Artificial Intelligence in Radiation Oncology and Biomedical Physics

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.

這本開創性的書籍探討了機器學習和其他人工智慧技術對受益於電離輻射的數百萬癌症患者的影響。它收錄了來自全球研究人員和臨床醫生的貢獻，專注於機器學習在醫學物理學中的臨床應用。

人工智慧和機器學習近年來引起了很大的關注，在醫學領域中得到了越來越廣泛的應用，許多臨床組件和商業軟體都包含了機器學習整合的方面。本書介紹了機器學習的一般原則和重要技術，然後討論了臨床應用，特別是在放射組織學、結果預測、註冊和分割、治療計劃、質量保證、影像處理和臨床決策方面。最後，本書還展望了人工智慧在放射腫瘤學中的未來角色。

這本書使醫學物理學家和放射腫瘤學家了解到機器學習在醫學物理學中的最新應用。從業人員將欣賞到每一章中深入的討論和詳細的描述。它強調臨床應用，能夠觸及醫學物理學專業的廣泛受眾。

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).

Dr. Gilmer Valdes於2013年從加州大學洛杉磯分校獲得醫學物理學博士學位。他在2013年至2014年期間在加州大學舊金山分校擔任博士後研究員，並在2014年至2016年期間在賓夕法尼亞大學擔任醫學物理學住院醫師。他目前是加州大學舊金山分校放射腫瘤學系和流行病學與生物統計學系的副教授，兼任職。他的主要研究重點是開發算法，以滿足機器學習在醫學領域的特殊需求。

Dr. Lei Xing是斯坦福大學醫學院放射腫瘤學系醫學物理學部門的Jacob Haimson＆Sarah S. Donaldson教授和主任。他還在斯坦福大學的電機工程、生物醫學信息學、Bio-X和分子影像計劃（MIPS）擔任聯合教職。Xing博士在約翰霍普金斯大學獲得物理學博士學位，並在芝加哥大學接受醫學物理學培訓。他的研究主要集中在醫學中的人工智能、醫學影像、治療計劃和劑量優化、醫學影像、影像儀器、影像引導介入、納米醫學以及分子影像在放射腫瘤學中的應用。他在上述每個領域都做出了獨特而重要的貢獻。Xing博士是400多篇同行評審的出版物的作者，許多專利的發明人/共同發明人，以及許多NIH，DOD，NSF，RSNA，AAPM，Komen，ACS和企業資助的共同調查員或首席調查員。他是AAPM（美國醫學物理學家協會）和AIMBE（美國醫學和生物工程學會）的會士。