Artificial Intelligence in Radiation Oncology and Biomedical Physics
Imaging in Medical Diagnosis and Therapy Series

This pioneering book explores how machine learning and other AI techniques impact millions of cancer patients who benefit from ionizing radiation. It features contributions from global researchers and clinicians, focusing on the clinical applications of machine learning for medical physics.

AI and machine learning have attracted much recent attention and are being increasingly adopted in medicine, with many clinical components and commercial software including aspects of machine learning integration. General principles and important techniques in machine learning are introduced, followed by discussion of clinical applications, particularly in radiomics, outcome prediction, registration and segmentation, treatment planning, quality assurance, image processing, and clinical decision-making. Finally, a futuristic look at the role of AI in radiation oncology is provided.

This book brings medical physicists and radiation oncologists up to date with the most novel applications of machine learning to medical physics. Practitioners will appreciate the insightful discussions and detailed descriptions in each chapter. Its emphasis on clinical applications reaches a wide audience within the medical physics profession.

1. AI Applications in Radiation Therapy and Medical Physics 2. Machine Learning for Image-Based Radiotherapy Outcome Prediction 3. Metric Predictions for Machine and Patient-Specific Quality Assurance 4. Data-Driven Treatment Planning, Plan QA and Fast Dose Calculation 5. Reinforcement Learning for Radiation Therapy Planning and Image Processing 6. Image Registration and Segmentation 7. Motion Management and Image-Guided Radiation Therapy 8. Outlook of AI in Medical Physics and Radiation Oncology

Dr. Gilmer Valdes received his PhD in medical physics from the University of California, Los Angeles, in 2013. He was a postdoctoral fellow with the University of California, San Francisco between 2013–2014 and a medical physics resident from 2014 to 2016 with the University of Pennsylvania. He is currently an associate professor with dual appointments in the Department of Radiation Oncology and the Department of Epidemiology and Biostatistics at the University of California, San Francisco. His main research focus is in the development of algorithms to satisfy special needs that machine learning applications have in medicine.

Dr. Lei Xing is the Jacob Haimson & Sarah S. Donaldson Professor and Director of Medical Physics Division of Radiation Oncology Department at Stanford University School of Medicine. He also holds affiliate faculty positions in the Department of Electrical Engineering, Biomedical Informatics, Bio-X and Molecular Imaging Program at Stanford (MIPS). Dr. Xing obtained his PhD in Physics from the Johns Hopkins University and received his medical physics training at the University of Chicago. His research has been focused on artificial intelligence in medicine, medical imaging, treatment planning and dose optimization, medical imaging, imaging instrumentations, image guided interventions, nanomedicine, and applications of molecular imaging in radiation oncology. He has made unique and significant contributions to each of the above areas. Dr. Xing is an author on more than 400 peer reviewed publications, an inventor/co-inventor on many issued and pending patents, and a co- investigator or principal investigator on numerous NIH, DOD, NSF, RSNA, AAPM, Komen, ACS and corporate grants. He is a fellow of AAPM (American Association of Physicists in Medicine) and AIMBE (American Institute for Medical and Biological Engineering).