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IPEM–IOP Series in Physics and Engineering in Medicine and Biology
Articial Intelligence
in Adaptive Radiation
Therapy
Edited by
Yi Wang
X. Sharon Qi
Artificial Intelligence in Adaptive
Radiation Therapy
Online at: https://doi.org/10.1088/978-0-7503-6119-4
IPEM–IOP Series in Physics and Engineering in Medicine and Biology
Editorial Advisory Board Members
Frank Verhaegen
Maastro Clinic, The Netherlands
Kwan Hoong Ng
University of Malaya, Malaysia
Carmel Caruana
University of Malta, Malta
Penelope Allisy-Roberts
formerly of BIPM, Sèvres, France
Rory Cooper
University of Pittsburgh, PA, USA
Alicia El Haj
University of Birmingham, UK
John Hossack
University of Virginia, USA
Tingting Zhu
University of Oxford, UK
Dennis Schaart
TU Delft, The Netherlands
Indra J Das
Northwestern University Feinberg School
of Medicine, USA
About the Series
The series in Physics and Engineering in Medicine and Biology will allow the Institute
of Physics and Engineering in Medicine (IPEM) to enhance its mission to ‘advance
physics and engineering applied to medicine and biology for the public good’.
It is focused on key areas including, but not limited to:
• clinical engineering
• diagnostic radiology
• informatics and computing
• magnetic resonance imaging
• nuclear medicine
• physiological measurement
• radiation protection
• radiotherapy
• rehabilitation engineering
• ultrasound and non-ionising radiation.
A number of IPEM–IOP titles are being published as part of the EUTEMPE
Network Series for Medical Physics Experts.
A full list of titles published in this series can be found here: https://iopscience.iop.
org/bookListInfo/physics-engineering-medicine-biology-series.
Artificial Intelligence in Adaptive
Radiation Therapy
Edited by
Yi Wang
Department of Radiation Oncology, Massachusetts General Hospital,
Harvard Medical School, 100 Blossom Street, Boston, MA 02114, USA
X. Sharon Qi
Department of Radiation Oncology, University of California, Los Angeles,
200 Medical Plaza Driveway, Los Angeles, CA 90095, USA
IOP Publishing, Bristol, UK
ª [2025] Institute of Physics and Engineering in Medicine. All rights, including for text and data
mining, AI training, and similar technologies, are reserved.
This book is available under the terms of the IOP-Standard Books License
No part of this publication may be reproduced, stored in a retrieval system, subjected to any
form of TDM or used for the training of any AI systems or similar technologies, or transmitted
in any form or by any means, electronic, mechanical, photocopying, recording or otherwise,
without the prior permission of the publisher, or as expressly permitted by law or under terms
agreed with the appropriate rights organization. Certain types of copying may be permitted
in accordance with the terms of licences issued by the Copyright Licensing Agency, the Copyright
Clearance Centre and other reproduction rights organizations.
Permission to make use of IOP Publishing content other than as set out above may be sought
at permissions@ioppublishing.org.
Yi Wang and X. Sharon Qi have asserted their right to be identified as the authors of this work in
accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988.
ISBN 978-0-7503-6119-4 (ebook)
ISBN 978-0-7503-6117-0 (print)
ISBN 978-0-7503-6120-0 (myPrint)
ISBN 978-0-7503-6118-7 (mobi)
DOI 10.1088/978-0-7503-6119-4
Version: 20251101
IOP ebooks
British Library Cataloguing-in-Publication Data: A catalogue record for this book is available
from the British Library.
Published by IOP Publishing, wholly owned by The Institute of Physics, London
IOP Publishing, No.2 The Distillery, Glassfields, Avon Street, Bristol, BS2 0GR, UK
US Office: IOP Publishing, Inc., 190 North Independence Mall West, Suite 601, Philadelphia,
PA 19106, USA
To my beloved wife, Ying, whose love and support give meaning to
every endeavor, and to my inspiring son, Lucas, whose dream of one day
landing on Mars reminds me always to reach higher.
—Yi Wang
To my husband, Yan,
to my daughter, Isabella,
and to my parents, for their love, patience and inspiration.
—X. Sharon Qi
Contents
Preface xx
Foreword xxi
Acknowledgments xxii
Editor biographies xxiii
List of contributors xxv
1 Fundamentals of artificial intelligence 1-1
Parsa Bagherzadeh, Laya Rafiee Sevyeri, Yujing Zou
and Shirin Abbasinejad Enger
1.1 A brief introduction to AI 1-1
1.2 Machine learning basics 1-3
1.2.1 Learning paradigms 1-3
1.2.2 Regression versus classification 1-4
1.2.3 Feature engineering and representation 1-4
1.2.4 Linear separability 1-8
1.2.5 Classical models 1-8
1.3 Artificial neural networks 1-12
1.3.1 Feed-forward neural networks 1-12
1.3.2 Recurrent neural networks 1-13
1.3.3 Convolutional neural networks 1-14
1.3.4 Attention 1-15
1.3.5 Training neural networks 1-18
1.3.6 Applications and use cases of deep learning 1-19
1.4 Model training and evaluation 1-20
1.4.1 Hyperparameters 1-20
1.4.2 Data split 1-20
1.4.3 Evaluation metrics 1-21
1.4.4 Overfitting versus under-fitting 1-23
1.5 Generative models 1-23
1.5.1 Generative adversarial networks 1-24
1.5.2 Diffusion models 1-25
1.5.3 Applications and use cases 1-26
1.6 Ethical consideration and bias 1-27
1.6.1 Transparency and explainability 1-27
1.6.2 Bias and fairness 1-28
1.6.3 Data privacy violation 1-28
vii
Artificial Intelligence in Adaptive Radiation Therapy
1.6.4 Risk and misuse 1-29
1.7 Summary 1-29
References 1-30
2 Introduction to artificial intelligence in radiation therapy 2-1
Elizabeth Huynh
2.1 Introduction 2-1
2.1.1 Radiation therapy workflow 2-1
2.1.2 Staff roles in radiation therapy 2-4
2.2 Overview of AI in radiation therapy 2-7
2.2.1 Patient evaluation and dose prescription 2-7
2.2.2 Treatment simulation 2-8
2.2.3 Contouring 2-8
2.2.4 Treatment planning 2-10
2.2.5 Quality assurance 2-12
2.2.6 Treatment delivery 2-13
2.2.7 Response assessment and toxicity management 2-13
2.3 Summary 2-14
References 2-14
3 Artificial intelligence in clinical decision making 3-1
Xinyu Zhang, Jiang Zhang, Xinzhi Teng, Yuanpeng Zhang and Jing Cai
3.1 Introduction 3-1
3.1.1 Introduction of clinical decision making and AI 3-1
3.1.2 The role of AI in clinical decision making 3-2
3.2 AI algorithms for clinical decision making 3-3
3.2.1 Workflow of AI development in clinical decision making 3-3
3.2.2 Radiomics 3-5
3.2.3 Data integration by AI 3-6
3.2.4 Interpretability of AI models 3-7
3.3 Application of AI in clinical decision making 3-8
3.3.1 Diagnosis and disease phenotyping 3-8
3.3.2 Personalized treatment 3-9
3.3.3 Treatment outcome and prognosis prediction 3-10
3.4 Challenges and future directions of AI in clinical decision making 3-11
3.4.1 Challenges and concerns 3-11
3.4.2 Future directions 3-12
viii
Artificial Intelligence in Adaptive Radiation Therapy
3.5 Summary 3-13
References 3-14
4 Imaging technologies in radiation therapy 4-1
Xinru Chen, Cenji Yu, Gregory Sharp and Jinzhong Yang
4.1 Introduction 4-1
4.2 Imaging for treatment planning 4-2
4.2.1 CT simulation 4-2
4.2.2 4D-CT 4-3
4.2.3 PET/CT 4-4
4.2.4 MRI 4-5
4.3 Imaging for treatment guidance 4-7
4.3.1 Portal imaging 4-7
4.3.2 CBCT 4-7
4.3.3 CT-on-rail and CT-linac 4-9
4.3.4 MR-linac 4-9
4.3.5 PET-linac 4-11
4.4 Imaging for motion management 4-11
4.4.1 ExacTrac 4-11
4.4.2 Varian triggered imaging 4-11
4.4.3 4D-CBCT 4-12
4.4.4 Cine MRI 4-12
4.4.5 4D-MRI 4-12
4.4.6 Surface imaging 4-13
4.5 Imaging for treatment assessment 4-13
4.5.1 Contrasted CT 4-14
4.5.2 PET/CT 4-14
4.5.3 Functional MRI 4-14
4.6 Summary 4-15
References 4-15
5 Big data for artificial intelligence in radiation oncology 5-1
Jie Fu, Sunan Cui and X. Sharon Qi
5.1 Introduction to big data in radiation oncology 5-1
5.1.1 Overview of big data 5-1
5.1.2 Sources of big data in radiation oncology 5-3
5.1.3 Big data and AI in radiation oncology 5-4
ix