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This book provides a comprehensive overview of the use of artificial intelligence (AI) in radiation therapy (RT), with a particular focus on adaptive radiation therapy (ART). It covers key topics, including AI-driven image processing, automatic segmentation, adaptive replanning and delivery, AI-enhanced quality assurance, clinical decision support, and related safety, educational, regulatory and ethical considerations. As an emerging and rapidly evolving field, AI in ART holds immense potential to enhance precision, efficiency, and treatment outcomes in cancer care. Yet, comprehensive resources remain limited. This book bridges the gap by offering a detailed and practical guide, presenting the latest research and clinical applications to help practitioners and researchers effectively integrate AI into their practice and research.
Key features:
- Comprehensive overview of artificial intelligence (AI) in radiation therapy (RT) and adaptive radiation therapy (ART)
- In-depth discussion of current developments in AI technologies facilitating ART
- Practical guidance for clinical practitioners and researchers
- AI in clinical trials
- Future perspectives
Dr. Yi Wang is a therapeutic medical physicist from Massachusetts General Hospital (MGH) and an Assistant Professor of Radiation Oncology at Harvard Medical School (HMS) in Boston. He leads the Laboratory of Machine Intelligence in Clinical Physics at MGH. As an internationally recognized expert on clinical artificial intelligence for radiation therapy, he serves on multiple AI-related committees and groups in the American Association of Physicists in Medicine (AAPM), including the Machine Intelligence Subcommittee (MIS), the Ad Hoc Advisory Committee on Artificial Intelligence Boot Camps (AHAIBC), as well as serving as the Chair of the Working Group on Generative Artificial Intelligence (WGGenAI) and Vice Chair of the Task Group 384 – clinical implementation of automated segmentation for adaptive radiation therapy (ART).
Dr. X. Sharon Qi is a Professor of Medical Physics in the Department of Radiation Oncology and a faculty member of the interdisciplinary Physics and Biology in Medicine (PBM) graduate program at the University of California Los Angeles (UCLA). She is board-certified in therapeutic radiologic physics by the American Board of Radiology and is a Fellow of the American Association of Physicists (FAAPM). Dr. Qi’s research focuses on the anatomical/biological/functional image guided therapy and adaptive therapy, predictive analytics and modelling, as well as the development and clinical application of AI in RT and ART. As a recognized expert in AI for radiation therapy, Dr. Qi serves on multiple committees, subcommittees and task groups within the AAPM, including the Therapy Physics Committee (TPC), Machine Intelligence Subcommittee (MIS), and as chair of the Task group 384 on clinical implementation of automated segmentation for adaptive radiation therapy (ART). Beyond the AAPM, she contributes to national initiatives through leadership and service roles within the American Society for Radiation Oncology (ASTRO) and NRG oncology.
Preface
Foreword
Acknowledgments
Editor biographies
List of contributors
1 Fundamentals of artificial intelligence
2 Introduction to AI in radiation therapy
3 AI in clinical decision making
4 Imaging technologies in radiation therapy
5 Big data for AI in radiation oncology
6 Introduction to adaptive radiotherapy
7 Overview of artificial-intelligence driven adaptive therapy workflow
8 Imaging, imaging processing, and synthetic CT
9 AI-based image registration and segmentation
10 AI-assisted dose prediction and re-planning
11 AI-based intrafraction motion monitoring for precise ART delivery
12 AI for quality assurance in ART
13 AI empowered response prediction and adaptation
14 Challenges of AI implementation in adaptive radiation therapy
15 Offline CT-based and online CBCT-based ART
16 AI in MRI-guided adaptive radiation therapy
17 Functional imaging-guided ART
18 Artificial intelligence in proton adaptive radiation therapy
19 AI in clinical trials
20 Safety and training considerations in the clinical implementation of AI ART
21 Ethical and regulatory considerations in AI for ART
22 Recent advances and future of AI-augmented ART