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KHAN’S Treatment Planning in Radiation Oncology

FIFTH EDITION

KHAN’S Treatment Planning in Radiation Oncology

FIFTH EDITION

E D I T O R S Paul W. Sperduto, MD, MPP, FASTRO Radiation Oncologist Minneapolis Radiation Oncology Minneapolis, Minnesota John P. Gibbons, PhD Chief Medical Physicist Department of Radiation Oncology

Acquisitions Editor: Nicole Dernoski Development Editor: Ariel S. Winter Editorial Coordinator: Vinodhini Varadharajalu Editorial Assistant: Maribeth Wood Marketing Manager: Kirsten Watrud Production Project Manager: Barton Dudlick Design Coordinator: Stephen Druding Manufacturing Coordinator: Beth Welsh Prepress Vendor: Lumina Datamatics

Copyright © 2016 Wolters Kluwer. Copyright © 2012 Lippincott Williams & Wilkins, a Wolters Kluwer business. Copyright © 2006 Lippincott Williams & Wilkins, a Wolters Kluwer business. Copyright © 1998 by Lippincott Williams & Wilkins. All rights reserved. This book is protected by copyright. No part of this book may be reproduced or transmitted in any form or by any means, including as photocop ies or scanned-in or other electronic copies, or utilized by any information storage and retrieval system without written permission from the copyright owner, except for brief quotations embodied in critical articles and reviews. Materials appearing in this book prepared by individu als as part of their official duties as U.S. government employees are not covered by the above-mentioned copyright. To request permission, please contact Wolters Kluwer at Two Commerce Square, 2001 Market Street, Philadelphia, PA 19103, via email at permissions@lww.com, or via our website at lww.com (products and services).

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Printed in China Library of Congress Cataloging-in-Publication Data

Names: Sperduto, Paul W., editor. | Gibbons, John P., Jr., editor. Title: Khan’s treatment planning in radiation oncology / editors, Paul Sperduto, MD, MPP, FASTRO, Radiation Oncologist, Minneapolis Radiation Oncology, Minneapolis, Minnesota, John Gibbons, PhD, Chief Medical Physicist, Department of Radiation Oncology, Ochsner Health System, New Orleans, Louisiana. Other titles: Treatment planning in radiation oncology. Description: Fifth edition. | Philadelphia, PA : Wolters Kluwer, [2022] | Includes bibliographical references and index. Identifiers: LCCN 2021038919 | ISBN 9781975162016 (hardback) | ISBN 9781975162047 (ebook) Subjects: LCSH: Cancer--Radiotherapy--Planning--Computer programs. | BISAC: MEDICAL / Oncology / General | MEDICAL / Radiology, Radiotherapy & Nuclear Medicine Classification: LCC RC271.R3 T74 2022 | DDC 616.99/40642--dc23 LC record available at https://lccn.loc.gov/2021038919

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To Teddi Dawn, my sister who had Down Syndrome, who inspired my interest in science and sadly died from COVID-19 during the writing of this book. I am forever grateful for your joy and mischief and forever saddened that I could not be with you or help you understand. —Paul W. Sperduto

To my children Valerie, Britton, Jay, Madison, Jack, and Vivienne who have brought much joy to my life —John P. Gibbons

Contributors

Judith Adams, CMD Proton Training and Development Specialist Radiation Oncology Massachusetts General Hospital Boston, Massachusetts Karthik Adapa, MBBS, MPP, MPH Doctoral Candidate Department of Radiation Oncology School of Medicine

Rachel C. Blitzblau, PhD Associate Professor Radiation Oncology Duke University Durham, North Carolina Stefan Both, PhD

Professor & Head of Medical Physics Department of Radiation Oncology University Medical Center Groningen Groningen, the Netherlands Frank J. Bova, PhD Professor Neurosurgery University of Florida Gainesville, Florida Andrew Brandmaier, MD, PhD Assistant Professor Department of Radiation Oncology Weill Cornell Medicine New York, New York David Carpenter, MD, MHSc Resident Physician Duke Cancer Institute Department of Radiation Oncology Durham, North Carolina Robert L. Carver, PhD Physicist Mary Bird Perkins Cancer Centre Baton Rouge, Louisiana Colin E. Champ, MD, CSCS Associate Professor

University of North Carolina Chapel Hill, North Carolina Alison N. Amos, PhD Clinical Assistant Professor Department of Radiation Oncology Division of Healthcare Engineering University of North Carolina Chapel Hill, North Carolina John A. Antolak, PhD

Associate Professor & Consultant Department of Radiation Oncology Mayo Clinic Rochester, Minnesota Elizabeth H. Baldini, MD Radiation Oncology Director, Sarcoma Center Radiation Oncology Dana-Farber Cancer Institute/Brigham and Women’s Hospital Boston, Massachusetts James M. Balter, PhD, FAAPM Professor and Director of Physics Research Department of Radiation Oncology

University of Michigan Ann Arbor, Michigan Christopher Beltran, PhD Chair, Medical Physics Department of Radiation Oncology Mayo Clinic Jacksonville, Florida

Department of Radiation Oncology Duke University Medical Center Durham, North Carolina Albert Chang, MD, PhD Associate Professor and Director of Brachytherapy Radiation Oncology University of California, Los Angeles Los Angeles, California

vii

viii

Contributors

Zhe (Jay) Chen, PhD Professor

Lei Dong, PhD Professor and Director of Medical Physics Department of Radiation Oncology Hospital of the Friedman University of Pennsylvania Philadelphia, Pennsylvania Robert L. Foote, MD Hitachi Professor of Radiation Oncology Research Radiation Oncology Mayo Clinic College of Medicine and Science Rochester, Minnesota William A. Friedman, MD Professor Neurosurgery

Department of Therapeutic Radiology Yale University School of Medicine Yale-New Haven Hospital New Haven, Connecticut Yen-Lin Chen, MD Assistant Radiation Oncologist Department of Radiation Oncologist Massachusetts General Hospital

Boston, Massachusetts Bhisham Chera, MD Associate Professor Associate Chair of Clinical Operations & Improvement Director of Patient Safety and Quality Department of Radiation Oncology University of North Carolina School of Medicine Chapel Hill, North Carolina James C. L. Chow, PhD Medical Physicist/Associate Professor Radiation Medicine Program/Department of Radiation Oncology Princess Margaret Cancer Centre, University Health Network/University of Toronto Toronto, Ontario, Canada Benjamin M. Clasie, PhD Department of Radiation Oncology Massachusetts General Hospital & Harvard Medical School Boston, Massachusetts Brian G. Czito, MD Professor Duke Cancer Institute Department of Radiation Oncology Durham, North Carolina Shiva Das, PhD Professor Department of Radiation Oncology University of North Carolina at Chapel Hill Chapel Hill, North Carolina Thomas F. DeLaney, MD Andres Soriano Professor of Radiation Oncology Massachusetts General Hospital Boston, Massachusetts Nicolas Depauw, PhD, DABR Physics Proton Treatment Planning Lead Radiation Oncology Massachusetts General Hospital Boston, Massachusetts

University of Florida Gainesville, Florida Yolanda I. Garces, MS, MD Associate Professor Radiation Oncology Mayo Clinic Northfield, Minnesota John P. Gibbons, PhD Chief Medical Physicist Department of Radiation Oncology

Ochsner Health System New Orleans, Louisiana Andrew Godley, PhD Director of Clinical Physics

Department of Radiation Oncology UT Southwestern Medical Center Dallas, Texas Vinai Gondi, MD Co-Director, Brain and Spine Tumor Center Director of Research and Education Northwestern Medicine Cancer Center Warrenville

Warrenville, Illinois Deen Gu, MHA Operations Manager Radiation Oncology

University of North Carolina Chapel Hill, North Carolina Kenneth R. Hogstrom, PhD

Professor Emeritus and Senior Medical Physics Advisor Department of Physics and Astronomy, Louisiana State University Radiation Oncology, Mary Bird Perkins Cancer Center Baton Rouge, Louisiana

Contributors

Myrsini Ioakeim–Ioannidou, MD Post-doctoral Research Fellow Radiation Oncology Massachusetts General Hospital Boston, Massachusetts Andrew Jackson, PhD Attending Medical Physicist Department of Medical Physics Memorial Sloan-Kettering Cancer Center New York, New York James A. Kavanaugh, PhD

Guang Li, PhD Associate Attending Physicist Department of Medical Physics Memorial Sloan Kettering Cancer Center New York, New York Jonathan G. Li, PhD Professor Radiation Oncology University of Florida Gainesville, Florida Andrew S. Lim, MD, FRCPC Radiation Oncologist University of California, Los Angeles Los Angeles, California Mu-Han Lin, PhD Director of Treatment Planning Radiation Oncology UT Southwestern Medical Center Dallas, Texas Shannon M. MacDonald, MD Associate Professor of Radiation Oncology Massachusetts General Hospital

Assistant Professor Radiation Oncology Washington University School of Medicine St. Louis, Missouri Paul J. Keall, PhD Professor and Director, ACRF Image X Institute Faculty of Medicine and Health University of Sydney Sydney, Australia Faiz M. Khan, PhD Professor Emeritus Department of Radiation Oncology University of Minnesota Medical School Minneapolis, Minnesota Jonathan P. S. Knisely, MD Assistant Professor Interim Department of Radiation Oncology Weill Cornell Medicine and New York Presbyterian Hospital New York, New York Hanne M. Kooy, PhD Associate Professor Radiation Oncology Massachusetts General Brigham & Harvard Medical School Boston, Massachusetts Rupesh Kotecha, MD Chief of Radiosurgery Department of Radiation Oncology Miami Cancer Institute, Baptist Health South Florida Miami, Florida Gerald J. Kutcher, PhD Professor of History Institution, Department of History

Boston, Massachusetts Gig S. Mageras, PhD Emeritus Medical Physics Memorial Sloan Kettering Cancer Center New York, New York Lawrence B. Marks, MD Dr. Sidney K. Simon Distinguished Professor of Oncology Research Professor and Chair, Department of Radiation Oncology Lineberger Cancer Center University of North Carolina Chapel Hill, North Carolina Jyoti Mayadev, MD Associate Professor of Radiation Medicine and Applied Sciences UC San Diego

San Diego, California Charles Mayo, PhD Professor Radiation Oncology University of Michigan Ann Arbor, Michigan

Binghamton University Binghamton, New York

Contributors

Lukasz M. Mazur, PhD Associated Professor and Director of Division of Healthcare Engineering Department of Radiation Oncology

Dominic H. Moon, MD Assistant Professor

Department of Radiation Oncology UT Southwestern Medical Center Dallas, Texas Arno J. Mundt, MD Professor and Chair Radiation Medicine and Applied Sciences UC San Deigo La Jolla, California Himanshu Nagar, MD Weill Cornell Medicine

University of North Carolina Chapel Hill, North Carolina Susan G.R. McDuff, MD, PhD Assistant Professor Department of Radiation Oncology Duke Cancer Center

Durham, North Carolina Ross McGurk, PhD Assistant Clinical Professor Radiation Oncology

New York, New York Colin Orton, PhD Professor Emeritus Wayne State University Detroit, Michigan Niko Papanikolaou, PhD Professor and Director University of Texas UTHSCSA: The University of Texas Health Science Center at San Antonio Medical Physics

University of North Carolina Chapel Hill, North Carolina Todd R. McNutt, PhD Associate Professor Radiation Oncology and Molecular Radiation Sciences Johns Hopkins Medicine Baltimore, Maryland Minesh P. Mehta, MD Medical Doctor Deputy Director and Chief of Radiation Oncology Radiation Oncology Miami Cancer Institute Miami, Florida Loren K. Mell, MD Tenured Professor and Vice chair Radiation Medicine and Applied Science UC San Diego San Diego, California Dimitris N. Mihailidis, PhD Associate Professor Radiation Oncology University of Pennsylvania Perelman School of Medicine Philadelphia, Pennsylvania Jessica Miller, PhD, DABR Associate Professor Department of Human Oncology School of Medicine & Public Health University of Wisconsin Madison, Wisconsin Radhe Mohan, PhD Professor Department of Radiation Physics The University of Texas MD Anderson Cancer Center Houston, Texas

San Antonio, Texas Ima Paydar, MD Department of Radiation Oncology University of Pennsylvania Philadelphia, Pennsylvania Garrett M. Pitcher, PhD Academic Medical Physicist Mary Bird Perkins Cancer Center Baton Rouge, Louisiana John P. Plastaras, MD, PhD Professor Department of Radiation Oncology University of Pennsylvania Philadelphia, Pennsylvania Dominique Rash, MD Assistant Clinical Professor Radiation Medicine and Health Sciences University of California, San Diego San Diego, California Francisco J. Reynoso, PhD Assistant Professor Department of Radiation Oncology Washington University in St. Louis St. Louis, Missouri

Contributors

Susan Richardson, PhD Medical Physicist Radiation Oncology Swedish Cancer Institute Seattle, Washington, DC

Alexander Sun, MD, FRCPC Associate Professor Department of Radiation Oncology University of Toronto Staff Radiation Oncologist Princess Margaret Cancer Centre Toronto, Ontario, Canada Nancy J. Tarbell, MD, FASTRO CC Wang Professor of Radiation Oncology Massachusetts General Hospital/Harvard Medical School Francis H. Burr Proton Therapy Center Boston, Massachusetts Bruce R. Thomadsen, PhD Professor Medical Physics University of Wisconsin Madison, Wisconsin Robert D. Timmerman, MD Professor, Vice Chair Department of Radiation Oncology University of Texas Southwestern Medical Center Dallas, Texas Jordan Torok, MD

Mark J. Rivard, PhD, FAAPM Professor of Radiation Oncology Department of Radiation Oncology Brown University

Providence, Rhode Island Kilian E. Salerno, MD Radiation Oncologist National Cancer Institute Radiation Oncology Branch

Bethesda, Maryland Bret Shultz, MHA Research Project Manager Department of Radiation Oncology University of North Carolina Chapel Hill, North Carolina Aaron B. Simon, MD, PhD

Assistant Professor Radiation Oncology University of California Irvine Orange, California Daniel R. Simpson, MD Assistant Professor Department of Radiation Medicine and Applied Sciences Moores Cancer Center UCSD School of Medicine La Jolla, California Paul W. Sperduto, MD, MPP, FASTRO Medical Director, Minneapolis Radiation Oncology Co-Director, University of Minnesota Gamma Knife Center Minneapolis, Minnesota Kevin L. Stephans, MD Assistant Professor Department of Radiation Oncology Taussig Cancer Institute, Cleveland Clinic Cleveland, Ohio Kenneth R. Stevens Jr., MD Former Chair and Professor Emeritus Radiation Medicine Department Oregon Health & Sciences University Portland, Oregon

Assistant Professor Radiation Oncology UPMC Saint Clair Hospital Cancer Center Pittsburgh, Pennsylvania Jan Unkelbach, PhD Assistant Professor of Medical Physics Radiation Oncology University Hospital Zurich Zurich, Switzerland Raj Varadhan, PhD, DABR, DABMP

Director of Physics/Technology Minneapolis Radiation Oncology Edina, Minnesota Gregory M.M. Videtic, MD, CM, FRPC, FACR, FASTRO Professor of Medicine Cleveland Clinic Lerner College of Medicine Staff Physician Department of Radiation Oncology, Taussig Cancer Center

Cleveland Clinic Cleveland, Ohio

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Contributors

Kenneth J. Weeks, PhD Medical Physicist

Catheryn M. Yashar, MD Professor and Vice Chair Clinical Affairs Department of Radiation Medicine and Applied Science University of California San Diego La Jolla, California Fang-Fang Yin, PhD Professor Department of Radiation Oncology Duke Clinics Durham, North Carolina Sua Yoo, PhD Associate Professor

Federal Medical Center Butner, North Carolina Christopher G. Willett, MD, FASTRO Professor and Chairman Department of Radiation Oncology Duke University Durham, North Carolina Neil M. Woody, MD, MS Department of Radiation Oncology Taussig Cancer Institute, Cleveland Clinic Cleveland, Ohio Binbin Wu, PhD Senior Medical Physicist Radiation Oncology & Molecular Radiation Sciences Johns Hopkins University Baltimore, Maryland Poonam Yadav, PhD, DABR Associate Professor, Lead MR Linac Program Department of Radiation Oncology Northwestern Memorial Hospital Northwestern University Feinberg School of Medicine Chicago, Illinois Yun Yang, PhD, DABR Assistant Professor of Radiation Oncology Department of Radiation Oncology Brown University Alpert School of Medicine Rhode Island Hospital Providence, Rhode Island

Department of Radiation Oncology Duke University Medical Center Durham, North Carolina Stephanie M. Yoon, MD Physician Department of Radiation Oncology University of California Los Angeles, California Ellen D. Yorke, PhD Attending Physicist Memorial Sloan Kettering Cancer Center Department of Medical Physics New York, New York

Preface

This edition includes new chapters and updates of prior chapters designed to keep pace with the many exciting innovations in radiation oncology. It is our hope that this edition will bring readers from yesterday’s standard of care to today’s state of the art and provide a peek over the horizon at the dawn of a new era in which the science and the art of radiation oncology come together as never before. The science includes new understanding of the potential lurking within discoveries in physics, biology, and technology. The art is the integrated clinical application of those discoveries, in con cert with advances in systemic therapies. For example, there is a new chapter on the treatment planning implications of combined immunotherapy and radiation. This and other chapters hold clues that may lead us beyond local control of an individual tumor to a future in which a systemic response may be ignited by the application of modern radiation techniques and systemic therapies in proper sequence and intensity. In addi tion, there is a re-focus on the patient, beginning with a new chapter on treatment planning and patient safety. As in previous editions, this textbook provides a comprehensive discussion of the clinical, physical, and biological aspects of treatment planning. Because of its primary focus on treatment planning, it covers this subject in more depth than other books dedicated purely to medical physics or clinical radiation oncology. This book is written for the entire treatment planning team, namely the radiation oncologist, medical physi cist, dosimetrist, and radiation therapist. In addition, we keep the students in focus by including key points and study questions at the end of each chapter. We are immensely grateful for the time and expertise of the chapter authors and acknowledge them as world-renowned experts on their respective topics. We also acknowledge Anne Malcolm, Senior Managing Editor, Ariel Winter, Development Edi tor, and the other editorial staff of Wolters Kluwer for their support and patience in the development and production of this book. Most importantly, we are deeply indebted to Faiz Khan for his passion for excellence, joy for teaching, youthful curiosity, and warm friendship which continue to inspire us both personally and professionally.

Paul W. Sperduto John P. Gibbons

xiii

Preface to First Edition

Traditionally, treatment planning has been thought of as a way of devising beam arrange ments that will result in an acceptable isodose pattern within a patient’s external contour. With the advent of computer technology and medical imaging, treatment planning has developed into a sophisticated process whereby imaging scanners are used to define target volume, simulators are used to outline treatment volume, and computers are used to select optimal beam arrangements for treatment. The results are displayed as isodose curves over laid on multiple body cross-sections or as isodose surfaces in three dimensions. The intent of the book is to review these methodologies and present a modern version of the treat ment planning process. The emphasis is not on what is new and glamorous, but rather on techniques and procedures that are considered to be the state of the art in providing the best possible care for cancer patients. Treatment Planning in Radiation Oncology provides a comprehensive discussion of the clinical, physical, and technical aspects of treatment planning. We focus on the application of physical and clinical concepts of treatment planning to solve treatment planning prob lems routinely encountered in the clinic. Since basic physics and basic radiation oncology are covered adequately in other textbooks, they are not included in this book. This book is written for radiation oncologists, physicists, and dosimetrists and will be useful to both the novice and those experienced in the practice of radiation oncology. Ample references are provided for those who would like to explore the subject in greater detail. We greatly appreciate the assistance of Sally Humphreys in managing this lengthy proj ect. She has been responsible for keeping the communication channels open among the editors, the contributors, and the publisher. Faiz M. Khan Roger A. Potish

Contents

Contributors

vii

Preface xiii Preface to the First Edition

Gynecologic Malignancies 240 Aaron B. Simon, Daniel R. Simpson, Loren K. Mell, Dominique Rash, Jyoti Mayadev, Arno J. Mundt, and Catheryn M. Yashar The Lymphomas 269 John P. Plastaras, Stefan Both, and Ima Paydar Cancers of the Skin, Including Mycosis Fungoides 284 Zhe (Jay) Chen, James C. L. Chow, Alexander Sun, Himanshu Nagar, Kenneth R. Stevens Jr., and Jonathan P. S. Knisely Pediatric Malignancies 309 Myrsini Ioakeim–Ioannidou, Shannon M. MacDonald, and Nancy J. Tarbell Soft Tissue and Bone Sarcoma 330 Kilian E. Salerno, Yen-Lin Chen, Thomas F. DeLaney, and Elizabeth H. Baldini

CHAPTER 11

SECTION I Treatment Planning: Safety and Biological Principles 1 CHAPTER 1 Patient Safety 2

CHAPTER 12

Lukasz M. Mazur, Lawrence B. Marks, Shiva Das, Ross McGurk, Karthik Adapa, Alison N. Amos, Deen Gu, Bret Shultz, and Bhisham Chera Normal Tissue Tolerance 26 Dominic H. Moon and Robert D. Timmerman Fractionation: Radiobiologic Principles and Clinical Practice 41 Colin Orton Immunologic Principles of Treatment Planning: Radiation as In Situ Vaccine and Dose Fractionation in the Immunotherapy Era 56 Andrew Brandmaier

CHAPTER 13

CHAPTER 2

CHAPTER 3

CHAPTER 14

CHAPTER 4

CHAPTER 15

SECTION III Treatment Planning: Physics and Dosimetric Principles 365 CHAPTER 16 Introduction: Process, Equipment, and Personnel 366 Faiz M. Khan and John P. Gibbons CHAPTER 17 Image-Guided Radiation Therapy 374 Guang Li, Gig S. Mageras, Lei Dong, and Radhe Mohan

SECTION II Treatment Planning: Site-Specific Cancers 67 CHAPTER 5

Cancers of the Central Nervous System 68 Vinai Gondi, Paul W. Sperduto, and Minesh P. Mehta Cancers of the Head and Neck 87 Yolanda I. Garces, Charles Mayo, Christopher Beltran, and Robert L. Foote

CHAPTER 6

Deformable Image Registration 404 Raj Varadhan Patient Simulation 420 Dimitris N. Mihailidis and Niko Papanikolaou Treatment Planning Algorithms: Photon Dose Calculations 437 John P. Gibbons

CHAPTER 18

Cancers of the Thorax/Lung 108 Gregory M.M. Videtic, Rupesh Kotecha, Neil M. Woody, and Kevin L. Stephans

CHAPTER 7

CHAPTER 20

Breast Cancer 134 Susan G.R. McDuff, Colin E. Champ, Sua Yoo, and Rachel C. Blitzblau Cancers of the Gastrointestinal Tract 155 David Carpenter, Jordan Torok, Christopher G. Willett, Fang-Fang Yin, and Brian G. Czito Cancer of the Genitourinary Tract: Prostate, Bladder, and Testicular Cancer 189 Stephanie M. Yoon, Andrew S. Lim, and Albert Chang

CHAPTER 8

Treatment Planning Algorithms: Brachytherapy 452 Kenneth J. Weeks

CHAPTER 21

CHAPTER 9

Treatment Planning Algorithms: Electron Beams 467 Kenneth R. Hogstrom, Garrett M. Pitcher, Robert L. Carver, and John A. Antolak

CHAPTER 22

CHAPTER 10

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xviii

Contents

Low Dose-Rate Brachytherapy 613 Yun Yang and Mark J. Rivard

Treatment Planning Algorithms: Proton Therapy 496 Hanne M. Kooy, Benjamin M. Clasie, and Nicolas Depauw Commissioning and Quality Assurance 507 Francisco J. Reynoso and James A. Kavanaugh Intensity-Modulated Radiation Therapy: Photons 528 Jan Unkelbach Linac Radiosurgery: System Requirements, Procedures, and Testing 570 Frank J. Bova, William A. Friedman, and Jonathan G. Li Stereotactic Ablative Radiotherapy 595 Mu-Han Lin, Andrew Godley, and Robert D. Timmerman Patient and Organ Movement 560 Paul J. Keall and James M. Balter

CHAPTER 29

CHAPTER 23

High-Dose-Rate Brachytherapy Treatment Planning 629 Bruce R. Thomadsen, Jessica Miller, Poonam Yadav, and Susan Richardson Electron Beam Treatment Planning 665 John A. Antolak

CHAPTER 30

CHAPTER 24

CHAPTER 31

CHAPTER 25

Proton Beam Therapy 690 Hanne M. Kooy, Judith Adams, and Nicolas Depauw Treatment Plan Evaluation 704 Ellen D. Yorke, Andrew Jackson, and Gerald J. Kutcher

CHAPTER 32

CHAPTER 26

CHAPTER 33

CHAPTER 27

Knowledge-Based Treatment Planning 722 Todd R. McNutt, John P. Gibbons, and Binbin Wu

CHAPTER 34

CHAPTER 28

Index 735

Treatment Planning Algorithms: Photon Dose

Calculations 20

John P. Gibbons

calculation models are an area of continuous development, and it is likely that each commercial vendor’s implemen tation of one or more of these models will differ in many respects. Nevertheless, the intent is to provide a basic under standing of the principles behind each of these algorithms. REPRESENTATION OF THE PATIENT FOR DOSE PLANNING Patient representation has evolved dramatically over the past 50 years. Initially, patients were considered as a flat water phantom of a specific source-to-surface distance (SSD) and depth for use in simple dose or monitor unit calculations. Development of external contour tools aided the treatment planner in determining patient-specific dose distributions. Such procedures resulted in the patient being represented as a homogeneous composition (i.e., water) but did allow for the application of surface corrections to the calculation. Patient heterogeneities could be represented in simple ways, such as using internal contours with assigned densities. The electron density to assign to the region could be inferred from CT atlases or, if available, the mean patient-specific CT number within the contoured structure. 3 The problem with this approach was that tissues such as lung and bone are not themselves homogeneous, and their density varia tions would not be taken into account using this approach. All modern radiotherapy systems use volumetric imag ing data to characterize the patient in a 3D voxel-by-voxel description. The most common imaging dataset used for radiotherapy treatment planning is a treatment-planning CT scan, obtained using a conventional CT simulator. A CT dataset of the treatment region constitutes the most accurate representation of the patient applicable for dose computation, primarily because of the one-to-one rela tionship between CT number and physical and/or elec tron density. 3 Dose algorithms that can use the density representation on a point-by-point basis are easier for heterogeneous calculations because contouring of the het erogeneities is typically not required. An exception to this occurs when data are present within the CT scan which will not be present for the treatment. One obvious exam ple is the CT-simulator couch, which is either manually or

INTRODUCTION Computerized treatment planning systems have been uti lized in radiotherapy planning since the 1950s. The first computer algorithm used has been attributed to Tsien 1 who used punch cards to store isodose distributions to allow for the addition of multiple beams. Since that time, advancements in computer speeds and algorithm devel opment have vastly improved our capability to predict photon dose distributions in patients. In an early attempt to classify computer planning algorithms, the International Commission on Radia tion Units and Measurement (ICRU) Report 42 2 divided photon dose calculation methods into two categories: empirical and model-based algorithms. Early empirical algorithms such as Bentley–Milan were developed using clinical beam data measured on a flat water phantom as input. Corrections were then made to incorporate various effects, such as changes in patient external contour, block ing or physical wedges, and so forth. Eventually, patient heterogeneity correction factors were incorporated, but these were applied afterward, that is, after water-based calculations were performed assuming a homogeneous patient geometry. Most of this development occurred prior to the advent of computed tomography (CT), or at least before the incorporation of CT images into the radiotherapy planning process. However, eventually the commercial utilization of empirical algorithms faded. In the early 1990s, three dimensional conformal radiation therapy (3D CRT) began to use patient-specific CT-image data in the plan ning process. Initially, this was limited to virtual simula tion. At that time computer-based algorithms that could incorporate the newly available volumetric density infor mation and compute true 3D dose distributions in a rea sonable amount of time were not yet available. In order to fully utilize this new information, it was necessary to develop new algorithms that could incorporate variations in individual patient anatomy. As a result, most, if not all, commercial treatment planning systems have moved to model-based photon calculation methods. In this chapter, we describe three photon calculation models currently in use in radiotherapy clinics. Photon

437

438 SECTION III Treatment Planning: Physics and Dosimetric Principles

automatically removed and, in some cases, replaced with a treatment couch by the planning system. Also relevant are temporary contrast agents that can produce a CT num ber that mimic a higher density material within the body. Usually, the contrast agent is used to aid in the tissue seg mentation, and so only the additional step of providing a more realistic CT number in the segmented region is required to correct for the presence of the contrast agent. The spatial reliability of CT scanners is typically within 2%, which leads to dose uncertainties of ∼ 1%. 3 Other imaging modalities provide information that will aid in the location and delineation of structures, but is of less value in the calculation of dose. For example, the advent of cone-beam CT within the treatment room provides invaluable information regarding patient alignment. How ever, the scatter contained within the images makes accu rate determination of density difficult. Although magnetic resonance imaging (MRI) is often able to provide superior tissue contrast, the information in MRI is not strongly related to electron density. Furthermore, MRI images are more prone to artifacts during image formation, which will degrade the quality of the calculated dose distributions. In addition to electron density, it is also necessary to determine the tissue composition for more modern calcula tion algorithms. In convolution/superposition algorithms, fluence attenuation tables are typically computed using mass-attenuation coefficient data, which are somewhat weakly dependent on material. Often these coefficients are determined for each voxel by linearly interpolating between published results of two different materials (e.g., water and bone) based on the density assigned to the voxel. For both Monte Carlo (MC) and Boltzmann transport calculations, a full material assignment must be made to allow for accu rate cross-sectional determination of both photon and elec tron transport throughout the patient volume. Ideally, the size of the voxels in the treatment planning CT should be close to the dose grid resolution used for calculation. A CT volume set typically consists of 50 to 200 images with a voxel matrix dimension of 512 × 512 for each image. For a 50-cm field of view, this corresponds to a voxel size of ∼ 1 mm in the transverse direction. The longitudinal voxel size depends on the slice thickness, but is typically from 2 to 5 mm. In many planning sys tems, the CT slice thickness is chosen as the voxel size of the dose grid. For these systems, it may be appropriate to downsample the CT image set to 256 × 256. This makes the transverse resolution more closely matched to that of the longitudinal direction, with only a minor degrada tion in the image. Degrading the resolution further from 256 × 256 may result in an unacceptable loss of detail. BASIC RADIATION PHYSICS FOR PHOTON BEAM DOSE CALCULATION Here, we present an introduction to the important aspects of X-ray production and interaction to understand the

capabilities and limitations of model-based photon treat ment planning algorithms.

Megavoltage Photon Production Figure 20.1 displays a cross-sectional view of a linear accelerator treatment head, which consists of a high- density shielding material such as lead, tungsten, or a lead-tungsten alloy. It consists of an X-ray target, flatten ing filter, ion chamber, and a primary and movable colli mator. High-energy electrons are accelerated in the linac’s accelerating structure and impinge on the X-ray target. The production of Bremsstrahlung, or braking radiation, occurs when the high-energy electrons strike a tungsten target located in the head of the accelerator. The size of the focal spot of the electrons on the target is on the order of a few millimeters. 4 This finite size contributes to the penum bra or the blurring of the beam near the edges of the field. A primary collimator, fabricated from a tungsten alloy, defines the maximum field diameter that can be used for treatment. At megavoltage energies, Bremsstrahlung is directed primarily in the forward direction. In most conventional C-arm accelerators, to make the beam intensity more uniform, a conical filter positioned in the beam prefer entially absorbs the photon fluence along the central axis. The presence of the field-flattening filter alters the energy spectrum, since the beam passing through the thicker central part of the filter has a higher proportion of low energy photons absorbed by the filter. This may not be necessary for modern treatment deliveries where modu lation is used to vary the intensity of the beam. Indeed, many treatment units now have the option of removing the filter for these treatments (e.g., Varian TrueBeam, Palo Alto, CA; Elekta Versa HD, Atlanta, GA), or have removed the flattening filter entirely (e.g., Accuray, Inc. TomoHD, Sunnyvale, CA) when a uniform field is not needed. Compton Scatter Photons can inelastically scatter via three main processes: photoelectric absorption, incoherent (Compton) scattering with atomic electrons, and pair production in the nuclear or electron electromagnetic field. In the energy range used for radiation therapy, most interactions are Compton scat tering events, which are discussed in more detail here. Compton-scattered photons may originate in either the accelerator treatment head or the patient (or phan tom). Most of the scatter dose generated by the accelera tor head is produced within the primary collimator and the field-flattening filter. These scattered photons and electrons are sometimes referred to as “extrafocal radia tion” which may be added to the primary photon beam emitted from the source. As the collimator jaws open, more scattered radiation is allowed to leave the treatment head, which results in an increase in the machine output with field size. This effect is known as collimator scatter , 5

CHAPTER 20 Treatment Planning Algorithms: Photon Dose Calculations 439

Electron beam

Primary collimator X-ray target

Flattening filter

Forward peaked X-ray beam

Scattering foil

Carrousel

Ion chamber

Secondary collimator

Flattened X-ray beam

Slot for wedges, blocks, compensators

A Patient FIGURE 20.1. Components of the treatment head of a linear accelerator. A: A cross-sectional view of the treatment head operat ing in X-ray therapy mode. B: A cut-away diagram of the linac. (Image(s) courtesy of Varian Medical Systems, Inc., Palo Alto, CA. Copyright [2021]. All rights reserved.) B

set in motion from the site of the photon interaction. At megavoltage energies, the range of charged particles can be several centimeters. The charged particles are mainly set in forward motion but are scattered consider ably as they slow down and come to rest. Electrons lose energy by two processes: inelastic collisions within the media (primarily with target electrons) and radiative interactions (primarily with target nucleus). Inelastic collisions that ionize the target atom can lead to second ary electrons, known as delta rays. Radiative interactions occur via Bremsstrahlung, which effectively transfers the energy back to a photon. Equations thatmodel these coupled electron–photon interactions are described later on. The indirect nature of photon dose deposition results in several features in photon dose distributions. Initially, the superficial dose increases or “builds up” from the sur face of the patient because of the increased number of charged particles being set in motion. This results in a low skin dose, the magnitude of which is inversely propor tional to the path length of the charged particles. The dose builds up to a maximum at a depth, d max , characteristic of the photon beam energy. At a point in the patient with a depth equal to the penetration distance of charged parti cles, charged particles coming to rest are being replenished by charged particles set in motion, and charged particle equilibrium (CPE) is said to be reached. In this case, the

although the collimator jaws themselves contribute little forward scatter. The photons scattered in the primary col limator and field-flattening filter also add to the fluence just outside the geometrical field boundary. Similar to the accelerator-produced scatter, the phan tom scatter primarily occurs in the forward direction and increases with the size of the field. However, for phantom generated scatter, the penetration characteristics of the beam are also altered. As the field size increases, the phantom scatter causes the beam to be significantly more penetrating with depth. This effect is significant enough that this energy difference must be included in the dose computations. The behavior of scatter from beam modifiers such as wedges must also be considered within the photon model. When the field size is small, a beam modifier mainly alters the transmission and does not contribute much scatter that arrives at the patient. However, when the field is large, beam modifiers begin to alter the pene tration characteristics of the beam, much as the phantom scatter does. This effect is exemplified by the increase in the wedge transmission factor with increasing field size and depth. 6,7 Electron Transport Photons are indirectly ionizing radiation. The dose is deposited by charged particles (electrons and positrons)

440 SECTION III Treatment Planning: Physics and Dosimetric Principles

dose at a point is proportional to the energy fluence of photons at the same point. The main criterion for CPE is that the energy fluence of photons must be constant out to the range of electrons set in motion in all directions. This does not occur in general in heterogeneous media, near the beam boundary, or for intensity-modulated beams. Electrons produced in the head of the accelerator and in air between the accelerator and the patient are called contamination electrons . The interaction of these electrons in and just beyond the buildup region contributes signifi cantly to the dose, especially if the field is large. Perturbation in electron transport can be exaggerated near heterogeneities. For example, the range of electrons is three to five times as long in lung as in water, and so beam boundaries passing through lung have much larger penumbral regions. Bone is the only tissue with an atomic composition significantly different from that of water. This can lead to perturbations in dose of only a few percent, 8 and so perturbations in electron scattering or stopping power are rarely taken into account. Bone can therefore be treated as “high-density water.” SUPERPOSITION/CONVOLUTION ALGORITHM The most common photon dose calculation in use for radiotherapy planning today is the superposition/convo lution algorithm. 8–19 This method incorporates a model based approach in describing the underlying physics of the interactions, while still being able to calculate dose in a reasonable time. The convolution/superposition method begins by modeling the indirect nature of dose deposition from photon beams. Primary photon interactions are dealt with separately from the transport of scattered photons and electrons set in motion. Dose Calculation under Conditions of CPE To begin, we consider the special case of dose determina tion under conditions of CPE. In this case, the total energy absorbed by charged particles at position r is the same as the total energy that escapes due to photon interactions at the same location. Thus, the primary dose D p and the first-scattered dose from a parallel beam of monoener getic photons can be computed as 9 D p ( r ) = ( K c( r )) P = ( μ en _ ρ ) P Ψ  P ( r ) = ( μ en __ ρ ) P ϕ P ( r = 0) h v p e − μr (20.1) where Ψ P ( r ) and ( K c ( r ) ) P are the primary energy fluence and collision kerma, respectively, at point r , ( µ en / ρ ) p is the mass energy absorption coefficient, ϕ P ( r = 0 ) is the pri mary photon fluence at the surface of the phantom, hν P is the primary photon energy, and μ is the attenuation

coefficient of primary photons. The total dose is the sum of the primary and scatter components D tot ( r ) = D P ( r ) + ∫ D P ( r ′ ) ( μ en ) scat _ ( μ en ) P   ( hv ) scat _ ( hv ) P   d P scat ( θ , r ′ ) _ d V e  − μ scat ( r ′ - r ) d V (20.2) where d P scat ( θ , r ′ ) / d V is the probability per unit volume of a primary photon being scattered into a solid angle cen tered about angle θ . These equations are complicated enough, but they do not take into account any secondary or higher-order pho ton scatter. They also neglect beam divergence and do not take into account tissue heterogeneities. They are valid only for CPE situations, so that the dose computation is not valid in the buildup region or near the field boundar ies, and the scatter dose is perturbed by heterogeneities lying between the scatter site at r ′ and the point r , where the total dose is being computed. Convolution/Superposition Method Unfortunately, Equation (20.1) is simplistic because it does not take into account the finite range of charged particles. In other words, the energy fluence that was present at the point the charged particles were set in motion upstream should replace the energy fluence in Equation (20.1). We may think of this energy fluence as that originating upstream (i.e., assuming that the charged particles all moved linearly downstream), but in reality, the particles may originate from any location around the calculation point, as long as it is within the particles’ range. Thus, rather than a single effective photon interac tion site, this expression for dose becomes a convolution integral about r : D ( r ) = ∫ K c ( r ′ ) A c ( r − r ′ ) d r ′ (20.3) where A c ( r − r ′ ) describes the contribution of charged particle energy that gets absorbed per unit volume at r ′ from interactions at r ′ and the integration is over all values of r ′ that make up volume d r ′ . The charged particle energy absorption kernel has a finite extent because the range of charged particles set in motion is finite. Equation (20.3) requires knowledge of the energy fluence due to both primary and scattered photons at all points. Time-consuming transport methods, such as the method of discrete ordinates or the MC method, would be needed to compute the scattered component accurately. A simpler solution is to utilize a scatter ker nel that includes the scattered photon component along with the contribution from charged particles. The ker nel is no longer finite because photon scatter (which has no range) is included. Now, only primary photons are explicitly transported. A convolution equation that separates primary photon transport and a kernel that accounts for the scattered photon and electrons set in

CHAPTER 20 Treatment Planning Algorithms: Photon Dose Calculations 441

motion away from the primary photon interaction site is as follows: D ( r ) = ∫ μ _ ρ Ψ  P ( r ′ ) A ( r − r ′ ) d r ′ = ∫ T P ( r ′ ) A ( r − r ′ ) d r ′ (20.4) where μ _ ρ is the mass attenuation coefficient, Ψ P ( r ′ ) is the primary energy fluence, and A ( r − r ′ ) includes the con tribution of scatter. The product of the mass attenuation coefficient and the primary energy fluence is the primary term a ( t otal e nergy r eleased per unit m ass) T P ( r ′ ) . Terma, first defined by Ahnesjo, Andreo, and Brahme, 20 is analo gous to kerma and has the same units as dose. The convolution kernels can, in principle, be obtained by analytic computation, deconvolution from dose distribu tions, or even by direct measurement. Most often, the kernels are computed with the MC method by interacting a large number of primary photons at one location and determining from where energy is absorbed, that is, from primary-gener ated charged particles, charged particles subsequently set in motion from scattered photons, or both. 12,13,20,21 Figure 20.2 illustrates isovalue lines for a 1.25-MeV kernel in water. As is evident from the figure, the kernel is forward directed even at this low energy. As the energy increases, the kernel becomes even more forward peaked. The convolution equation is restricted to describing monoenergetic parallel beams of primary photons inter acting with homogeneous phantoms. To model a clini cal radiotherapy beam, the contribution for each energy bin of the photon spectrum must be summed. At pres ent, the spectral information is derived from MC simula tions benchmarked by measurement. Using the EGS4 MC method, Mohan and Chui 22 first quantified the spectrum of clinical accelerators using the MC method. Since that time, several other authors have performed simulations to calculate photon energy spectrum. 18,23,24 The spectrum will also vary with off-axis position if a field-flattening filter is used. Figure 20.3 shows that the mean energy of primary radiation (directly from the tar get) for a Varian 2100C (flattened) 10-MV photon beam decreases off-axis but the extrafocal photons (primary collimator and flattening filter) do not. 18 This off-axis decrease is due to differential hardening of the beam by the field-flattening filter. Since the direct photon com ponent dominates, the model must take into account the change in the energy spectrum across the field. Collimators and block field outlines are usually mod eled with a mathematical mask function , which consists of the fraction of the incident fluence transmitted through the modifier. For a collimator, the mask function inside the field is unity, and underneath the collimator it is equal to the primary collimator transmission. For a block, the mask function inside the field is the primary transmission Modeling Primary Photons Incident on the Phantom

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FIGURE 20.2. Cobalt-60 (more precisely, 1.25-MeV primary photons) kernels for water computed using Monte Carlo simulation (MCS). The isovalue lines are in units of cGy MeV −1 photon −1 . A: The contribu tion due to electrons set in motion from primary photons (i.e., the primary contribution). B: The first scatter contribution. C: The sum of the primary and all scatter contributions. (Reprinted from Mackie TR, Bielajew AF, Rogers DW, et al. Generation of photon energy de position kernels using the EGS4 Monte Carlo code. Phys Med Biol . 1988;33:1–2;with permission of IOP Publishing. All rights reserved.) Copyright © 2021 Wolters Kluwer, Inc. Unauthorized reproduction of the content is prohibited.

through the block tray, and underneath the block it is equal to the primary block transmission. The mask func tion alone would not be able to model the penumbral blurring of the field boundary. This has been modeled by an aperture function . The mask function is convolved by a two-dimensional (2D) blurring kernel that represents the finite size of the source. The blurring kernel is usually assumed to be a normal function with a standard deviation equal to the projection of the source spot’s width through the collimation system (thereby accounting for magnifi cation of the source at large distances from the collimator system). Finally, the mask function is multiplied by the energy fluence distribution for the largest open field.

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