Hepatic metastases. Radiol Clin North Am 1998; 36: 349–363. 2. Baker ME, Pelley R. Hepatic metastases: basic principles and implications for radiologists. Radiology 1995; 197: 329–37. 3. Finlay IG, McArdle CS. Occult metastases in colorectal carcinoma. Br J Surg 1986; 73: 732–5. 4. Kinkel K, Lu Y, Both M, Warren RS, Thoeni RF. Detection of hepatic metastases from cancers of the gastrointestinal tract by using noninvasive imaging methods (US, CT, MR imaging, PET): a meta-analysis. Radiology 2002; 224: 748–56. 5. Greenway B. Hepatic metastases from colorectal cancer: resection or not. Br J Surg 1988; 75: 513–19. 6. Moertel CG. Drug therapy: chemotherapy for colorectal cancer. N Engl J Med 1994; 330: 1136–42. 7. Cooke DA, Parkin A, Wiggins P, Robinson PJ, Giles GR. Hepatic perfusion index and the evolution of liver metastases. Nucl Med Commun 1987; 8: 970–4. 8. Nott DM, Grime JS, Yates J et al. Changes in the hepatic perfusion index during the growth and development of experimental hepatic micrometastases. Nucl Med Commun 1987; 8: 995–1000. 9. Perkins AC, Whalley DR, Ballantyne KC, Hardcastle JD. Reliability of the hepatic perfusion index for the detection of liver metastases. Nucl Med Commun 1987; 8: 982–9. 10. Leen E, Goldberg JA, Robertson J et al. Image-directed Doppler ultrasonography: a novel technique for the diagnosis of colorectal liver metastases. J Clin Ultrasound 1993; 21: 221–30. 11. Platt JF, Francis IR, Ellis JH, Reige KA. Liver metastases: early detection based on abnormal contrast material enhancement at dual-phased helical CT. Radiology 1997; 205: 49–53. 12. Thimgan MS, Yee HF Jr. Quantitation of rat hepatic stellate cell contraction: stellate cells, contribution to sinusoidal resistance. Am J Physiol Gastrointest Liver Physiol 1999; 277: 137–43. 13. Shah V, Haddad FG, Garcia-Cardena G, et al. Liver sinusoidal endothelial cells are responsible for nitric oxide modulation of resistance in the hepatic sinusoids. J Clin Invest 1997; 100: 2923–30. 14. Lautt WW, Legare DJ, Almeida MS. Adenosine as putative regulator of hepatic arterial flow (the buffer reponse). Am J Physiol 1985; 248: 331–8. 15. Lautt WW. The 1995 Ciba-Geigy Award Lecture. Intrinsic regulation of hepatic blood flow. Can J Physiol Pharmacol 1996; 74: 223–33. 16. Pandharipande PV, Krinsky GA, Rusinek H, Lee VS. Perfusion imaging of the liver: current challenges and future goals. Radiology. 2005; 234: 661–73. 17. Miles KA, Hayball MP, Dixon AK. Functional images of hepatic perfusion obtained with dynamic CT. Radiology 1993; 188: 405–11.

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18. Dugdale PE, Miles KA. Hepatic metastases: the value of quantitative assessment of contrast enhancement on computed tomography. Eur J Radiol 1999; 30: 206–13. 19. Blomley M, Coulden R, Dawson P. Liver perfusion studied with ultrafast CT. J Comput Assist Tomogr 1995; 19: 424–33. 20. Tsushima Y, Blomley JK, Kusano S, Endo K. The portal component of hepatic perfusion measured by dynamic CT: an indicator of hepatic parenchymal damage. Dig Dis Sci 1999; 44: 1632–8. 21. Miles KA, McPherson SJ, Hayball MP. Transient splenic inhomogeneity on contrast enhanced CT: mechanism and effect of liver disease. Radiology 1995; 194: 91–5. 22. Leggett DA, Kelley BB, Bunce IH, Miles KA. Colorectal cancer: diagnostic potential of CT measurements of hepatic perfusion and implications for contrast enhancement protocols. Radiology 1997; 205: 716–20. 23. Miles KA, Leggett DA, Kelley BB et al. In vivo assessment of neovascularization of liver metastases using perfusion CT. Br J Radiol 1998; 71: 276–81. 24. Halavaara JT, Hamberg LM, Leong FS et al. Functional CT with an experimental intravascular contrast agent in the assessment of liver vascular physiology. Acad Radiol 1996; 3: 946–52. 25. Materne R, Van Beers BE, Smith AM et al. Non-invasive quantification of liver perfusion with dynamic computed tomography and a dual-input onecompartmental model. Clin Sci (Lond) 2000; 99: 517–25. 26. Van Beers BE, Leconte I, Materne R et al. Hepatic perfusion parameters in chronic liver disease: dynamic CT measurements correlated with disease severity. AJR Am J Roentgenol 2001; 176: 667–73. 27. Cuenod C, Leconte I, Siauve N et al. Early changes in liver perfusion caused by occult metastases in rats: detection with quantitative CT. Radiology 2001; 218: 556–561. 28. Cuenod CA, Leconte I, Siauve N et al. Deconvolution technique for measuring tissue perfusion by dynamic CT: application to normal and metastatic liver. Acad Radiol 2002; 9: S205–11. 29. Fournier LS, Cuenod CA, de Bazelaire C et al. Early modifications of hepatic perfusion measured by functional CT in a rat model of hepatocellular carcinoma using a blood pool contrast agent. Eur Radiol 2004; 14: 2125–33. 30. Axel L. Tissue mean transit time from dynamic computed tomography by a simple deconvolution technique. Invest Radiol 1983; 18: 94–19. 31. Zierler KL. Equation for measuring blood flow by external monitoring of radioisotopes. Circ Res 1965; 16: 309–21. 32. Miles KA, Kelley BB. Altered perfusion adjacent to hepatic metastases. Clin Radiol 1997; 52: 162–3. 33. Tsushima Y, Blomley MJ, Yokoyama H, Kusano S, Endo K. Does the presence of distant and local malignancy alter parenchymal perfusion in apparently disease-free areas of the liver? Dig Dis Sci 2001; 46: 2113–19. 34. Desch CE, Benson AB 3rd, Somerfield MR et al. American Society of Clinical Oncology. Colorectal cancer surveillance: 2005 update of an American Society of Clinical Oncology practice guideline. J Clin Oncol, 2005; 23: 8512–19. 35. Compton CC, Fielding LP, Burgart LJ et al. Prognostic factors in colorectal cancer. College of American Pathologists Consensus Statement 1999. Arch Pathol Lab Med 2000; 124: 979–94.

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36. Garcea G, Sharma RA, Dennison A et al. Molecular biomarkers of colorectal carcinogenesis and their role in surveillance and early intervention. Eur J Cancer 2003; 39: 1041–52. 37. Smith JJ, Sorensen AG, Thrall JH. Biomarkers in imaging: realizing radiology’s future. Radiology 2003; 227: 633–8. 38. Mooteri S, Rubin D, Leurgans S et al. Tumor angiogenesis in primary and metastatic colorectal cancers. Dis Colon Rectum 1996; 39: 1073–80. 39. Miles KA, Colyvas K, Griffiths MR, Bunce IH. Colon cancer: Risk stratification using hepatic perfusion CT. European Radiology 2004; 14: Suppl 2, 129. 40. Fuentes MA, Keith CJ, Griffiths M, Durbridge G, Miles KA. Hepatic haemodynamics: interrelationships between contrast enhancement and perfusion on CT and Doppler perfusion indices. Br J Radiol 2002; 75: 17–23. 41. Ballantyne KC, Charnley RM, Perkins AC et al. Hepatic perfusion index in the diagnosis of overt metastatic colorectal cancer. Nucl Med Commun 1990; 11: 23–8. 42. Hugier M, Maheswari S, Toussaint P et al. Hepatic flow scintigraphy in evaluation of hepatic metastases with gastrointestinal malignancy. Arch Surg 1993; 128: 1057–9. 43. Carter R, Hemingway D, Cooke TG et al. A prospective study of six methods for detection of hepatic colorectal metastases. Ann R Coll Surg Engl 1996; 78: 27–30. 44. Leen E, Goldberg JA, Anderson JR et al. Hepatic perfusion changes in patients with liver metastases: comparison with those patients with cirrhosis. Gut 1993; 34: 554–7. 45. Kissel A, Rixe O, Methlin A et al. Quantification of hepatic arterial and portal venous flow using ultrasound contrast agents for early detection of liver metastases of colorectal cancers. J Radiol 2001; 82: 1621–5. 46. Leen E, Goldberg JA, Angerson WJ, McArdle CS. Potential role of Doppler perfusion index in selection of patients with colorectal cancer for adjuvant chemotherapy. Lancet 2000; 355: 34–37. 47. Roumen RM, Scheltinga MR, Slooter GD, van der Linden AW. Doppler perfusion index fails to predict the presence of occult hepatic colorectal metastases. Eur J Surg Oncol 2005; 31: 521–7. 48. Yarmenitis SD, Kalogeropoulou CP, Hatjikondi O et al. An experimental approach of the Doppler perfusion index of the liver in detecting occult hepatic metastases: histological findings related to the hemodynamic measurements in Wistar rats. Eur Radiol 2000; 10: 417–24. 49. Totman JJ, O’Gorman RL, Kane PA, Karani JB. Comparison of the hepatic perfusion index measured with gadolinium-enhanced volumetric MRI in controls and in patients with colorectal cancer. Br J Radiol 2005; 78: 105–9. 50. Breedis C, Young G. The blood supply of neoplasms in the liver. Am J Pathol 1954; 30: 969–77. 51. Fong Y. Doppler perfusion index in colorectal cancer. Lancet 2000; 355: 5–6. 52. Warren HW, Anderson WJ, Leen E et al. Haemodynamic changes associated with colorectal liver metastases: evidence of a humoral mediator. Br J Surg 1993; 80: 1561(abstr). 53. Carter R, Anderson JH, Cooke TG, Baxter JN, Angerson WJ. Splanchnic blood flow changes in the presence of hepatic tumour: evidence of a humoral mediator. Br J Cancer 1994; 69: 1025–6.

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54. Ridge JA, Bading JR, Gelbard AS, Benua RS, Daly J. Perfusion of colorectal hepatic metastases: relative distribution from the hepatic artery and portal vein. Cancer 1987; 59: 1547–53. 55. Kan Z, Ivancev K, Lunderquist A et al. In vivo microscopy of hepatic tumors in animal model: a dynamic investigation of blood supply to hepatic metastases. Radiology 1993; 187: 621–6. 56. Morris VL, MacDonald IC, Koop S et al. Early interaction of cancer cells with the microvasculature in mouse liver and muscle during hematogenous metastasis: videomicroscopic analysis. Clin Exp Metastasis 1993; 11: 377–50. 57. Kruskal JB, Thomas P, Kane RA, Goldberg SN. Hepatic perfusion changes in mice livers with developing colorectal cancer metastases. Radiology 2004; 231: 482–90. 58. Kopljar M, Brkljacic B, Doko M, Horzic M. Nature of Doppler perfusion index changes in patients with colorectal cancer liver metastases. J Ultrasound Med 2004; 23: 1295–300. 59. Tian JL, Zhang JS. Hepatic perfusion disorders: etiopathogenesis and related diseases. World J Gastroenterol 2006 May 28; 12: 3265–70. 60. Haugeberg G, Strohmeyer T, Lierse W. The vascularization of liver metastases. J Cancer Res Clin Oncol 1988; 114: 415–19. 61. Terayama N, Terada T, Nakanuma Y. A morphometric and immunohistochemical study on angiogenesis of human metastatic carcinomas of the liver. Hepatology 1996; 24: 816–19. 62. Takeda A, Stoeltzing O, Ahmad SA et al. Role of angiogenesis in the development and growth of liver metastasis. Ann Surg Oncol 2002; 9: 610–16. 63. Bader TR, Grabenwoger F, Prokesch RW, Krause W. Measurement of hepatic perfusion with dynamic computed tomography: assessment of normal values and comparison of two methods to compensate for motion artifacts. Invest Radiol 2000; 35: 539–47. 64. Nakashige A, Horiguchi J, Tamura A et al. Quantitative measurement of hepatic portal perfusion by multidetector row CT with compensation for respiratory misregistration. Br J Radiol 2004; 77: 728–34. 65. Kapanen MK, Halavaara JT, Hakkinen AM. Assessment of vascular physiology of tumorous livers: comparison of two different methods. Acad Radiol 2003; 10: 1021–9. 66. Kapanen MK, Halavaara JT, Hakkinen AM. Open four-compartment model in the measurement of liver perfusion. Acad Radiol 2005; 12: 1542–50. Erratum in: Acad Radiol 2006; 13: 270. 67. Jeffrey GM, Hickey BE, Hider P. Follow-up strategies for patients treated for non-metastatic colorectal cancer (Cochrane Review). In: The Cochrane Library, Issue 2, 2003. Oxford: Update Software, 2003.

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12 Beyond RECIST: CT perfusion in evaluating treatment response and complications Natalie Charnley and Kenneth A Miles

INTRODUCTION Response assessment in oncology, using conventional morphological imaging, relies on tumor shrinkage. This assessment has been standardized using the Response Evaluation Criteria in Solid Tumors (RECIST), which have recently been updated to reflect more accurately the change in volume of spherical lesions.1 Despite this change, RECIST measurements are prone to errors, are particularly difficult for small lesions, and are laborious. Furthermore malignant lesions may be difficult to distinguish from areas of fibrosis.1 Functional imaging using perfusion computed tomography (CTP), dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), [15O]H2O-positron emission tomography (PET), or ultrasound (US) may provide more specific information about changes in tumor biology. Tumor shrinkage may be a valid endpoint for established cytotoxic drugs, but may not be relevant for biological agents under development, which are often cytostatic. In addition, functional imaging may be more sensitive in detecting an early response to treatment and can potentially identify subpopulations of patients enriched for response. Perfusion describes the flow of blood through tissues, and is governed by the tumor vasculature. Tumor blood vessels have structural and functional abnormalities including irregular branching, increased permeability, and independence from normal flow control mechanisms. This leads to variable and inadequate delivery of nutrients to the tumor tissue. Increased permeability of tumor vasculature with respect to normal tissue causes differential uptake of contrast agents, which is seen on functional imaging.

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The importance of tumor vasculature in supporting growth and the associated therapeutic implications have long been recognized.2 Several agents, including conventional cytotoxic drugs, can effect changes in tumor vasculature.3,4 Over the past decade, there has been increasing interest in the development of specific antiangiogenic drugs and vascular disrupting agents (VDAs), which impair angiogenesis and destroy existing vasculature, respectively. There is an increasing need for direct imaging of the biological endpoints of these drugs, in particular tumor perfusion and endothelial permeability. Furthermore, vascular changes have been observed in tumors following radiotherapy.5,6 There is interest in monitoring these changes to predict radiation response and also in assessing perfusion of normal tissue in the evaluation of radiotherapyinduced complications. Computed tomography (CT) remains the mainstay technique for response evaluation in oncology, and is widely used for RECIST measurements. CT perfusion can be readily included as part of a conventional CT examination to provide a widely available and relatively cheap means of obtaining a simultaneous functional response assessment. Interestingly, there are no data available for the in vivo reproducibility of manual RECIST measurements in which the same subject has undergone CT twice (test–retest protocol). Yet it is well recognized that differences in slice positioning can produce apparent changes in tumor size. Even repeated size measurements from the same tumor study show considerable variability.7 The test–retest performance of CT perfusion is discussed more fully in Chapter 2. Nevertheless, the coefficient of repeatability expressing the 95% confidence limits of individual CT perfusion measurements in humans has been reported to be 28% for normal brain (Griffiths, personal communication) and 65% for rectal cancer.8 CTP is especially well suited to assessing the response to agents which act on tumor vasculature. CTP can provide a range of parameters relevant to tumor perfusion, namely blood flow (BF), blood volume (BV), mean transit time (MTT), and permeability surface area (PS), and can provide quantitative data. Commercial analysis software approved by the US Food and Drug Administration (FDA) is now available, which makes data analysis more feasible. Importantly, studies have shown that CTP-derived parameters correlate with histological measurements of angiogenesis.9,10,40

CT PERFUSION IN ASSESSING TUMOR RESPONSE TO DRUGS WHICH AFFECT TUMOR VASCULATURE Some of the earliest studies of CTP in oncology investigated agents which affect the blood–brain barrier (BBB) in patients with brain tumors.

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In one of these studies, perfusion parameters were investigated in 10 patients with primary brain tumors after a week of oral dexamethasone.3 Both the transfer constant and the plasma volume within the tumors decreased with steroid treatment. Interestingly, when enhancement was assessed on contrast-enhanced CT scans, no change in enhancement was visible in the majority of cases. A further study of CTP in patients with brain tumors assessed BBB permeability following administration of the agent RMP-7, a bradykinin analog.11 Chemotherapeutic agents traditionally have poor penetration of the BBB, and so a drug which can break down the tumor BBB would potentially improve delivery of the cytotoxic agent. CTP demonstrated an increase in BBB permeability with RMP-7 (Figure 12.1). On the background of this finding, RMP-7 has been taken forward into further clinical development with the cytotoxic carboplatin, and is now in phase II trials.12 Tumor hypoxia adversely affects prognosis, and is associated with reduced survival following surgery or radiotherapy.13,14 Drugs are available which can preferentially target and destroy hypoxic cells. BW12C is an agent which binds preferentially to oxyhemoglobin and causes reduced tissue oxygenation.15 Thirty-two patients with advanced gastrointestinal cancer were recruited to a phase I trial of BW12C plus a standard cytotoxic, mitomycin C, to assess effects of the drug on oxygenation and tumor perfusion.15 Infusion of BW12C was followed after 2 hours by imaging with CTP. In five out of six patients who were eligible for the imaging study perfusion was reduced by 30%, though

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12 Permeability (µl/min/ml)

Permeability (µl/min/ml)

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100 80 60 40 20

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0 Baseline With RMP-7

Baseline With RMP-7

Figure 12.1 Changes in the vascular permeability of gliomas (a) and normal brain tissue (b) following RMP-7 assessed using computed tomography perfusion (CT). (Data from reference 11)

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2

ml/min/ml

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1

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Figure 12.2 Changes in tumor perfusion following BW12C assessed by CT perfusion. (Reproduced with permission from reference 16)

this change was not significant. However, the greatest reductions in perfusion were seen in those tumors exhibiting higher baseline values (Figure 12.2). There have been several studies where functional imaging has been used to assess vascular response to conventional cytotoxic agents. Many of these studies have been in the neoadjuvant treatment of breast cancer, where the rationale is to establish a biomarker of response to standard chemotherapy prior to surgery.4,17 A single study has used CTP for this purpose.18 Dynamic CT was performed in 19 patients prior to and following standard cytotoxic treatment with anthracycline and taxane, and contrast enhancement was compared with residual tumor extent postoperatively. Perfusion parameters were not actually derived in this study, but the pattern of contrast enhancement at early and late phases was assessed. For late-phase CTP, the accuracy of detecting residual disease was 100% (7/7) where there was diffuse contrast enhancement in a whole quadrant prior to chemotherapy, and 82% (9/11) where contrast enhancement was localized. The ability of CTP to assess the response of squamous cell carcinomas of the oropharynx to induction chemotherapy has been recently assessed by Gandhi et al.19 CTP was performed in a series of nine patients of stage 3 or 4, before and 3 weeks after one cycle of induction chemotherapy. A reduction in BV of 20% or more showed a substantial agreement with clinical response assessed by endoscopy. Two small studies have also highlighted the potential for CTP to evaluate

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a

b

Figure 12.3 CT perfusion images of non-small-cell lung cancer before (a) and after (b) chemotherapy

the treatment response in lung cancer (Figure 12.3). Choi et al. measured peak tumor enhancement in residual masses in seven patients with small-cell cancer following chemotherapy.20 Peak enhancement values increased in four, decreased in one, and were unchanged in two. All four patients demonstrating an increase in enhancement were reported to exhibit improvement. As part of a larger study, Kiessling et al. presented a single case of non-small-cell lung cancer in which a partial response on size criteria was associated with a reduction in perfusion.21

CT PERFUSION IN ASSESSING TUMOR RESPONSE TO NOVEL ANTIVASCULAR AGENTS IN PHASE I TRIALS Standard clinical development of novel drugs begins in phase I trials. The main purpose of these is to ascertain the range of toxic effects of that drug, and its maximum tolerated dose (MTD), thought to be the most effective dose. The pharmacokinetics and pharmakodynamics of

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that agent are also assessed, as are early response data. MTD is relevant to treatment with cytotoxic drugs, but not to many biological agents such as antiangiogenic drugs and VDAs. Furthermore, there has been no correlation between the activity of antiangiogenic agents and their toxicity.22 Instead, surrogate endpoints must be investigated. Angiogenesis is the formation of new tumor blood vessels, essential for tumor growth from 1 mm3 in size.2 The trigger for angiogenesis is additionally determined by hypoxia, acidosis, and hypoglycemia.23 The process of angiogenesis is tightly coordinated by a network of cytokines. Vascular endothelial growth factor (VEGF) stimulates angiogenesis and also has effects on vascular permeability. Fibroblast growth factor (FGF) and platelet-derived growth factor (PDGF) also have a role in angiogenesis, but are not vascular endothelium-specific. Integrins such as αvβ3, which is expressed selectively on activated tumor vasculature, potentiate angiogenesis and enable endothelial cell migration towards the tumor. Many of the steps in the angiogenic pathway have been targeted by novel antiangiogenic drugs, and several of these are in clinical development.22,24–27 Several of these trials compare CTP to other functional imaging such as DCE-MRI, and also to histology, in proof of principle studies. A summary of these trials is given in Table 12.1. Willett’s group25 conducted a phase I trial of of bevacizumab, a VEGFspecific antibody, in six patients with rectal adenocarcinoma. Patients received a regimen of preoperative bevacizumab and chemoradiotherapy. Perfusion was assessed initially 12 days following a single infusion of the antiangiogenic agent. CTP revealed a significant decrease in tumor perfusion of 40–44% and of blood volume of 16–39%, in four out of five patients assessed. This was associated with a significant decrease in tumor microvessel density (MVD). In addition, a fall in tumor interstitial fluid pressure, and an increase in vessels positive for smooth muscle actin, were observed. The authors suggest that this is supportive of vascular normalization with bevacizumab. Meijerink et al.24 assessed the perfusion of a range of advanced solid tumors in a phase I trial of 13 patients using a combination of agents: gefitinib, a tyrosine kinase inhibitor of epidermal growth factor receptor 1 (EGFR1), and the antiangiogenic agent AZD21271, a tyrosine kinase inhibitor of VEGFR1–3. Both agents were given daily. CTP was performed prior to starting treatment, and 4–6 weeks following treatment. A longer scanning protocol was used for patients with liver metastases compared to other sites, in order to determine the arterial and portal fractions of tumor blood flow separately. In five out of six patients with extrahepatic masses, there was an average decrease in perfusion of

10 4 13

21 5 25 6

6 19

Yeung, 19943 Ford, 199611 Meijerink, 200624

Thomas, 200327 Willett, 200425 McNeel, 200526 Xiong, 200422

Falk, 199415 Tozaki, 200418

BW12C Anthracycline + taxane

Endostatin Bevacizumab MEDI-522 SU6668

Dexamethasone RMP-7 Gefitinib + AZD2171

Treatment

GI Breast

Various Rectum Various Various

Brain Brain Various

Tumor site

Binds to OHb Neoadjuvant cytotoxic

AA AA AA AA

Steroid Bradykinin analog AA

Agent

8 weeks 12 days 8 weeks 4 weeks, 12 weeks 1h NSt

1 week 1h 4–6 weeks

Follow-up

NS ↓tumor BF in 5/6 patients 82–100% accurate in detecting residual disease postoperatively

↓Transfer constant, ↓plasma volume ≠BBB permeability ↓BF in 5/6 with extrahepatic mets, reversible ↓perfusion in hepatic mets ↓BF in 4 patients 4/5 ↓tumor BF, BV, MVD ≠MTT with ≠doses of drug 5/6 ↓flow on CT

CTP findings

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GI, gastrointestinal; AA, antiangiogenic agent; OHb, oxyhemoglobin; NSt, not stated: assessment made after 4–6 cycles of chemotherapy, likely to be 12–18 weeks; BBB, blood–brain barrier; BF, blood flow; mets, metastases; BV, blood volume; MVD, microvessel density; MTT, mean transit time; NS, non-significant

n

Monitoring drug treatment using perfusion CT (CTP)

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Table 12.1

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18% followed by a plateau. In liver metastases, an initial average decrease in perfusion of 39% was observed followed by a return to previous hepatic artery blood flow. Importantly, no correlation was seen between changes in tumor size as assessed by RECIST and change in perfusion. MEDI-522, a monoclonal antibody to the αvβ3 integrin expressed selectively on tumor neovasculature, was evaluated in a phase I trial of 25 patients with a variety of solid tumors.26 Patients underwent CTP at baseline and following 8 weeks of weekly MEDI-522 given as a range of doses. Tumor blood flow, blood volume, mean transit time, and permeability surface area were also assessed. Only 11 patients had tumors which satisfied the size criteria for follow-up CT scanning. A positive relationship was seen between increasing dose of drug and mean transit time, but not the other parameters. This would suggest a MEDI-522related reduction in blood flow through small tumor vessels. No complete or partial responses were observed by RECIST criteria, but three patients experienced prolonged stable disease. There was no correlation between CT parameters and MEDI-522 serum concentration. Endostatin is an endogenous antiangiogenesis agent. A phase I clinical trial of recombinant human endostatin was conducted by Thomas’s group27 in 21 patients with advanced solid tumors. Patients received the drug daily for a 28-day cycle, and at 8 weeks underwent imaging with CTP, DCE-MRI, fluorodeoxyglucose (FDG)-PET, and US. In four patients, a reduction in tumor perfusion was seen on the CT time–attenuation curve, which was suggestive of a decrease in microvessel density. No changes were seen on MRI or US at this time, and there was no change in MVD to corroborate the CT findings. In addition, the change in the CTP time–attenuation curves did not predict response to treatment. Xiong et al.22 investigated the pharmacokinetics and pharmacodynamics of SU6668, a novel tyrosine kinase inhibitor of VEGFR, fibroblast growth factor receptor (FGFR), and platelet-derived growth factor receptor (PDGFR). The drug was given daily in a 4-week cycle, and tumor perfusion was assessed by CTP and DCE-MRI at 4 and 12 weeks. Five out of six evaluable patients had a reduction in tumor blood flow on CTP at 4 and 12 weeks. Two out of four patients showed an equivalent response on MRI. Changes in functional imaging were not matched with any clinical responses in this study. Unfortunately, several of these trials have had small numbers of patients, and sometimes have not had the power to detect significant changes in perfusion parameters. It is difficult to make comparisons between results of phase I trials. Different perfusion parameters are often chosen for these studies, and the specific CTP protocol and data

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model used are not always described. However, none of the above studies in which multiple parameters were studied showed significant changes in vascular permeability on treatment, even when significant changes in other parameters had been observed.22,25,26 This finding is interesting, considering the importance of VEGF not only in stimulating angiogenesis but also in determining capillary leakiness. CT measurements of permeability effectively describe the rate of transfer of contrast material between intravascular and extravascular compartments. It is feasible that in some circumstances, a decrease in transport of contrast material due to a treatment-induced reduction in vascular permeability may be offset by an increase in transport resulting from a concomitant reduction in tumor interstitial fluid pressure. When evaluating tumor response, the time scale for imaging is of paramount importance. Frequently, changes are assessed at times when we would expect to see a response on conventional cross-sectional imaging, but this may not be the most appropriate time point for antiangiogenic drugs and VDAs. Response should not be assessed too early, as during the first few days following an antiangiogenic drug, it is thought that the vasculature undergoes some normalization,28 which may lead to a reduction in interstitial fluid pressure and an initial possible increase in perfusion. However, assessing at late time points may cause a change in tumor size and difficulty in co-registering the regions of interest.

CT PERFUSION IN ASSESSING TUMOR RESPONSE TO RADIOTHERAPY Radiotherapy has the potential to cause changes in tumor and normal tissue vasculature by its ability to cause breaks in DNA. The ability of radiation to damage blood vessels has long been recognized, as evidenced by the late effect of radiation-induced telangiectasia. In the case of vessel damage, it would be expected that tumor and normal tissue blood flow would decrease. However, paradoxically, an early effect of radiation could be an increase in blood flow, brought about by radiotherapy-induced inflammation, and also a decrease in tumor interstitial pressure which may open compressed tumor vessels and allow reperfusion. Three studies have used CTP to monitor the effects of radiotherapy on tumor vasculature and response to treatment. As with monitoring drug response, the direction of change in perfusion parameters is affected by the timing of assessment. Harvey’s group investigated tumor blood

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flow in 22 patients with carcinoma of the prostate, both 1–2 weeks and 6–12 weeks following completion of treatment.29 Increases in perfusion, contrast agent clearance, and fractional vascular volume were observed 1–2 weeks following treatment, and these were maintained at the later time point. Increases in contrast agent clearance and fractional vascular volume, but not perfusion, were also seen at the same time points in other tumor types.30 CTP has also been used to assess patients undergoing combined modality treatment with chemoradiotherapy.31 Nine patients with rectal carcinoma underwent a 6–8-week course of chemoradiation prior to surgery. Patients were assessed following completion of treatment. A reduction in BF and increase in MTT were observed, suggesting damage to vasculature. BV and PS remained constant. Response to other forms of radiation treatment can also be studied using CTP. Bondestam et al.32 have investigated perfusion parameters in six patients with meningiomas, undergoing brachytherapy treatment by stereotactic implantation of iodine-125 seeds. A 41% reduction in tumor blood flow was seen 3 months following the procedure using CTP imaging.

OTHER THERAPIES Other tumor treatment modalities evaluated by CT perfusion include transcatheter arterial chemoembolization and laser thermal therapy. Tsushima et al. used CT perfusion to study the changes in hepatic perfusion following transcatheter arterial chemoembolization of liver tumors using adriamycin and lipiodol.33 Mean arterial perfusion in four tumors fell from 90 ml/min/100 ml to 28 ml/min/100 ml. The potential utility of CT perfusion for monitoring laser thermal therapy has been demonstrated in animals.34 The mean decrease in tumor perfusion was 41 ± 14%. An even greater reduction in tumor perfusion (64 ± 10%, p < 0.001) was produced by hypocapnia, which minimized heat dissipation through blood flow, resulting in a significantly larger thermal lesion.

IDENTIFICATION OF SUBPOPULATIONS ENRICHED FOR RESPONSE The relationship between tumor vascularity and treatment response is complex. Highly vascularized tumors tend to be more aggressive, and therefore potentially less likely to respond to treatment. However, a

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well-developed tumor blood supply is also essential for the effective delivery of chemotherapeutic drugs to tumor tissue, and thus poorly perfused tumors may also be considered less likely to respond. Furthermore, low levels of perfusion and/or a heterogeneous vascular network are frequently associated with areas of tumor hypoxia, a feature that is known to confer resistance to not only chemotherapy but also radiotherapy. Thus, CT perfusion measurements have the potential to predict tumor response to chemotherapy or radiotherapy with low and/or heterogeneous contrast enhancement and perfusion, implying a likelihood of an unfavorable outcome. Three preliminary studies illustrate the potential for CT perfusion to identify patients less likely to respond to chemotherapy. In a cohort of patients with small-cell lung cancer, Choi et al. determined peak contrast enhancement from spiral CT acquisitions performed at 40 s and 2–3 min following a 120-ml bolus of contrast medium (300 mg/ml).20 Peak tumor enhancement correlated with the subsequent reduction in tumor volume following chemotherapy (r = 0.57, p < 0.002). Ten of 11 (91%) of patients with tumor enhancement less than 30HU failed to achieve a reduction in tumor volume of 70% or more (Figure 12.4). A small study of 22 patients has shown the potential of CT measurements of hepatic arterial and portal perfusion to predict chemotherapeutic response in colorectal cancer.35 Portal perfusion was significantly lower in those patients whose disease progressed despite treatment (29 versus 42 ml/min/100 ml, p < 0.05). A portal perfusion value of 30 ml/min/100 ml had a predictive value for progression despite treatment of 80%. More recently, in a study of nine patients with stage 3 or 4 squamous cell carcinomas of the oropharynx, Gandhi et al. showed that higher tumor blood volume measurements on CTP were associated with a greater endoscopic tumor response (p < 0.05) following induction chemotherapy.19 The ability of CT perfusion values to predict response to radiotherapy has been shown by Hermans et al. in a study of 105 patients with head and neck cancer undergoing radiotherapy with curative intent.36 In multivariate analysis, low perfusion (along with T-stage) was an independent predictor of local control but not of regional control or cause-specific survival. Within T-stages 3 and 4, perfusion values could stratify patients according to likely response. Amongst 34 patients with T-4 tumors, the likelihood of local control at 18 months was less than 10% if pre-treatment tumor perfusion was less than the median value of 83.5 ml/min/100 g. Sahani et al. related the baseline CT perfusion values in rectal cancers to the subsequent response to chemoradiation.31 However, the

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50HU

0HU

c

d

50HU

0HU

Figure 12.4 Conventional CT (a, c) and maximum enhancement images (b, d) of two cases of small-cell lung cancer. The tumor in the upper row shows higher enhancement than the tumor in the lower row and is therefore more likely to respond to chemotherapy. (Maximum enhancement images displayed with identical windows)

results are in contrast with the studies above in that a high initial BF and a short MTT were associated with poor response. The presence of intratumoral arteriovenous shunts was proposed as a possible explanation for these findings. The use of imaging to identify patients unlikely to respond to cancer therapy has the potential to save the morbidity and cost of futile treatment. However, the diagnostic thresholds chosen must favor high specificity over sensitivity in order to ensure that treatment is not withheld from potential responders in error. This use of imaging is also affected by the prevalence of non-responders in the treated population, and is more useful when the proportion of patients failing to respond

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is moderate or high. By reflecting the vascular heterogeneity that leads to hypoxia, the future development of parameters that simultaneously encapsulate both the intensity and heterogeneity of contrast enhancement and/or perfusion may be able to improve these predictive values further.

ASSESSMENT OF TREATMENT COMPLICATIONS The toxic effects of chemotherapeutic agents and radiotherapy may be associated with changes in organ perfusion. Such changes are potentially assessable with CT perfusion, either as part of a tumor evaluation study or as a primary study of treatment toxicity. Millar et al. correlated CTP parameters with symptoms of radiation toxicity in 14 patients with cerebral metastases undergoing short-course palliative cranial irradiation.37 A 19% increase was observed in PS at day 2, which returned to its original value on day 5. MTT was negatively correlated with headache score, whereas nausea tended to be associated with increased blood volume. The high spatial resolution afforded by CTP enables assessment of the changes in intrarenal perfusion to be associated with drug nephrotoxicity.38 Milder degrees of nephrotoxicity may be associated with greater reductions in cortical than in medullary perfusion (Figure 12.5). Bone-marrow transplantation is increasingly used in the management of hematological malignancies. Veno-occlusive disease and graft-versushost disease are important hepatic complications of this procedure. The measurement of liver hemodynamics can contribute to the assessment of these conditions. Figure 12.6 illustrates the ability of CT perfusion to depict the changes in hepatic arterial and portal perfusion in a case of graft-versus-host disease.

CONCLUSIONS In conclusion, CTP is a versatile and validated technique which can be used to assess several parameters reflecting changes in tumor vasculature. CTP has the potential to monitor a range of drugs which can affect tumor vasculature, including antiangiogenic agents with various mechanisms of action on the angiogenic network. There is also the potential to evaluate radiotherapy-induced changes in tumor vasculature, and also the perfusion of normal tissue in the assessment of radiotherapy-induced complications. The use of CT perfusion to identify

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Perfusion (ml/min/ml)

5 4 3 2 1 0 1

1

2

2 3 4 Patient number

4

5

Norm

Figure 12.5 (a) CT perfusion image in a patient with nephrotoxicity induced by cyclosporine. The effects of cyclosporine are more readily seen in cortical perfusion values (b). Data are arranged in order of decreasing blood levels from left to right. Norm, normal values. (Reproduced with permission from reference 16)

subpopulations enriched for response offers the prospect of personalized cancer care and improved cost-effectiveness. To date, the evaluation of tumor response with CT perfusion has mostly been applied in the early stages of drug development to provide proof of biological activity. Such evidence of activity can impact significantly

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a

b

c

d

211

Figure 12.6 Hepatic arterial (a) and portal (b) perfusion images in a patient with graft-versus-host disease following bone-marrow transplantation. Hepatic arterial perfusion is markedly reduced throughout the liver. The changes are comparable to those seen in liver allograft rejection (c, d)

on whether a particular agent progresses to late-stage trials. The simplicity and wide availability of CT perfusion also makes the technique ideally suited to tumor response evaluation in late-stage trials and clinical practice. However, there remains a need for further evidence linking CT perfusion responses to ultimate outcomes such as survival. In the future, technology should be developed to enable the imaging of specific angiogenic pathways by linking contrast agent to angiogenic markers, as has been achieved with DCE-MRI.39 Protocols need to be standardized so that comparisons can be made between trials (see Chapter 3), and also efforts should be made to elucidate the optimal time for imaging these agents.

REFERENCES 1.

Padhani A, Ollivier L. The RECIST (Response Evaluation Criteria in Solid Tumors) criteria: implications for diagnostic radiologists. Br J Radiol 2001; 74: 983–6.

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2. Folkman J. Tumor angiogenesis: therapeutic implications. N Engl J Med 1971; 285: 1182–6. 3. Yeung WT, Lee TY, Del Maestro RF et al. Effects of steroids on iopamidol blood–brain barrier transfer constant and plasma volume in brain tumors measured with X-ray computed tomography. J Neurooncol 1994; 18: 53–60. 4. Delille JP, Slantetz PJ, Yeh ED et al. Invasive ductal breast carcinoma response to neoadjuvant chemotherapy: non invasive monitoring with functional MR imaging pilot study. Radiology 2003; 228: 63–9. 5. Hoskin PJ, Saunders MI, Goodchild K et al. Dynamic contrast enhanced magnetic resonance scanning as a predictor of response to accelerated radiotherapy for advanced head and neck cancer. Br J Radiol 1999; 72: 1093–8. 6. Mineura K, Yasuda T, Kowada M et al. Positron emission tomographic evaluation of radiochemotherapeutic effect on regional cerebral hemocirculation and metabolism in patients with glioma. J Neurooncol 1987; 5: 277–85. 7. Erasmus JJ, Gladish GW, Broemeling L et al. Interobserver and intraobserver variability in measurement of non-small-cell carcinoma lung lesions: implications for assessment of tumor response. J Clin Oncol 2003; 21: 2574–82. 8. Goh V, Halligan S, Gartner L, Bassett P, Bartram Cl. Quantitative colorectal cancer perfusion management by multidetector-row CT: does greater tumor coverage improve measurement reproducibility? Br J Radiol 2006; 79: 578–83. 9. Hayashi K, Tozaki M, Sugisaki M et al. Dynamic multislice helical CT of ameloblastoma and odontogenic keratocyst: correlation between contrast enhancement and angiogenesis. J Comput Assist Tomogr 2002; 26: 922–6. 10. Jinzaki M, Tanimoto A, Mukai M et al. Double-phase helical CT of small renal parenchymal neoplasms: correlation with pathologic findings and tumor angiogenesis. J Comput Assist Tomogr 2000; 24: 835–42. 11. Ford JM, Miles KA, Hayball MP et al. A simplified technique for measurement of blood-brain barrier permeability using computed tomography: preliminary results of the effect of RMP-7. In: Faulkner K, Carey B, Crellin A, Harrison RM, eds. Quantitative Imaging in Oncology. Proceedings of the 19th LH Gray Conference. London: British Institute of Radiology 1996: 1–3. 12. Warren K, Jakacki R, Widemann B et al. Phase II trial of intravenous lobradimil and carboplatin in childhood brain tumors: a report from the Children’s Oncology Group. Cancer Chemother Pharmacol 2006; 58: 343–7. 13. Hockel M, Schlenger K, Mitze M et al. Hypoxia and radiation response in human tumors. Semin Radiat Oncol 1996; 6: 3–9. 14. Nordsmark M, Overgaard M, Overgaard J. Pretreatment oxygenation predicts radiation response in advanced squamous cell carcinoma of the head and neck. Radiother Oncol 1996; 41: 31–9. 15. Falk SJ, Ramsay JR, Ward R et al. BW12C perturbs normal and tumor tissue oxygenation and blood flow in man. Radiother Oncol 1994; 32: 210–17. 16. Miles K, Blomley M. Applications of perfusion CT. In: Miles K, Blomley M, Dawson P, eds. Functional Computed Tomography. Oxford: ISIS Medical Media 1997: 89–116. 17. Mankoff DA, Dunnwald LK, Gralow JR et al. Changes in blood flow and metabolism in locally advanced breast cancer treated with neoadjuvant chemotherapy. J Nucl Med 2003; 44: 1806–14. 18. Tozaki M, Uno S, Kobayashi T et al. Histological breast cancer extent after neoadjuvant chemotherapy: comparison with multidetector-row CT and dynamic MRI. Radiat Med 2004; 22: 246–53.

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19. Gandhi D, Chepeha DB, Miller T et al. Correlation between initial and early follow-up CT perfusion parameters with endoscopic tumor response in patients with advanced squamous cell carcinomas of the oropharynx treated with organ-preservation therapy. Am J Neuroradiol 2006; 27: 101–6. 20. Choi J-B, Park C-K, Park DW et al. Does contrast enhancement on CT suggest tumor response for chemotherapy in small cell carcinoma of the lung? J Comput Assist Tomogr 2002; 26: 797–800. 21. Kiessling F, Boese J, Corvinus C et al. Perfusion CT in patients with advanced bronchial carcinomas: a novel chance for characterisation and treatment monitoring? Eur Radiol 2004; 14: 1226–33. 22. Xiong HQ, Herbst R, Faria SC et al. A phase I surrogate endpoint study of SU6668 in patients with solid tumors. Invest New Drugs 2004; 22: 459–66. 23. Carmeliet P, Jain RK. Angiogenesis in cancer and other diseases. Nature 2000; 407: 249–57. 24. Meijerink MR, van Cruijsen H, Hoekman K et al. The use of perfusion CT for the evaluation of therapy combining AZD2171 with gefitinib in cancer patients. Eur Radiol 2006 Oct 27; [EPub ahead of print]. 25. Willett CG, Boucher Y, di Tomaso E et al. Direct evidence that the VEGFspecific antibody bevacizumab has antivascular effects in human rectal cancer: Nat Med 2004; 10: 145–7. 26. McNeel DG, Eickhoff J, Lee FT et al. Phase I trial of a monoclonal antibody specific for integrin (MEDI-522) in patients with advanced malignancies, including an assessment on tumor perfusion. Clin Cancer Res 2005; 11: 7851–60. 27. Thomas JP, Arzoomanian RZ, Alberti D et al. Phase I pharmacokinetic and pharmaocodynamic study of recombinant human endostatin in patients with advanced solid tumors. J Clin Oncol 2003; 21: 223–31. 28. Winkler F, Kozin SV, Tong RT et al. Kinetics of vascular normalisation by VEGFR2 blockade governs brain tumor response to radiation: role of oxygentation, angiopoietin-1 and matrix metalloproteases. Cancer Cell 2004; 6: 553–63. 29. Harvey CJ, Blomley MJ, Dawson P, et al. Functional CT imaging of the acute hyperaemic response to radiation therapy of the prostate gland: early experience. J Comput Assist Tomogr 2001; 25: 43–9. 30. Harvey C, Dooher A, Morgan J et al. Imaging of tumor therapy responses by dynamic CT. Eur J Radiol 1999; 30: 221–6. 31. Sahani DV, Kalva SP, Hamberg LM et al. Assessing tumor perfusion and treatment response in rectal cancer with multisection CT: initial observations. Radiology 2005; 234: 785–92. 32. Bondestam S, Halavaara JT, Jaaskelainen JE et al. Perfusion CT of the brain in the assessment of flow alterations during brachytherapy of meningioma. Acta Radiol 1999; 40: 469–73. 33. Tsushima Y, Funabasama S, Aoki J, Sanada S, Endo K. Quantitative perfusion map of malignant liver tumors created from dynamic computed tomography data. Acad Radiol 2004; 11: 215–23. 34. Purdie TG, Sherar MD, Lee TY. The use of CT perfusion to monitor the effect of hypocapnia during laser thermal therapy in a rabbit model. Int J Hypertherm 2003; 19: 461–79. 35. Sommerfeld NWB, Miles KA, Dugdale P, Leggett DAC, Bunce IH. Colorectal cancer: progressive disease is associated with altered liver perfusion on functional CT. Abstract presented at 50th Annual Meeting of the Royal Australian and New Zealand College of Radiologists, 21–25 October, 1999, Sydney: Abstr No 56.

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36. Hermans R, Meijerink M, Van den Bogaert W et al. Tumor perfusion rate determined noninvasively by dynamic computed tomography predicts outcome in head-and-neck cancer after radiotherapy. Int J Radiat Oncol Biol Phys 2003; 57: 1351–6. 37. Millar BM, Purdie TG, Yeung I et al. Assessing perfusion changes during whole brain irradiation for patients with cerebral metastases. J Neurooncol 2005; 71: 281–6. 38. Miles KA; Hayball MP, Dixon AK. Functional imaging of changes in human intra-renal perfusion using quantitative dynamic computed tomography. Invest Radiol 1994; 29: 911–14. 39. Mulder WJ, Strijkers GJ, Habets JW et al. MR molecular imaging and fluorescence microscopy for identification of activated tumor endothelium using a bimodal lipidic nanoparticle. FASEB J 2005; 19: 2008–10. 40. Wang ZQ, Li JS, Lu GM et al. Correlation of CT enhancement, tumor angiogenesis and pathologic grading of pancreatic carcinoma. World J Gastroenterol 2003; 9: 2100–4.

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13 Perfusion CT–PET: opportunities for combined assessment of tumor vascularity and metabolism Kenneth A Miles

Technology that integrates computed tomography (CT) and positron emission tomography (PET) within a single imaging device is a relatively recent development in imaging. Nevertheless, such systems are fast becoming the standard for delivery of clinical PET services. The CT component of these combined examinations has generally comprised an examination for attenuation correction and anatomical assignment of abnormalities identified on PET, and is therefore performed without contrast medium. However, there is a growing interest in the use of intravenous contrast media during PET–CT.1 Extending these applications for contrast media to include CT perfusion would enable anatomical information about tumors to be co-registered with perfusion data and metabolic information, such as glucose metabolism, in a single examination. This combined approach would allow simultaneous assessment of multiple endothelial-related (i.e. blood flow, blood volume, vascular permeability) and tumor cell-related (i.e. metabolism) aspects of tumor biology (Figure 13.1). The use of CT to assess perfusion as opposed to administration of a second PET tracer such as [O15]water circumvents the need for an on-site cyclotron. Furthermore, because CT depicts perfusion data with higher spatial resolution, some of the limitations of PET perfusion studies can be avoided, including the underestimation of perfusion values in small tumors due to the partial volume effect, and the spill over of counts from adjacent structures with high blood flow (e.g. heart, aorta, liver).2 To date, there have been few reports describing the implementation of CT perfusion and PET on a

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a

b

c

d

Figure 13.1 Combining fluorodeoxyglucose-positron emission tomography (FDG-PET) with computed tomography perfusion (CT) allows simultaneous assessment of anatomy (a: conventional CT), glucose metabolism (b: FDG-PET), and multiple endothelial related parameters (c: relative blood volume, d: vascular permeability). The recurrent glioma is seen as focally increased glucose metabolism and increased vascular permeability (arrows)

single imaging device. However, there have been a number of studies in which patients have been examined by both techniques on separate systems. These studies, combined with data in which other methods of perfusion imaging have been correlated against PET, have shown that tumor vascularity and metabolism are not consistently coupled, and a combined assessment using CT perfusion and PET in a single examination could provide a more detailed picture of the biological behavior of tumors.

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BIOMOLECULAR MEDIATORS FOR TUMOR VASCULARITY AND METABOLISM The tumor circulation is an important determinant of the microenvironment within tumor cells, including their metabolic status. A well developed tumor vascular supply will ensure a sufficient delivery of glucose and oxygen to support the metabolism essential for tumor growth. However, as tumors increase in size, they may outgrow their blood supply. The subsequent reduction in the delivery of oxygen renders such tumors hypoxic. Yet, hypoxia is also recognized to be a feature of the tumor microenvironment that stimulates glucose metabolism.3,4 Thus, the relationship between tumor vascularity and metabolism is complex. Tumor hypoxia is known to be associated with increased tumor aggression and a poor response to a variety of therapeutic strategies.3 Hypoxia inducible factors (HIFs), such as HIF-1α and HIF-2α, are important mediators of the tumor metabolic response to hypoxia. HIFs increase the transcription of several molecules that adapt the tumor to its hypoxic environment. Not only do HIFs increase the expression of glucose transporters and hexokinase, HIFs also stimulate angiogenesis, promote tumor aggression, and confer resistance to chemotherapy and radiotherapy (Figure 13.2).3 The ability for tumors to mount an effective response to hypoxia is highly variable. Histological and imaging

Oncogene mutations e.g. p53

Tissue hypoxia

HIF

Upregulated molecules Biological effects

Nitric oxide Vascular dilatation and permeability

VEGF

Angiogenesis

Glut-1

MDR p-Gp

Multidrug Increased glucose metabolism resistance

Figure 13.2 Summary of the effects of hypoxia inducible factors (HIFs) on tumor biology. VEGF, vascular endothelial growth factor; Glut-1, glucose transporter; MDR P-Gp, multidrug resistance P-glycoprotein

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studies that compare markers of hypoxia and glucose metabolism have shown that these aspects of the tumor microenvironment do not always correlate.5–7 HIFs can also be expressed constitutively by tumors as a consequence of oncogene mutations, resulting not only in increased glucose metabolism but also increased angiogenesis and perfusion.3 Indeed, high levels of angiogenesis and elevated glucose metabolism are both associated with increased metastatic potential and poor patient survival for a range of cancers.8–15 However, in non-small-cell lung cancer, increased expression of HIFs with low microvessel density (MVD) has also been shown to be associated with a worse prognosis.16 Thus, the balance between tumor vascularity and metabolic status offers important information concerning the tumor microenvironment. High glucose metabolism with increased vascularity would represent a different biological status of the tumor than high metabolism with poor vascularity, the latter indicating adaptation to hypoxia. Low glucose metabolism with poor vascularity would suggest a failure of the adaptive response to hypoxia.

VASCULAR–METABOLIC RELATIONSHIPS IN TUMORS Imaging studies using a range of techniques suggest a highly variable relationship between tumor vascularity and glucose metabolism (Table 13.1). Factors influencing the relationship include tumor type, grade and size. Although demonstrating an overall trend between glucose metabolism and blood volume in cerebral glioma, Aronen et al found that uncoupling of these processes could occur with high-grade tumors.17 Studies in breast cancer have found only poor or moderate correlations between vascularity and metabolism,18,19 whilst studies of liver tumors have suggested a negative correlation to date20,21 (Figure 13.3). In nonsmall-cell lung cancer (NSCLC), the relationship between tumor circulation and metabolism appears to be dependent on tumor size. One study comprising patients with NSCLC undergoing surgical resection found a statistically significant correlation between fluorodeoxyglucose (FDG) uptake and CT measurements of tumor vascularity.22 The mean diameter of the tumors included in this study was 2.9 ± 0.3 cm. On the other hand, a separate study comparing PET measurements of perfusion with FDG uptake in patients with stage IIIA-N2 NSCLC, and thus probably larger tumors, showed no correlation.23 A more recent study using CT perfusion and FDG-PET to specifically assess the impact of tumor size on the relationship between blood flow and metabolism

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Table 13.1 Summary of imaging studies comparing tumor vascularity and metabolism Study

Tumor type

Techniques

Findings

Aronen et al.17

Glioma

Mankoff et al.18

Breast

DC-MRI FDG-PET H215O-PET FDG-PET

Semple et al.19

Breast

Fukuda et al.20

Van Laarhoven et al.21 Tateishi et al.22

HCC, CCC, colon liver mets Colon liver mets NSCLC

Maximum CBV correlates with maximum FDG (r = 0.573, p = 0.023) Perfusion and metabolism weakly correlated. High metabolism–flow ratio predicts poor treatment response Moderate correlation between vascularity and metabolism Negative correlation (r = −0.713, p = 0.006)

Hoekstra et al.23

NSCLC

Miles et al.24

NSCLC

Veronesi et al.25

Lung metastases

DC-MRI FDG-PET H215O-PET FDG-PET DC-MRI FDG-PET Perf CT FDG-PET H215O-PET FDG-PET Perf CT FDG-PET Perf CT FDG-PET

Negative trend (r = −0.421, p = 0.082) Vascularity and metabolism correlate in surgically resectable tumors No correlation between perfusion and metabolism in stage IIIA-N2 Correlation between vascularity and metabolism in small tumors only (r = 0.85, p = 0.03) FDG uptake and angiogenesis independent

CBV, cerebral blood volume; CCC, cholangiocarcinoma; DC-MRI, dynamic contrast-enhanced magnetic resonance imaging; FDG-PET, fluorodeoxyglucose-positron emission tomography; HCC, hepatocellular carcinoma; H215O, oxygen-15-labeled water; mets, metastases; NSLLC, non-small-cell lung cancer; Perf CT, computed tomography perfusion.

found a correlation between these parameters only for tumors with a cross-sectional area of less than 4.5 cm2, equivalent to a mean diameter of 2.4 cm.24 Larger tumors tended to exhibit lower perfusion values but greater FDG uptake. The metabolic–flow difference correlated with tumor size, implying uncoupling of blood flow and glucose metabolism in larger tumors. Uncoupling of flow and metabolism has also been observed in pulmonary metastases.25 The degree to which such uncoupling of flow and metabolism develops in larger tumors of other cancer types has yet to be determined. The dependence of blood flow–metabolic relationships in NSCLC on tumor size has implications for the use of FDG-PET and quantitative CT perfusion in the evaluation of pulmonary nodules.26 The finding of low perfusion in large tumors highlights an important pitfall for CT perfusion in the diagnostic assessment of pulmonary nodules, particularly for nodules with a diameter greater than approximately 2.5 cm.

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a

b

c

Figure 13.3 Corresponding conventional contrast-enhanced CT (a), CT perfusion (b), and FDG-PET (c) images demonstrating increased perfusion and increased glucose metabolism in hepatic metastases from colorectal cancer

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Histological studies in lung carcinomas indicate this pitfall to be due to an association between tumor necrosis and lower contrast enhancement [27]. On the other hand, vascularity well in excess of metabolism may be a feature of inflammatory lesions (Figure 13.4)28

Vascular–metabolic relationships and tumor aggression As discussed above, tumor adaptation to hypoxia is associated with increased tumor aggression and resistance to treatment. The finding of high FDG uptake with poor vascularity offers a potential imaging correlate for this tumor biological status. Indeed, Miles et al.24 found that a high metabolic–flow difference was more likely to occur in advanced NSCLC, whilst Mankoff et al.18 showed that breast cancers with a high ratio of glucose metabolism to perfusion were less likely to respond favorably to treatment. In separate imaging studies of head and neck cancer, low perfusion assessed by CT perfusion and high FDG uptake have both been found to be independent predictors for poor local control following treatment.29,30 However, the significance of

SPV = 5.5

10

SUV = 6.3

0 SPV = 5.5

a

b

20

0

SUV = 2.8

c

Figure 13.4 Conventional CT (a), CT perfusion (b), and FDG-PET (c) images of malignant (upper row) and inflammatory (lower row) lung lesions. In the malignant lesion, glucose metabolism (expressed as the standardized uptake value (SUV)), exceeds perfusion (expressed as the standardized perfusion value (SPV)), whereas the flow–metabolic relationship is reversed in the inflammatory lesion

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vascular–metabolic relationships may be tumor-specific. In separate studies of rectal cancer, high tumor perfusion before chemoradiation, assessed with dynamic contrast-enhanced MRI or CT perfusion, predicted a poor response,31,32 whilst pretreatment tumor uptake of FDG showed no correlation with tumor shrinkage rate, presence of microscopic residual disease, or local recurrence rate.33 The emergence of chemotherapeutic agents, such as tirapazamine, and gene therapy strategies that target hypoxia suggest a clinical role for the imaging detection of tumor hypoxia.3 Certain radiopharmaceuticals such as [18F]fluoromisonidazole (F-MISO) are selectively taken up in proportion to tissue hypoxia. However, such imaging agents provide little information about the ability of tumor cells to adapt to hypoxia, for example by increasing glycolysis. Imaging assessments of the balance between glucose metabolism and perfusion will more closely reflect tumor adaptation to hypoxia, and are thus potentially of greater prognostic significance than the presence of hypoxia itself.

Changes in tumor vascularity and metabolism following therapy Imaging is widely used to assess tumor response to therapy, both in clinical practice and in trials assessing the efficacy of novel drugs or other therapies. The main imaging approaches used are predicated on serial measurements of tumor size on CT or other cross-sectional imaging techniques (e.g. RECIST: Response Evaluation Criteria in Solid Tumors). However, such anatomically based methods are constrained by poor reproducibility, slow response rates, and residual non-tumorous masses. Furthermore, certain treatment approaches, for example drugs that target the tumor vasculature, may produce little or no change in tumor size despite therapeutic efficacy. In the light of these limitations, there is increasing interest in using imaging techniques that assess changes in tumor physiology in response to therapy, rather than change in size. As discussed in Chapter 12, CT perfusion is one such approach, but the use of FDG-PET as a tumor response marker is also emerging. To date, these methods have mostly been used independently. However, the limited data available in which both perfusion and FDG uptake have been measured before and after treatment show that perfusion and glucose metabolism may not change in parallel in response to therapy, reflecting different responses of the vascular and cellular compartments of the tumor34–37 (Figure 13.5). Tumor type, drug type and dose, and time since therapy are all factors that affect the relative magnitude of change in these parameters.

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Before therapy Tumor area: −85% and −83%

Perfusion: −12% and +3%

Glucose metabolism: −42% and −68%

After therapy

CT

Perfusion CT

FDG-PET

Figure 13.5 Different morphological, vascular, and metabolic responses to therapy in non-small-cell lung cancer. (Reproduced with permission from reference 38)

The studies that have assessed changes in both tumor perfusion and glucose metabolism following therapy to date are summarized in Table 13.2. In many cases, the reduction in perfusion is of greater magnitude than the fall in glucose metabolism. Uncoupling of flow and metabolism appears to be particularly likely following antivascular therapy, probably reflecting drug-induced hypoxia and secondary stimulation of glucose metabolism. Thus, the relative magnitude of changes in tumor perfusion and glucose metabolism in response to therapy may depend upon both tumor type and treatment regimen. The study of rectal cancer by Willett et al. demonstrating different CT perfusion and FDG-PET responses to the vascular endothelial growth factor (VEGF) antibody bevacizumab particularly highlights the benefits of combining CT perfusion with FDG-PET.37 Based on the results of above studies, it is possible to propose a subclassification of therapeutic responses into those that are: (1) balanced (i.e. a significant reduction in both glucose metabolism and tumor vascularity), (2) predominantly vascular, and (3) predominantly metabolic (Table 13.3). A balanced response seems most likely to be associated with a good outcome. It is likely that the predominantly vascular and predominantly metabolic responses will carry different clinical significance. The possibility of modulating tumor responses by adapting therapy for individual patients on the basis of their imaging findings can be envisaged. In the study of Willett et al. the addition of chemoradiation to bevacizumab produced a more balance response, and this anti-VEGF agent is currently licensed for use in combination with other therapies.

Tumor type

Various

Locally advanced breast cancer

Androgen-independent prostate cancer

Rectal cancer

Study

Herbst et al.34

Mankoff et al.35

Kurdziel et al.36

Willett et al.37 Bevacizumab

Thalidomide

Flow–metabolic relationships dose–dependent. Reduced perfusion but increased glucose metabolism at dose that produced greatest endothelial and tumor cell apoptosis Increased perfusion with small reduction in FDG uptake in tumors failing to respond to treatment. Decreased perfusion and FDG uptake in tumors that proceeded to partial or complete response Positive correlation between prostate specific antigen (PSA) response and change in glucose metabolism. Negative correlation between PSA response and change in perfusion Significant reduction in perfusion. No change in FDG uptake

H215O-PET FDG-PET

Perf CT FDG-PET

H215O-PET FDG-PET

H215O-PET FDG-PET

Findings

Techniques

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Endostatin

Drug regimen

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Table 13.2 Summary of studies assessing changes in both tumor perfusion and glucose metabolism following therapy

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Table 13.3 Sub–classification of functional tumor response based upon perfusion and metabolic imaging

Unchanged or increased perfusion Reduced perfusion

Unchanged or increased metabolism

Reduced metabolism

No response

Predominantly metabolic response Balanced response

Predominantly vascular response

On the other hand, it may be appropriate to add an antivascular drug to a treatment regimen producing a predominantly metabolic response. The ultimate goal would be to tailor an individual patient’s therapy to the vascular–metabolic response exhibited by their tumor.

HEPATIC PHOSPHORYLATION OF GLUCOSE ASSESSED BY CT PERFUSION–PET FDG-PET has been used to evaluate the kinetics of liver glucose metabolism using a three-compartment model with four rate constants (k1–4) that define the rates of transfer between plasma FDG, tissue FDG, and tissue FDG-6-phosphate (Figure 13.6).39 CT measurements of hepatic perfusion can feed into this kinetic analysis by providing an estimate of the rate of passage between intravascular and intracellular nonphosphorylated compartments, k1.40 The permeability of the liver sinusoids to glucose is sufficiently high for this rate constant approximate perfusion, as confirmed by the studies of Munk et al.41 Hence, the ratio of hepatic FDG uptake to hepatic perfusion provides an index of the hepatic phosphorylation fraction (HPFI: see Box 13.1). Applying the above methodology to patients with colorectal cancer it has been possible to demonstrate reduced hepatic phosphorylation of

Plasma FDG

k1 k2

Tissue FDG

k3 k4

Tissue FDG-6-PO4−

Figure 13.6 Three-compartment kinetic model for hepatic glucose metabolism.39 FDG, fluorodeoxyglucose; FDG-6-PO4-, FDG-6-phosphate

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Box 13.1

Derivation of the hepatic phosphorylation fraction index (HPFI)

Using the three-compartmental model described by Choi et al. (Figure 13.6),39 the fraction of intracellular glucose that undergoes phosphorylation (i.e. the phosphorylation fraction, PF) is given by: PF = k3/(k2 + k3)

(1)

The net influx constant, Ki is given by: Ki = (k1k3)/(k2 + k3) = k1 × PF

(2)

If the standardised uptake value (SUV) of FDG in the liver is used as a surrogate for the net influx constant (Ki) and CT perfusion measurements of combined arterial and portal perfusion as a surrogate for k1, then an index of hepatic phosphorylation (HPFI) can be obtained from: HPFI = SUV / Hepatic perfusion

(3)

glucose in patients with extrahepatic tumors (HPFI 2.62 vs. 4.05, p < 0.002) and in those patients surviving less than 2 years (HPFI 2.84 vs. 4.06, p = 0.014).40 There was no evidence of weight loss amongst the patients with reduced phosphorylation. Derangements of liver metabolism in patients with cancer are well recognized, including reduced hepatic glucose production and glucose recycling and altered glucose transport across hepatic cell membranes.42 These abnormalities contribute to the syndrome of anorexia and weight loss known as cachexia, a common outcome for patients with advanced cancer. Cachexia is a cause of significant morbidity, affecting both the efficacy of antitumor therapy and patient survival, and has therefore been identified as a target for therapy.43,44 CT perfusion–PET assessments of hepatic glucose phosphorylation offer the potential to identify metabolic changes associated with cachexia before the development of weight loss and to provide a means of monitoring treatments aimed at controlling those metabolic abnormalities.

SUMMARY The increased glucose metabolism exhibited by tumors has been recognized since Warburg’s experiments in the 1930s.45 The fact that the growth, survival, and expansion of solid tumors are highly dependent on the vascular system recruited by the malignancy was demonstrated in the early 1970s by Folkman.46 Although it is reasonable to hypothesize that the metabolic requirements of tumors are mirrored

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by alterations in tumor hemodynamics, experience has shown a much more complex relationship between these processes. The balance between vascularity and metabolism is an important determinant of the biological behavior of tumors and their response to therapy. CT perfusion–PET using integrated imaging devices now offers the opportunity to quantify both these processes in-vivo in a single examination, not only for research but also in the clinical arena.

REFERENCES 1. Antoch G, Freudenberg LS, Beyer T, Bockisch A, Debatin JF. To enhance or not to enhance? 18F-FDG and CT contrast agents in dual-modality 18F-FDG PET/CT. J Nucl Med 2004; 45(Suppl) 1: 56S–65S. 2. Bacharach SL, Libutti SK, Carrasquillo JA. Measuring tumor blood flow with H(2)(15)O: practical considerations. Nucl Med Biol 2000; 27: 671–767. 3. Semenza GL. HIF-1 and tumor progression: pathophysiology and therapeutics. Trends Mol Med 2002; 8(4 Suppl): S62–7. 4. Raghunand N, Gatenby RA, Gillies RJ. Microenvironmental and cellular consequences of altered blood flow in tumors. Br J Radiol 2003; 76: 11–22. 5. van Laarhoven HW, Kaanders JH, Lok J et al. Hypoxia in relation to vasculature and proliferation in liver metastases in patients with colorectal cancer. Int J Radiat Oncol Biol Phys 2006; 64: 473–82. 6. Foo SS, Abbott DF, Lawrentschuk N, Scott AM. Functional imaging of intratumoral hypoxia. Mol Imaging Biol 2004; 6: 291–305. 7. Rajendran JG, Wilson DC, Conrad EU et al. [(18)F]FMISO and [(18)F]FDG PET imaging in soft tissue sarcomas: correlation of hypoxia, metabolism and VEGF expression. Eur J Nucl Med Mol Imaging 2003; 30: 695–704. 8. Ahuja V, Coleman RE, Herndon J, Patz EF Jr. The prognostic significance of fluorodeoxyglucose positron emission tomography imaging for patients with non-small cell lung carcinoma. Cancer 1998; 83: 918–24. 9. Vansteenkiste JF, Stroobants SG, Dupont PJ et al. Prognostic importance of the standardized uptake value on (18)F-fluoro-2-deoxy-glucose-positron emission tomography scan in non-small-cell lung cancer: an analysis of 125 cases. Leuven Lung Cancer Group. J Clin Oncol 1999; 17: 3201–6. 10. Higashi K, Ueda Y, Arisaka Y et al. 18F-FDG uptake as a biologic prognostic factor for recurrence in patients with surgically resected non-small cell lung cancer. J Nucl Med 2002; 43: 39–45. 11. Jeong HJ, Min JJ, Park JM et al. Determination of the prognostic value of [(18)F] fluorodeoxyglucose uptake by using positron emission tomography in patients with non-small cell lung cancer. Nucl Med Commun 2002; 23: 865–70. 12. Downey RJ, Akhurst T, Gonen M et al. Preoperative F-18 fluorodeoxyglucosepositron emission tomography maximal standardized uptake value predicts survival after lung cancer resection. J Clin Oncol 2004; 22: 3255–60. 13. Fontanini G, Bigini D, Vignati S et al. Microvessel count predicts metastatic disease and survival in non-small cell lung cancer J Pathol 1995; 177: 57–63.

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14. Meert AP, Paesmans M, Martin B et al. The role of microvessel density on the survival of patients with lung cancer: a systematic review of the literature with meta-analysis. Br J Cancer 2002; 87: 694–701. 15. Tanaka F, Yanagihara K, Otake Y, et al. Prognostic factors in resected pathologic (p-) stage IIIA-N2, non-small-cell lung cancer. Ann Surg Oncol 2004; 11: 612–18. 16. Giatromanolaki A, Koukourakis MI, Sivridis E et al. Relation of hypoxia inducible factor 1 alpha and 2 alpha in operable non-small cell lung cancer to angiogenic/molecular profile of tumors and survival. Br J Cancer 2001; 85: 881–90. 17. Aronen HJ, Pardo FS, Kennedy DN et al. High microvascular blood volume is associated with high glucose uptake and tumor angiogenesis in human gliomas. Clin Cancer Res 2000; 6: 2189–200. 18. Mankoff DA, Dunnwald LK, Gralow JR et al. Blood flow and metabolism in locally advanced breast cancer: relationship to response to therapy. J Nucl Med 2002; 43: 500–9. 19. Semple SI, Gilbert FJ, Redpath TW et al. The relationship between vascular and metabolic characteristics of primary breast tumors. Eur Radiol 2004; 14: 2038–45. 20. Fukuda K, Taniguchi H, Koh T, Kunishima S, Yamagishi H. Relationships between oxygen and glucose metabolism in human liver tumors: positron emission tomography using (15)O and (18)F-deoxyglucose. Nucl Med Commun 2004; 25: 577–83. 21. van Laarhoven HW, de Geus-Oei LF, Wiering B et al. Gadopentetate dimeglumine and FDG uptake in liver metastases of colorectal carcinoma as determined with MR imaging and PET. Radiology 2005; 237: 181–8. 22. Tateishi U, Nishihara H, Tsukamoto E et al. Lung tumors evaluated with FDGPET and dynamic CT: the relationship between vascular density and glucose metabolism. J Comput Assist Tomogr 2002; 26: 185–90. 23. Hoekstra CJ, Stroobants SG, Hoekstra OS et al. Measurement of perfusion in stage IIIA-N2 non-small cell lung cancer using H(2)(15)O and positron emission tomography. Clin Cancer Res 2002; 8: 2109–15. 24. Miles KA, Griffiths MR, Keith CJ. Blood flow-metabolic relationships are dependent on tumor size in non-small cell lung cancer: a study using quantitative contrast-enhanced computer tomography and positron emission tomography. Eur J Nucl Med Mol Imaging 2006; 33: 22–8. 25. Veronesi G, Landoni C, Pelosi G et al. Fluoro-deoxi-glucose uptake and angiogenesis are independent biological features in lung metastases. Br J Cancer 2002; 86: 1391–5. 26. Swensen SJ, Viggiano RW, Midthun DE et al. Lung nodule enhancement at CT: multicenter study. Radiology 2000; 214: 73–80. 27. Yamashita K, Matsunobe S, Tsuda T et al. Intratumoral necrosis of lung carcinoma: a potential diagnostic pitfall in incremental dynamic computed tomography analysis of solitary pulmonary nodules? J Thorac Imaging 1997; 12: 181–7. 28. Miles KA, Griffiths MR, Fuentes MA. Standardized perfusion value: universal CT contrast enhancement scale that correlates with FDG PET in lung nodules. Radiology 2001; 220: 548–53. 29. Hermans R, Meijerink M, Van den Bogaert W et al. Tumor perfusion rate determined noninvasively by dynamic computed tomography predicts outcome in

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30.

31.

32.

33. 34.

35.

36.

37.

38.

39.

40.

41.

42. 43. 44.

45. 46.

229

head-and-neck cancer after radiotherapy. Int J Radiat Oncol Biol Phys 2003; 57: 1351–6. Allal AS, Slosman DO, Kebdani T et al. Prediction of outcome in head-andneck cancer patients using the standardized uptake value of 2-[18F]fluoro-2deoxy-D-glucose. Int J Radiat Oncol Biol Phys 2004; 5: 1295–300. Devries AF, Griebel J, Kremser C et al. Tumor microcirculation evaluated by dynamic magnetic resonance imaging predicts therapy outcome for primary rectal carcinoma. Cancer Res 2001; 61: 2513–16. Sahani DV, Kalva SP, Hamberg LM et al. Assessing tumor perfusion and treatment response in rectal cancer with multisection CT: initial observations. Radiology 2005; 234: 785–92. Oku S, Nakagawa K, Momose T et al. FDG-PET after radiotherapy is a good prognostic indicator of rectal cancer. Ann Nucl Med 2002; 16: 409–16. Herbst RS, Mullani NA, Davis DW et al. Development of biologic markers of response and assessment of antiangiogenic activity in a clinical trial of human recombinant endostatin. J Clin Oncol 2002; 20: 3804–14. Mankoff DA, Dunnwald LK, Gralow JR et al. Changes in blood flow and metabolism in locally advanced breast cancer treated with neoadjuvant chemotherapy. J Nucl Med 2003; 44: 1806–14. Kurdziel KA, Figg WD, Carrasquillo JA et al. Using positron emission tomography 2-deoxy-2-[18F]fluoro-D-glucose, 11CO, and 15O-water for monitoring androgen independent prostate cancer. Mol Imaging Biol 2003; 5: 86–93. Willett CG, Boucher Y, di Tomaso E et al. Direct evidence that the VEGFspecific antibody bevacizumab has antivascular effects in human rectal cancer. Nat Med 2004; 10: 145–7. Miles KA. Computed tomography measurements of perfusion in cancer therapy. In: Shields AF, Price P, eds. In vivo Imaging of Cancer Therapy Totowa, NJ: Humana Press, 2007: in press. Choi Y, Hawkins RA, Huang SC et al. Evaluation of the effect of glucose ingestion and kinetic model configurations of FDG in the normal liver. J Nucl Med 1994; 35: 818–23. Miles KA, Comber L, Keith CJ et al. Combining perfusion CT and FDG-PET data demonstrates reduced hepatic phosphorylation of glucose in patients with advanced colorectal cancer and poor survival. Eur Radiol 2003; 13(Suppl 1): 293. Munk OL, Bass L, Roelsgaard K et al. Liver kinetics of glucose analogs measured in pigs by PET: importance of dual-input blood sampling. J Nucl Med 2001; 42: 795–801. Tayek JA. A review of cancer cachexia and abnormal glucose metabolism in humans with cancer. J Am Coll Nutr 1992; 11: 445–56. Capra S, Ferguson M, Ried K. Cancer: impact of nutrition intervention outcome—nutrition issues for patients. Nutrition 2001; 17: 769–72. Laviano A, Meguid MM, Inui A, Muscaritoli M, Rossi-Fanelli F. Therapy insight: cancer anorexia-cachexia syndrome—when all you can eat is yourself. Nat Clin Pract Oncol 2005; 2: 158–65. Warburg O, Wind F, Negelin E. The metabolism of tumors in the body. J Gen Physiol 1927; 8: 519–30. Folkman J. Tumor angiogenesis: therapeutic implications. N Engl J Med 1971; 285: 1182–6.

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Index Page numbers in italics indicate figures, tables or boxes. accuracy studies 43 adiabatic approximation 30–1 Ambrose, J. 7, 8 anatomy history of development 3–6 imaging and 6–8 knowledge in ancient times 1–3 ancient civilizations 1–3 angiogenesis 73–83, 202 characteristics 76–7 endogenous inhibitors 75 imaging 77–80, 82–3 rectal cancer 129, 134–6 therapeutic inhibitors see antiangiogenic agents in tumor growth, progression and metastases 74–5 angiogenic switch 74–5 angiopoietins 82 animal tumor models, validation studies 39–41, 42 annexin V 82 anthracycline 200, 203 antiangiogenic agents 73–4, 82, 198 cancer management 77–8 evaluation of efficacy 78–81, 202–5 lymphoma 169 rectal cancer 137–9 antivascular agents, novel 201–5

appearance (arrival) time (T0) 34, 62 maps 61–2, 63 Aristotle 3, 4 arrival time (T0) see appearance time arterial concentration function, input 31–6 delay in contrast agent arrival 33–4 dispersion 33 to liver 34–5 recirculation effect 35–6 attenuation change over time 55, 56, 56 thresholds for segmentation 66, 66, 67 tube voltage and 54, 54 Avastin see bevacizumab Axel, Leon 11 axillary lymph nodes, sentinel 165, 165 AZD21271 202–4, 203 beam hardening effects 38, 54 lungs 116, 123 lymph nodes 164 benign prostatic hyperplasia 148–50, 149

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bevacizumab 73–4, 77, 81 perfusion CT Perfusion 138–9, 202, 203 perfusion CT–PET 223, 224 binomial spatial smoothing 62–4, 63, 64 blood–brain barrier (BBB) permeability 11 brain tumors 92, 93, 95, 96 drugs affecting 198–9, 199 radiation planning 98, 98 blood flow (perfusion) (F) 16, 62 average, in region of interest 64, 68, 70 central volume principle 19–20 comparison with enhancement 58, 58–9 maps 61, 63 reproducibility studies 42–3 validation of measurements 39–41 blood volume (Vb; BV) 16, 62 average, in region of interest 64–5, 68, 70 central volume principle 19–20 maps 61, 63 reproducibility studies 42–3 validation of measurement 40–1 bone marrow transplantation, complications 209, 211 bowel preparation, rectal cancer 130 brain tumors 89–100 choice of protocol 57 combined perfusion CT Perfusion–PET 216 development of perfusion imaging 10, 11–12 history of imaging 7, 8, 10 image segmentation 66, 66, 68 parameter maps 61, 63 radiation planning 97–8, 98 treatment response assessment 99, 198–9, 199, 203 validation of measurements 40 vascularity–metabolism relationship 218, 219 vs cerebral infarction 96–7, 97

breast cancer combined perfusion CT Perfusion–PET 224 lymph node metastases 164, 165, 165 treatment response assessment 203 vascularity–metabolism relationship 218, 219, 221 bronchogenic carcinoma 112 BW12C 199–200, 200, 203 cachexia 226 calibration, CT system 38, 114, 115, 116 capillary endothelium, transfer flux of solute through 17–19, 18 capillary permeability surface area product (PS) 16–17, 62 average, in region of interest 68, 71 maps 61, 63 reproducibility studies 42–3 validation of measurement 39–41, 42 carbon-11-labeled carbon monoxide 80 carcinoembryonic antigen 191 central volume principle 19–20, 181 cerebral angiography, history of development 6–7 cerebral blood flow (CBF) brain tumors 93, 97 deconvolution method 11 Kety–Schmidt method 10, 26 cerebral blood volume (CBV) 93, 94, 95, 97 radiation necrosis vs recurrent tumor 99 cerebral infarction 96–7 cerebral lymphoma 94–6 cerebral tumors see brain tumors cervical cancer 158–9 chemoradiotherapy predicting response to 207–8

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chemoradiotherapy (Continued) response assessment 136–7, 137, 206 chemotherapy assessment of complications 209, 210 predicting response to 207 response assessment 99, 168, 200, 200–1, 224 China, ancient 2 circulation, historical discovery 8–10 colorectal cancer antiangiogenic therapy 77 combined perfusion CT Perfusion–PET 225–6 factors predicting survival 186, 187 liver metastases 173, 184, 185, 188–9, 192–3 occult hepatic micrometastases 185–6 prediction of response to therapy 207 reproducibility studies 42 see also rectal cancer compartment models 27, 27–9 contrast injection rate and 55–6, 56 hepatic glucose metabolism 225, 225, 226 liver perfusion 177–8 complications of treatment, assessing 209 computed tomography (CT) angiography 47 history of development 7–8 perfusion see perfusion computed tomography system calibration 38, 114, 115, 116 computer-aided diagnosis (CAD), lung cancer 124 contrast appearance time see appearance (arrival) time contrast enhancement 20, 55–7 see also tumor enhancement

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contrast media, X-ray 15–16 delayed arrival in tissue 33–4 history of development 6–7 injection rates 55–6, 56 input arterial concentration function 31–6 kinetic models 26–31 plasma normalized quantities 20 rectal cancer 131 signal-to-noise ratio 38 type 55 volume and concentration 55–7, 56 convolution 20, 22–4, 23, 178 cooption, vascular 74 Cormack, Allan McLeod 8 Crookes, Sir William 6 CT see computed tomography CT enhancement see enhancement, CT CT number 20 CTP see perfusion computed tomography deconvolution analysis 11, 31 liver metastases 185 liver perfusion 178–83 protocols 49, 51, 53, 55 theory 24, 178–80, 179 dexamethasone 199, 203 dispersion, measured arterial concentration function 33 dissection, human body 2, 3, 4–5 distributed parameter models 29–31 distribution volume (Vd) 16 fractional 181 liver 177, 182, 183 Doppler perfusion index, liver 188–9 Doppler ultrasound liver metastases 188–9 prostate cancer 149 dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) 79, 81, 189

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Egypt, ancient 1–2 endostatin (Endostar) 74, 203, 204, 224 endothelial progenitor cells (EPCs) 75 enhancement, CT 20, 55–7 see also tumor enhancement exposure time 53–5 extraction efficiency (fraction) (E) 17 FE 19, 62 two-compartment model 28–9 validation of measurement 39–40, 42 fibroblast growth factors (FGFs) 75, 202 Fick, Adolf 9–10 Fick principle application 12, 25, 27 history 9–10 validation 39, 43 Fick’s law 19 FIRF see flow scaled impulse residue function flow extraction product see FE flow scaled impulse residue function (FIRF) 21, 24, 30–1 scanning protocols and 36, 37 fluorodeoxyglucose positron emission tomography (18FDG-PET) angiogenesis imaging 80, 81 combined with perfusion CT Perfusion see perfusion computed tomography– positron emission tomography lymph node metastases 163 pulmonary nodules 115, 119, 120–2 tumor vascularity and 218–21, 219 [18F]fluoromisonidazole (F-MISO) 80, 222

functional computed tomography (CT) 79 Galen, Claudius 2, 3–4, 5, 9 gastric cancer, lymph node metastases 164, 164–5 gastrointestinal stromal tumors (GIST) 78, 81 gastrointestinal tumors 129–41 treatment response assessment 203 see also colorectal cancer; rectal cancer gefitinib 202–4, 203 Geynes, John 4 glioblastoma 94 gliomas 93–4, 94, 95 combined perfusion CTPerfusion–PET 216 vascularity–metabolism relationship 218, 219 glucose metabolism assessment 215, 216 hepatic, perfusion CT–PET 225, 225–6 tumor vascularity and 217, 217–21, 219 see also fluorodeoxyglucose positron emission tomography; tumor vascular–metabolic relationships graft-versus-host disease 209, 211 Greece, ancient 2–3 growth, tumor see tumor growth hardening, beam see beam hardening effects Harvey, William 8–9 head and neck cancer 100–6 imaging 101–2 predicting response to therapy 102–5, 103, 104, 105, 207 treatment response assessment 200, 200–1

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hematological malignancies 78 hepatic arterial fraction (HAF; hepatic perfusion index, HPI) 35, 62, 183 calculation 181–3, 182 dynamic liver scintigraphy 188 liver metastases 184, 185 pathophysiology of changes 189, 190 slope-ratio methods 176–7 validation in animal model 41, 42 hepatic artery 174, 174 input to liver 34–5 regulation of blood flow 175 hepatic blood flow 181 arterial (Fa) 181, 182, 183 portal (Fp) 182, 183 total (FT) 181, 182, 183 hepatic blood volume (HBV) 177 hepatic metastases see liver metastases hepatic perfusion index (HPI) see hepatic arterial fraction hepatic phosphorylation fraction index (HPFI) 225–6, 226 hepatocellular carcinoma 141, 141–2 Hippocrates 2–3 historical perspective 1–12 Hounsfield, Sir Godfrey 7–8, 11 Hounsfield units (HU or CT number) 20 Huang Ti 2 hypoxia, tumor 74, 100, 217 drugs targeting 199–200, 200 selective imaging 222 tumor metabolic response 217, 217–18, 221 hypoxia inducible factors (HIFs) 217, 217–18 image(s) frequency/interval 36–7, 51 number of 51 series, overall length of time 51

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image acquisition protocols 36–7, 50–5 image processing 61–71 rectal cancer 132–3, 132–4, 135 image segmentation 65–9 different tissue types 68–9, 70, 71 specific organs and tumor 66, 66, 67 vascular structures 66, 67–8 imaging angiogenesis 78–81, 82–3 history of development 6–8 see also specific imaging modalities impulse residue function (IRF) 20, 21, 21–2 convolution and 22–4, 23 flow scaled see flow scaled impulse residue function India, ancient 2 injection rates, contrast media 55–6 input arterial concentration function see arterial concentration function, input integrins 202 in vivo videomicroscopy 190–1 iodine doses injected 55, 58 enhancement 20, 38 perfusion calculations 58, 58–9 sensitivity, calibration for 114, 115, 116 tube voltage and 54, 54 iodine-containing contrast agents see contrast media, X-ray Ito (stellate) cells 175, 191 Johnson and Wilson model 17–19, 18, 29–30 adiabatic approximation 30–1 arrival time (T0) parameter 34 hepatic artery fraction 35 reproducibility studies 42 scanning protocol 37 validation 40

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Kety, Seymour 10 Kety–Schmidt method 10, 26 kinetic models 26–31 liver perfusion 177–8 validation 39–41 see also Johnson and Wilson model; two-compartment model laser thermal therapy 206 lenalidomide 74, 78 Leonardo da Vinci 5 liver complications of marrow transplantation 209, 211 glucose metabolism, perfusion CT Perfusion–PET 225, 225–6 hepatic artery and portal vein input 34–5 limitations of perfusion CT 191–2 perfusion physiology 174, 174–5 quantification of perfusion with CT 176–83 liver metastases 173–93 factors predicting survival 186, 187 in vivo videomicroscopy 190–1 novel antiangiogenic agents 202–4 occult microscopic 173–4, 185–6 other imaging techniques 188–9 pathogenesis of microcirculatory changes 189–91 CT Perfusion for detection 183–6, 184 renal cancer 156, 157 vascularity–metabolism relationships 219, 220 liver microcirculation 174–5 physiopathology of metastatic changes 189–91 quantitative measurement 175–83 liver scintigraphy, dynamic 188 liver tumors primary 141, 141–2 transcatheter arterial chemoembolization 206

validation of CT measurements 41, 42 vascularity–metabolism relationship 218, 219, 220 lung cancer 111–26 diagnostic evaluation 117–19, 118 enhancement thresholds 58 FDG-PET 115, 120–2 future developments 123–5 heterogeneity 122, 123 hypoxia inducible factors 218 image segmentation 66, 67 lymph node metastases 164, 166, 166 non-small-cell 112, 120, 124 pathology 112 perfusion thresholds 59 potential limitations 122–3 predicting response to therapy 124, 207, 208 presentation 112 radiologic–pathologic correlations 119–20 small-cell 112, 120, 124 technical aspects 113–17, 114, 115, 117 treatment response assessment 124, 201, 201 vascularity–metabolism relationships 218–19, 219, 221 see also pulmonary nodules lymph node metastases 163–7 diagnosis 164, 164–6, 165, 166 growth curves 167, 167 lymph nodes 163–71 enlargement 163 technical issues 164 lymphoma 167–71, 169 primary cerebral 94–6 treatment response monitoring 168–71, 170 magnetic resonance angiography (MRA) 80

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magnetic resonance imaging (MRI) brain tumors 90 dynamic contrast-enhanced (DCE) 79, 81, 189 prostate cancer 149 rectal cancer 139 mammary adenocarcinoma, R3230AC rat-derived 39–40 maximum enhancement see peak enhancement maximum slope method 26 maximum tolerated dose (MTD) 81, 201–2 mean transit time (Tm; MTT) 16, 62 average, in region of interest 64–5, 68–9, 71 central volume principle 19–20 contrast enhancement protocols 56, 56 liver 177, 181, 182, 183 reproducibility studies 42–3 validation of measurement 40–1 MEDI-522 203, 204 meningiomas 91–2, 92, 93, 206 metastases angiogenesis and 74–5 brain 96 liver see liver metastases lymph node see lymph node metastases renal cancer 156, 157 micrometastases, detection in liver 173–4, 185–6 microvessel density (MVD) 202 lung cancer 120, 124 lymphoma 168 prostate cancer 149 rectal cancer 129, 137 renal cancer 158, 158 Miles, Ken 11–12 minimum transit time (Tmin; minTT) 21, 21–2 contrast enhancement protocols 56, 56 Mondino de Luzzi 4

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Moniz, Egas 6–7 Moore, George 10 myelodysplastic syndrome (MDS) 78 myeloma, multiple 78 nephrotoxicity, drug 209, 210 Nexavar see sorafenib noise 38, 54–5 spatial smoothing 62–4, 63, 64 no outflow assumption 25–6 validation 39, 43 oxygen-15-labeled water (H215O) 80, 81 oxygenation, tumor 100–1 pancreatic tumors 139–41, 140 parametric maps 61–2, 62, 63, 70–1 cerebral tumors 93 head and neck tumors 104, 105 liver 183, 184 pulmonary nodules 115, 115 rectal cancer 134, 135, 137, 138 partial volume averaging (PVA) 31–2 Patlak analysis 29 lung cancer 125 protocols 49, 53, 57 peak (maximum) enhancement 52–3, 62, 113–14 calculation of perfusion from 58, 58–9 image segmentation 66, 67, 67–8 pulmonary nodules 114–15, 119 perfusion see blood flow perfusion computed tomography (CTP) 15–43 basic terms 16–17 history of development 10–12 kinetics modeling 26–31 practical issues 36–8 protocols see protocols, perfusion CT Perfusion theory 15–36 validation 39–43 without kinetics modeling 25–6

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perfusion computed tomography–positron emission tomography (CT–PET) 48, 126, 215–27 advantages 215, 216 hepatic phosphorylation of glucose 225, 225–6, 226 treatment response assessment 222–5, 223, 224, 225 tumor aggression and 221–2 tumor vascular–metabolic relationships 218–21, 219 perfusion-weighted map 62, 68, 69 permeability surface area product see capillary permeability surface area product phantoms, iodine sensitivity 115, 116 physiology history of development 8–10 knowledge in ancient times 1–3 placental growth factor (PlGF) 75 platelet-derived growth factor (PDGF) 75, 202 portal vein 174, 174 input to liver 34–5 regulation of blood flow 175 positron emission tomography (PET) angiogenesis imaging 80, 81 combined with perfusion CT see perfusion computed tomography–positron emission tomography lymph nodes 163 rectal cancer 139 prediction of response to therapy 206–9 hepatic metastases 186, 187 lung cancer 124, 207, 208 rectal cancer 136 prostate cancer 147–56 combined perfusion CT–PET 224 detection and staging 147–52, 150, 151

perfusion–pathology correlation 152–3, 153, 154, 155 response to radiation therapy 153–5, 206 prostate gland, normal 148–50, 149 protocols, perfusion CT Perfusion 47–59, 48 choice 57 comparative results 57–9 contrast enhancement 55–7 example 49 image acquisition 50–5 pulmonary nodules 114, 114–17, 117 rectal cancer 130–1, 132 theoretical model used and 36–7 PS see capillary permeability surface area product pulmonary metastases, vascularity–metabolism relationships 219, 219 pulmonary nodules 111, 112, 113–26, 117 diagnostic evaluation 117–19, 118 enhancement/perfusion measurements 113–17, 114, 115, 117 fluorodeoxyglucose (FDG) PET 115, 119, 120–2 increasing numbers detected 112–13 limitations of perfusion CT Perfusion 122–3 small 122 strategy for investigating 121, 121–2 vascularity–metabolism relationships 219–21, 221 see also lung cancer R3230AC rat-derived mammary adenocarcinoma 39–40

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radiation dose 49–50 pulmonary nodules 123 scanning protocol and 37 signal-to-noise ratio and 38 radiotherapy (RT) assessment of complications 209 brain tumors 97–8, 98, 99, 206 predicting response to 102–5, 103, 104, 105, 207 prostate cancer 153–5, 206 response assessment 205–6 tumor oxygenation and 100 Radon, Johann 8 recirculation 35–6 RECIST criteria 77–8, 197, 198 rectal cancer 129–39 bevacizumab therapy 77, 138–9, 202, 203, 223, 224 chemoradiotherapy 136–7, 137, 206, 207–8 clinical experience 134–9 combined perfusion CT Perfusion–PET 223, 224 data and image analysis 132–3, 132–4, 135 patient preparation 130 technique 130–1, 132 vascularity–metabolism relationship 222 see also colorectal cancer regions of interest (ROI) 61 average mean transit time 64–5 image segmentation 65–9 liver 174, 176, 183 prostate cancer 152, 153 pulmonary nodules 114 rectal cancer 134, 135 Renaissance period 4–5 renal cancer (RCC) 156, 156–8 antiangiogenic therapy 77–8 metastatic lesions 156, 157 pathologic correlations 158, 159 reproducibility animal studies 41, 42 clinical studies 42–3

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residue function liver perfusion imaging 178–80, 179 see also flow scaled impulse residue function; impulse residue function; tumor residue function respiration gating 50, 191 suspended or quiet 50 respiratory motion 50 correction 37, 116, 192 kidneys 157 liver 191–2 pulmonary nodules 116–17, 123 response assessment, treatment see treatment response assessment Revlimid see lenalidomide RMP-7 199, 199, 203 Röntgen, Wilhelm Conrad 6 Santorio, Santorio 9 scanning protocols 36–7 see also image acquisition protocols scientific basis 15–43 scintigraphy, dynamic liver 188 segmentation, image see image segmentation signal-to-noise ratio 38 single compartment model 49 skeletal muscle tumors 40–1 slices, CT, number and thickness 51–3, 53 slope-ratio methods, liver perfusion imaging 176–7 solitary pulmonary nodules see pulmonary nodules sorafenib 74, 77–8, 81 spatial smoothing 62–4, 63, 64 spiral computed tomography (CT) systems 11–12 standardized enhancement value (SEV) 115

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standardized perfusion value (SPV) 52, 115 lung tumors 119, 120–1 renal cancer 158, 158 standardized uptake value (SUV) 115–16, 120–1 stellate (Ito) cells 175, 191 stroke, ischemic 96–7 SU5416 81 SU6668 203, 204 sunitinib (SU11248; Sutent) 74, 78, 81 T0 see appearance (arrival) time table toggling 52 taxane 200, 203 terms, basic 16–17 thalidomide 74, 78, 81, 224 Thornton, J. B. 1 time–density curve (TDC) arterial 23, 23–4, 35–6 tumor 21, 22–4, 23, 35–6 tirapazamine 222 transcatheter arterial chemoembolization, liver tumors 206 transfer constant see FE transfer flux of solute through capillary endothelium 17–19, 18 transfer function, tissue 178, 180 transrectal ultrasound (TRUS) 147 treatment complications, assessing 209 predicting response see prediction of response to therapy treatment response assessment 197–211, 203 brain tumors 99, 198–9, 199, 203 combined perfusion CT–PET 222–5, 223, 224, 225 drugs affecting tumor vasculature 198–201, 199, 200, 201 lung cancer 124, 201, 201

lymphoma 168–71, 170 other therapies 206 phase I trials of novel antivascular agents 201–5 prostate cancer 153–5 radiotherapy 205–6 rectal cancer 136–9, 137, 138 tube, X-ray see X-ray tube tumor enhancement 58 calculation of perfusion from 58, 58–9 peak (maximum) see peak enhancement protocols 55–7 tumor growth angiogenesis and 74–5 nodal metastases and 167, 167 tumor perfusion characteristics 76–7 comparison with enhancement 58, 58–9 measurement 15 see also blood flow tumor progression 74–5 tumor residue function (tumor TDC) 21, 22–4, 23, 35–6 tumor vascular–metabolic relationships 217–25 after therapy 222–5 biomolecular mediators 217, 217–18 imaging studies 218–21, 219, 220, 221 tumor aggression and 221–2 tumor vasculature 73 assessing response to treatment 198–201, 199, 200, 201 characteristics 76, 76–7 two-compartment model 27, 27–9 arrival time (T0) parameter 34 hepatic artery fraction 35 protocols 49 scanning protocol 36, 37 validation 39–40 see also Patlak analysis

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ultrasound (US) contrast-enhanced 80, 148 transrectal (TRUS) 147 see also Doppler ultrasound urogenital tract tumors 147–59 validation 39–43 animal tumor models 39–41, 42 clinical studies 42–3 vascular disrupting agents (VDAs) 198 vascular endothelial growth factor (VEGF) 75, 202 pulmonary nodules 120 therapeutic inhibitors 74, 77 vascular structures, image segmentation 66, 67–8 vatalanib 81

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venous outflow, assumption of no 25–6, 39 Vesalius, Andreas 5–6, 9 videomicroscopy, in vivo 190–1 volume of distribution see distribution volume VX2 tumor cells 40–1, 42 Weibull functions 180 X-ray contrast agents see contrast media, X-ray X-rays discovery 6 exposure factors 53–5 X-ray tube current (mA) 53–5 voltage (kVp) 38, 53–5, 54

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