TUM Library

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Cover; Half Title; Title Page; Copyright Page; Dedication; Table of Contents; Series preface; Preface; Acknowledgments; Editors; Contributors; 1: Big data in radiation oncology: Opportunities and challenges; 2: Data standardization and informatics in radiation oncology; 3: Storage and databases for big data; 4: Machine learning for radiation oncology; 5: Cloud computing for big data; 6: Big data statistical methods for radiation oncology; 7: From model-driven to knowledge- and data-based treatment planning; 8: Using big data to improve safety and quality in radiation oncology

9: Tracking organ doses for patient safety in radiation therapy10: Big data and comparative effectiveness research in radiation oncology; 11: Cancer registry and big data exchange; 12: Clinical and cultural challenges of big data in radiation oncology; 13: Radiogenomics; 14: Radiomics and quantitative imaging; 15: Radiotherapy outcomes modeling in the big data era; 16: Multi-parameterized models for early cancer detection and prevention; Index

Big Data in Radiation Oncology gives readers an in-depth look into how big data is having an impact on the clinical care of cancer patients. While basic principles and key analytical and processing techniques are introduced in the early chapters, the rest of the book turns to clinical applications, in particular for cancer registries, informatics, radiomics, radiogenomics, patient safety and quality of care, patient-reported outcomes, comparative effectiveness, treatment planning, and clinical decision-making. More features of the book are: Offers the first focused treatment of the role of big data in the clinic and its impact on radiation therapy. Covers applications in cancer registry, radiomics, patient safety, quality of care, treatment planning, decision making, and other key areas. Discusses the fundamental principles and techniques for processing and analysis of big data. Address the use of big data in cancer prevention, detection, prognosis, and management. Provides practical guidance on implementation for clinicians and other stakeholders. Dr. Jun Deng is a professor at the Department of Therapeutic Radiology of Yale University School of Medicine and an ABR board certified medical physicist at Yale-New Haven Hospital. He has received numerous honors and awards such as Fellow of Institute of Physics in 2004, AAPM Medical Physics Travel Grant in 2008, ASTRO IGRT Symposium Travel Grant in 2009, AAPM-IPEM Medical Physics Travel Grant in 2011, and Fellow of AAPM in 2013. Lei Xing, Ph.D., is the Jacob Haimson Professor of Medical Physics and Director of Medical Physics Division of Radiation Oncology Department at Stanford University. His research has been focused on inverse treatment planning, tomographic image reconstruction, CT, optical and PET imaging instrumentations, image guided interventions, nanomedicine, and applications of molecular imaging in radiation oncology. Dr. Xing is on the editorial boards of a number of journals in radiation physics and medical imaging, and is recipient of numerous awards, including the American Cancer Society Research Scholar Award, The Whitaker Foundation Grant Award, and a Max Planck Institute Fellowship.

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