Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5525_Библиотеки_им_академика_М_И_Перельмана.pdf
Preface
Artificial Intelligence (AI) is playing a transformative role in healthcare, particularly
in the field of radiation therapy (RT). Adaptive radiation therapy (ART), a complex
and dynamic treatment approach that incorporates feedback mechanisms to account
for patient-specific anatomic, biological, and functional changes during the course of
treatment, holds significant promise. The integration of AI into ART heralds a new
era of enhanced precision, efficiency, and personalization in cancer care.
This book, Artificial Intelligence in Adaptive Therapy, is a comprehensive and
timely exploration of how AI technologies are reshaping the landscape of ART from
data acquisition and image processing to decision-making and treatment delivery.
This book is designed to serve as a foundational and forward-looking resource for
medical physicists, radiation oncologists, researchers, and clinicians interested in
understanding and leveraging the power of AI in RT and ART.
The book begins with the fundamental concepts of AI (chapter 1) and gradually
builds toward advanced clinical applications (section II) in ART, and clinical
applications in ART (section III), providing a structured and in-depth view of the
entire AI–ART ecosystem.
Early chapters lay the groundwork by introducing AI in the context of radiation
therapy, including imaging technologies, big data utilization, image registration and
segmentation, dose prediction, and motion monitoring, etc. Subsequent chapters
dive into the technical pillars of each step of the ART workflow. This book also
addresses the growing role of AI in quality assurance, treatment adaptation,
response prediction, and clinical trials.
Recognizing the real-world challenges of implementation, later chapters
(section III) provide critical discussions on ethical, regulatory, safety, and training
considerations. The book concludes with a vision of recent advances and the future
of AI-augmented ART, highlighting promising research directions and emerging
innovations.
Our goal is to equip clinicians, researchers, and trainees with the knowledge to
critically engage with AI technologies and thoughtfully incorporate them into
clinical practice. As adaptive therapy continues to evolve, AI will be a key enabler
of innovation, efficiency, and improved patient outcomes. Whether you are new to
the field or an experienced practitioner seeking to stay current, this book aims to
inform, inspire, and guide your journey through the rapidly evolving world of AIpowered adaptive therapy. We hope this book will serve as both a guide and a
catalyst for further advancement in this exciting and fast-moving field.
xx
Foreword
In the ever-advancing field of radiation oncology, the integration of artificial
intelligence (AI) promises transformative change, particularly in the realm of
adaptive radiation therapy (ART). The recent advances in AI have come just in
time to bring ART to clinical reality and represents a significant leap forward in
personalizing treatment plans, optimizing radiation therapy delivery, and improving
patient outcomes. Artificial Intelligence in Adaptive Radiation Therapy serves as the
first comprehensive guide to understand and apply AI-driven techniques in radiation
oncology as they relate to ART, providing a deep dive into both the theoretical
underpinnings and practical implementations of the topic.
The chapters within this book cover a wide array of topics, from the basics of AI
and its introduction into radiation therapy to more advanced applications of AIdriven imaging, treatment planning, and real-time adaptation. Each chapter is
meticulously crafted by leading experts in the field, offering insights into how AI can
enhance every step of the ART workflow, from simulation to quality assurance. The
discussions are grounded in the latest research and clinical practice, making this
book an invaluable resource for clinicians, researchers, and trainees alike.
As you dive into the pages of Artificial Intelligence in Adaptive Radiation Therapy,
you will not only discover the profound impact that AI is having on the highly
technical field of radiation oncology, but also in the way we approach cancer
treatment in general. The innovations discussed here not only push the boundaries of
what is possible in radiation oncology, but also pave the way for future technological
advancements that will continue to enhance the precision and effectiveness of
radiation oncology and cancer care.
This book is much more than just a compilation of knowledge; it is a testament to
the collaborative efforts of scientists, clinicians, and engineers who are dedicated to
advancing the field of radiation therapy. It is hoped that the insights shared in these
pages will inspire further exploration and innovation, ultimately leading to better
outcomes for cancer patients worldwide.
Welcome to the future of radiation therapy—where AI plays a central role in
shaping the next generation of cancer treatment.
Michael Steinberg, MD
Professor and Chair
Department of Radiation Oncology
David Geffen School of Medicine at UCLA
xxi
Acknowledgments
The editors gratefully acknowledge the contributions of all chapter authors, whose
expertise and dedication have been essential to the creation of this book. Their
collective scholarship has brought together a comprehensive and timely perspective
on this rapidly evolving field.
We extend our sincere appreciation to the reviewers, whose thoughtful and
constructive feedback greatly enhanced the scientific rigor and clarity of the work.
We are also thankful to the IOP Publishing team for their consistent guidance and
support throughout the publication process.
Our deepest appreciation to our families for their patience, understanding and
unwavering support throughout the preparation of this book.
xxii
Editor biographies
Yi Wang
Dr Yi Wang is a medical physicist from the Department of
Radiation Oncology at Massachusetts General Hospital (MGH)
and an Assistant Professor of Radiation Oncology at Harvard
Medical School (HMS) in Boston, MA, USA. He earned his
BEng in Automation from the Beijing University of Aeronautics
and Astronautics (China) in 2002, and his MS and PhD in
Biomedical Engineering from the University of Michigan (Ann
Arbor, MI, USA) in 2004 and 2009, respectively. He completed
the three-year residency in therapeutic medical physics at the Harvard Medical
Physics Residency Program in 2012, including a year of postdoctoral fellowship at
the Francis H Burr Proton Therapy Center at MGH. Since then, he has been
working as a faculty physicist in the Department of Radiation Oncology at MGH.
Locally, he leads an AI Lab and serves as the Director of the Mass General Brigham
(MGB) Undergraduate Fellowship Program in Medical Physics. As an internationally recognized expert on AI for radiation therapy, he serves in multiple AI-related
committees, task groups, and working groups in the American Association of
Physicists in Medicine (AAPM), including the Machine Intelligence Subcommittee,
Ad Hoc Advisory Committee on Artificial Intelligence Boot Camps, the Vice Chair
of Task Group 384 (Clinical Implementation of Automated Segmentation for
Adaptive Radiation Therapy), and the Chair of the Working Group on
Generative Artificial Intelligence. He has authored and co-authored over 40 journal
articles, conference papers, and book chapters.
X. Sharon Qi
X. Sharon Qi, PhD, is a Professor of Medical Physics in the
Department of Radiation Oncology at the University of
California Los Angeles (UCLA). She is an affiliated faculty
member of UCLA’s CAMPEP-accredited Physics and Biology
in Medicine Interdisciplinary Graduate Program. Dr. Qi is boardcertified in Therapeutic Radiologic Physics by the American
Board of Radiology and is a Fellow of the American
Association of Physicists in Medicine (AAPM).
Dr. Qi earned her bachelor’s degree in physics and her PhD degree in
Experimental Particle Physics from the Institute of High Energy Physics, Chinese
Academy of Sciences in Beijing, China. She pursued her doctoral research as a
scientist at Fermi National Accelerator Laboratory (Fermilab) in Batavia, Illinois,
before completing a postdoctoral fellowship in Medical Physics at the Medical
College of Wisconsin. She served as a faculty member in the Department of
Radiation Oncology at the University of Colorado Denver prior to joining UCLA.
xxiii
Artificial Intelligence in Adaptive Radiation Therapy
Dr. Qi has served as both principal investigator and co-investigator on various
research projects and clinical trials. She has published more than 130 peer-reviewed
journal articles, over 220 peer reviewed abstracts, and 7 book chapters. Her research
focuses on image-guided radiation therapy and adaptive radiation therapy, outcome
modeling and response prediction, big data analytics, and the application of AI in
radiotherapy oncology.
As an internationally recognized expert in AI for radiation therapy, Dr. Qi
contributes actively to multiple AI-focused committees, working groups, and task
groups with the AAPM. Her roles include serving on the Machine Intelligence
Subcommittee (MIS) and the Therapy Physics Committee (TPC), Chairing Task
Group 384 on the clinical implementation of automated segmentation for adaptive
radiation therapy, and acting as Vice Chair of the Working Group on Generative
Artificial Intelligence. Beyond AAPM, she also contributes to broader national
initiatives through the American Society for Radiation Oncology (ASTRO) and
NRG Oncology.
Beyond her research and committee service, Dr. Qi actively contributes to
scientific publishing, serving as Deputy Editor and Associate Editor for the
Journal of Medical Physics, and holding editorial and peer review roles for other
journals in radiation therapy.
xxiv
List of contributors
Brian Anderson
University of North Carolina, 101 Manning Drive, Chapel Hill, NC 27514, USA
Parsa Bagherzadeh
McGill University, 3755 Ch de la Côte Ste-Catherine Montréal, Québec, H3T
1E2, Canada
James M Balter
University of Michigan, 1500 E Medical Center Drive, Ann Arbor, MI, USA
Kristy Brock
The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, FCT
14.6048, Unit 1902, Houston, TX 77030, USA
Jing Cai
The Hong Kong Polytechnic University, Y921, Block Y, The Hong Kong
Polytechnic University, No 11 Yuk Choi Road, Hung Hom, Kowloon, Hong
Kong
Carlos Eduardo Cardenas
University of Alabama at Birmingham, 1700 6th Avenue Street, Birmingham, AL
35233, USA
Maria Chan
Memorial Sloan Kettering Cancer Center, 136 Mountainview Blvd, Basking
Ridge, NJ 07920, USA
Xinru Chen
The University of Texas MD Anderson Cancer Center, 1400 Pressler Street,
Houston, TX 77030, USA
Laurence Court
The University of Texas MD Anderson Cancer Center, 1515 Holcombe
Boulevard, Houston, Texas 77030, USA
Sunan Cui
University of Washington, 1959 NE Pacific Street Box Number 356043, Seattle,
WA 98195, USA
Xianjin Dai
Stanford University, 875 Blake Wilbur Drive, Stanford, CA 94305, USA
Denis Dudas
Czech Technical University in Prague, Břehová 78/7, 115 19 Prague, Czech
Republic
xxv
Artificial Intelligence in Adaptive Radiation Therapy
Issam El Naqa
H Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive,
Tampa, FL 33612, USA
Shirin Abbasinejad Enger
McGill University, 3755 Ch de la Côte Ste-Catherine Montréal, Québec, H3T
1E2, Canada
Andrew Fanning
University of Nebraska Medical Center, 986861 Nebraska Medical Center,
Omaha, NE 68198-6861, USA
Jie Fu
University of Washington, 1959 NE Pacific Street Box Number 356043, Seattle,
WA 98195, USA
Yu Gao
Stanford University, 875 Blake Wilbur Drive, Palo Alto, CA, USA
Huaizhi Geng
University of Pennsylvania, 3400 Civic Center Boulevard, TRC-2 West,
Philadelphia, PA 19104, USA
Andrew Godley
University of Texas Southwestern Medical Center, 2280 Inwood Rd, Dallas,
TX 75235, USA
Bin Han
Stanford University, 875 Blake Wilbur Drive, Palo Alto, CA, USA
Joseph Harms
University of Alabama at Birmingham, 1700 6th Avenue Street, Birmingham, AL
35233, USA
Elizabeth Huynh
London Health Sciences Centre, 800 Commissioners Road East, London, ON,
N6A 5W9, Canada
Megan Hyun
Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY
10065, USA
Yi Lao
City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA
91010, USA
Michael Vincent Lauria
University of California, Los Angeles, 200 Medical Plaza Driveway Suite B265,
Los Angeles, CA 90095, USA
xxvi
Artificial Intelligence in Adaptive Radiation Therapy
Sang Ho Lee
University of Pennsylvania, 3400 Civic Center Boulevard, TRC-2 West,
Philadelphia, PA 19104, USA
Sang Kyu Lee
Memorial Sloan Kettering Cancer Center, 480 Red Hill Road, Middletown, NJ
07748, USA
Mu-Han Li
University of Texas Southwestern Medical Center, 2280 Inwood Rd, Dallas,
TX 75235, USA
Lianli Liu
Stanford University, 875 Blake Wilbur Drive, Palo Alto, CA, USA
Kelly Nealon
Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street,
Boston, MA 02114, USA
Jack Neylon
University of California, Los Angeles, 200 Medical Plaza Driveway, Suite B265,
Los Angeles, CA 90095, USA
Oscar Pastor-Serrano
Stanford University, 3145 Porter Drive, Wing A, Palo Alto CA 94304, USA
Joel Anthony Pogue
University of Alabama at Birmingham, 1700 6th Avenue Street, Birmingham, AL
35233, USA
Richard Allen Popple
University of Alabama at Birmingham, 1700 6th Avenue Street, Birmingham, AL
35233, USA
Jennifer Pursley
Mayo Clinic, Rochester, 200 1st Street SW, Rochester, MN 55905, USA
X. Sharon Qi
University of California, Los Angeles, 200 Medical Plaza Driveway, Suite B265,
Los Angeles, CA 90095, USA
Laya Rafiee Sevyeri
Medical Physics Unit, Department of Oncology, McGill University, Montreal,
Canada
Gregory Sharp
Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street,
Boston, MA 02114, USA
xxvii
Artificial Intelligence in Adaptive Radiation Therapy
Chenyang Shen
University of Texas Southwestern Medical Center, 2280 Inwood Rd, Dallas,
TX 75235, USA
Lauren Smith
Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY
10065, USA
Dennis Nichols Stanley
University of Alabama at Birmingham, 1700 6th Avenue Street, Birmingham, AL
35233, USA
Xinzhi Teng
The Hong Kong Polytechnic University, Y921, Block Y, The Hong Kong
Polytechnic University, No.11 Yuk Choi Road, Hung Hom, Kowloon, Hong
Kong
Ivan Vazquez
The University of Texas MD Anderson Cancer Center, 1840 Old Spanish Trl,
Houston, TX 77054, USA
Justin Visak
University of Texas Southwestern Medical Center, 2280 Inwood Rd, Dallas,
TX 75235, USA
Natalie Nicole Viscariello
University of Alabama at Birmingham, 1700 6th Avenue Street, Birmingham, AL
35233, USA
Tonghe Wang
Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York,
NY 10065, USA
Yi Wang
Massachusetts General Hospital, Harvard Medical School, 100 Blossom Street,
Boston, MA 02114, USA
Brian Winey
Massachusetts General Hospital, Harvard Medical School, 100 Blossom Street,
Cox 3, Boston, MA 02114, USA
Jinzhong Yang
The University of Texas MD Anderson Cancer Center, 1400 Pressler Street,
Houston, TX 77030, USA
Ming Yang
The University of Texas MD Anderson Cancer Center, 1840 Old Spanish Trl,
Houston, TX 77054, USA
Xiaofeng Yang
Emory University, 1365 Clifton Road NE, Building C, Atlanta, GA 30322, USA
xxviii
Artificial Intelligence in Adaptive Radiation Therapy
Cenji Yu
Mayo Clinic, Rochester, 200 First Street SW, Rochester, MN 55905, USA
Ying Xiao
University of Pennsylvania, 3400 Civic Center Boulevard, TRC-2 West,
Philadelphia, PA 19104, USA
Lei Xing
Stanford University, 875 Blake Wilbur Drive, Stanford, CA 94305, USA
Jiang Zhang
The Hong Kong Polytechnic University, Y921, Block Y, The Hong Kong
Polytechnic University, No 11 Yuk Choi Road, Hung Hom, Kowloon, Hong
Kong
Xinyu Zhang
The Hong Kong Polytechnic University, Y921, Block Y, The Hong Kong
Polytechnic University, No 11 Yuk Choi Road, Hung Hom, Kowloon, Hong
Kong
Yuanpeng Zhang
The Hong Kong Polytechnic University, Y921, Block Y, The Hong Kong
Polytechnic University, No 11 Yuk Choi Road, Hung Hom, Kowloon, Hong
Kong
Yao Zhao
The University of Texas MD Anderson Cancer Center, 1400 Pressler Street,
Houston, TX 77030, USA
Dandan Zheng
University of Rochester, 601 Elmwood Avenue, Rochester, NY 14642, USA
Yujing Zou
McGill University, 3755 Ch de la Côte Ste-Catherine Montréal, Québec, H3T
1E2, Canada
xxix