
Big Data in Radiation Oncology
Description
Alles über E-Books | Antworten auf Fragen rund um E-Books, Kopierschutz und Dateiformate finden Sie in unserem Info- & Hilfebereich.
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.
More details
Other editions
Additional editions


Persons
Lei Xing, PhD, is currently the Jacob Haimson Professor of Medical Physics and Director of the Medical Physics Division of the Radiation Oncology Department of Stanford University. Dr. Xing obtained his PhD in Physics from the Johns Hopkins University in 1992 and received his Medical Physics training at the University of Chicago. He has been a member of the Radiation Oncology faculty at Stanford since 1997. He is also an affiliated faculty member in Stanford's Department of Electrical Engineering, the Biomedical Informatics Program, the Molecular Imaging Program at Stanford, and the Bio-X program. His research has 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 an author on more than 250 peer-reviewed publications, a co-inventor on many issued and pending patents, and a co-investigator or principal investigator on numerous NIH, DOD, NSF, and ACS grants and projects from other funding agencies and corporations. He and his lab members have received numerous awards from ACS, AAPM, ASTRO, WMIC, and RSNA in the past decade. Dr. Xing serves on the editorial boards of a number of journals in radiation physics and medical imaging and is a recipient of numerous awards, including the American Cancer Society Research Scholar Award, The Whitaker Foundation Grant Award, and a Max Planck Institute Fellowship.
Content
Preface
Acknowledgments
Editors
Contributors
1. Big data in radiation oncology: Opportunities and challenges
Jean-Emmanuel Bibault
2. Data standardization and informatics in radiation oncology
Charles S. Mayo
3. Storage and databases for big data
Tomas Skripcak, Uwe Just, Ida Schoenfeld, Esther G.C. Troost, and Mechthild Krause
4. Machine learning for radiation oncology
Yi Luo and Issam El Naqa
5. Cloud computing for big data
Sepideh Almasi and Guillem Pratx
6. Big data statistical methods for radiation oncology
Yu Jiang, Vojtech Huser, and Shuangge Ma
7. From model-driven to knowledge- and data-based treatment planning
Morteza Mardani, Yong Yang, Yinyi Ye, Stephen Boyd, and Lei Xing
8. Using big data to improve safety and quality in radiation oncology
Eric Ford, Alan Kalet, and Mark Phillips
9. Tracking organ doses for patient safety in radiation therapy
Wazir Muhammad, Ying Liang, Gregory R. Hart, Bradley J. Nartowt, David A. Roffman, and Jun Deng
10. Big data and comparative effectiveness research in radiation oncology
Sunil W. Dutta, Daniel M. Trifiletti, and Timothy N. Showalter
11. Cancer registry and big data exchange
Zhenwei Shi, Leonard Wee, and Andre Dekker
12. Clinical and cultural challenges of big data in radiation oncology
Brandon Dyer, Shyam Rao, Yi Rong, Chris Sherman, Mildred Cho, Cort Buchholz, and Stanley Benedict
13. Radiogenomics
Barry S. Rosenstein, Gaurav Pandey, Corey W. Speers, Jung Hun Oh, Catharine M.L. West, and Charles S. Mayo
14. Radiomics and quantitative imaging
Dennis Mackin and Laurence E. Court
15. Radiotherapy outcomes modeling in the big data era
Joseph O. Deasy, Aditya P. Apte, Maria Thor, Jeho Jeong, Aditi Iyer, Jung Hun Oh, and Andrew Jackson
16. Multi-parameterized models for early cancer detection and prevention
Gregory R. Hart, David A. Roffman, Ying Liang, Bradley J. Nartowt, Wazir Muhammad, and Jun Deng
Index
System requirements
File format: PDF
Copy-Protection: Adobe-DRM (Digital Rights Management)
System requirements:
- Computer (Windows; MacOS X; Linux): Install the free reader Adobe Digital Editions prior to download (see eBook Help).
- Tablet/smartphone (Android; iOS): Install the free app Adobe Digital Editions or the app PocketBook before downloading (see eBook Help).
- E-reader: Bookeen, Kobo, Pocketbook, Sony, Tolino and many more (only limited: Kindle).
The file format PDF always displays a book page identically on any hardware. This makes PDF suitable for complex layouts such as those used in textbooks and reference books (images, tables, columns, footnotes). Unfortunately, on the small screens of e-readers or smartphones, PDFs are rather annoying, requiring too much scrolling.
This eBook uses Adobe-DRM, a „hard” copy protection. If the necessary requirements are not met, unfortunately you will not be able to open the eBook. You will therefore need to prepare your reading hardware before downloading.
Please note: We strongly recommend that you authorise using your personal Adobe ID after installation of any reading software.
For more information, see our eBook Help page.