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.

chapter 1|11 pages

Big data in radiation oncology: Opportunities and challenges

ByJean-Emmanuel Bibault

chapter 2|10 pages

Data standardization and informatics in radiation oncology

ByCharles S. Mayo

chapter 3|18 pages

Storage and databases for big data

ByTomas Skripcak, Uwe Just, Ida Schönfeld, Esther G.C. Troost, Mechthild Krause

chapter 4|19 pages

Machine learning for radiation oncology

ByYi Luo, Issam El Naqa

chapter 5|18 pages

Cloud computing for big data

BySepideh Almasi, Guillem Pratx

chapter 6|17 pages

Big data statistical methods for radiation oncology

ByYu Jiang, Vojtech Huser, Shuangge Ma

chapter 7|13 pages

From model-driven to knowledge- and data-based treatment planning

ByMorteza Mardani, Yong Yang, Yinyi Ye, Stephen Boyd, Lei Xing

chapter 8|11 pages

Using big data to improve safety and quality in radiation oncology

ByEric Ford, Alan Kalet, Mark Phillips

chapter 9|22 pages

Tracking organ doses for patient safety in radiation therapy

ByWazir Muhammad, Ying Liang, Gregory R. Hart, Bradley J. Nartowt, David A. Roffman, Jun Deng

chapter 10|8 pages

Big data and comparative effectiveness research in radiation oncology

BySunil W. Dutta, Daniel M. Trifiletti, Timothy N. Showalter

chapter 11|28 pages

Cancer registry and big data exchange

ByZhenwei Shi, Leonard Wee, Andre Dekker

chapter 12|19 pages

Clinical and cultural challenges of big data in radiation oncology

ByBrandon Dyer, Shyam Rao, Yi Rong, Chris Sherman, Mildred Cho, Cort Buchholz, Stanley Benedict

chapter 13|17 pages


ByBarry S. Rosenstein, Gaurav Pandey, Corey W. Speers, Jung Hun Oh, Catharine M.L. West, Charles S. Mayo

chapter 14|22 pages

Radiomics and quantitative imaging

ByDennis Mackin, Laurence E. Court

chapter 15|23 pages

Radiotherapy outcomes modeling in the big data era

ByJoseph O. Deasy, Aditya P. Apte, Maria Thor, Jeho Jeong, Aditi Iyer, Jung Hun Oh, Andrew Jackson

chapter 16|18 pages

Multi-parameterized models for early cancer detection and prevention

ByGregory R. Hart, David A. Roffman, Ying Liang, Bradley J. Nartowt, Wazir Muhammad, Jun Deng