Academic Press Machine Learning and Artificial Intelligence in Radiation Oncology: A Guide for Clinicians

Product Description Machine Learning in Radiation Oncology: A Guide for Clinicians is designed for the application of practical concepts in machine learning to clinical radiation oncology. The book addresses the existing void in a resource to educate practicing clinicians on how machine learning can be used to improve clinical and patient-centered outcomes. Sections cover the fundamental concepts of machine learning and radiation oncology, detail techniques applied in genomics, discuss translational opportunities, such as in radiogenomics and autosegmentation, present current clinical applications in clinical decision-making, cover how to integrate AI into workflow and use cases, and elaborate on cross-collaborations within industry. The book is a valuable resource for oncologists, radiologists and members of the biomedical field who want to learn more about machine learning and its present and potential uses in radiation oncology. Review Provides important fundamental concepts, translational opportunities, and current applications of machine learning in radiation oncology From the Back Cover Machine Learning in Radiation Oncology: A Guide for Clinicians is designed for the application of practical concepts in machine learning to clinical radiation oncology. It addresses the existing void in a resource to educate practicing clinicians about how machine learning can be used to improve clinical and patient-centered outcomes. This book is divided into three sections: the first addresses fundamental concepts of machine learning and radiation oncology, detailing techniques applied in genomics; the second section discusses translational opportunities, such as in radiogenomics and autosegmentation; and the final section encompasses current clinical applications in clinical decision making, how to integrate AI into workflow, use cases, and cross-collaborations with industry. The book is a valuable resource for oncologists, radiologists and several members of biomedical field who need to learn more about machine learning as a support for radiation oncology. About the Author The focus of Dr. Rosenstein’s research program for the past 20 years has been the identification of genetic/genomic markers associated with the development of adverse effects resulting from radiotherapy. In this context, he was one of the first investigators to hypothesize that possession of single nucleotide polymorphisms in certain genes may render some cancer patients more susceptible to injuries resulting from radiotherapy. Ten years ago Dr. Rosenstein established and has co-led the Radiogenomics Consortium (RGC) with Dr. Catharine West, representing an international consortium with 225 investigators at 131 institutions in 31 countries. Through his research efforts, Dr. Rosenstein, has been in the forefront of research in the use of big data in radiation oncology and has collaborated with investigators possessing expertise in bioinformatics and statistics to employ machine learning-based modeling approaches in radiogenomic studies. Following core surgical training in the UK, Dr Rattay took up an academic clinical training post at the University Hospitals of Leicester developing his research interest in the field of radiobiology and radiogenomics of the normal tissues, specifically focusing on breast radiotherapy. During his subsequent NIHR Research Fellowship, he carried out work to improve the prediction of acute skin toxicity in the breast integrating patient, treatment and genomic variables. He also conducted a qualitative interview study to elicit patients’ views of predictive radiogenomics testing and the potential impact on their decision-making in breast cancer treatment. In 2018 Dr Rattay was appointed Clinical Lecturer (Assistant Professor) in Surgery at the University of Leicester. Dr Rattay is continuing research in the field of precision medicine on prediction of radiotherapy toxicity including patient-re