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  • Big Data in Radiation Oncology

    Big Data in Radiation Oncology by Deng, Jun; Xing, Lei;

    Series: Imaging in Medical Diagnosis and Therapy;

      • GET 20% OFF

      • The discount is only available for 'Alert of Favourite Topics' newsletter recipients.
      • Publisher's listprice GBP 56.99
      • The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.

        27 226 Ft (25 930 Ft + 5% VAT)
      • Discount 20% (cc. 5 445 Ft off)
      • Discounted price 21 781 Ft (20 744 Ft + 5% VAT)

    27 226 Ft

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    Availability

    Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
    Not in stock at Prospero.

    Why don't you give exact delivery time?

    Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.

    Short description:

    This book gives readers an in-depth look into how big data is having an impact on the clinical care of cancer patients. Basic principles are covered early on, and clinical applications become the focus thereafter. A final section introduces emerging models for cancer prevention and detection.

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    Long description:

    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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    Table of Contents:

    Series Preface



    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 Schönfeld, 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

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