Using Big Data Analytics to Advance Precision Radiation Oncology.

Journal: International journal of radiation oncology, biology, physics
Published Date: Mar 2, 2018

Abstract

Big clinical data analytics as a primary component of precision medicine is discussed, identifying where these emerging tools fit in the spectrum of genomics and radiomics research. A learning health system (LHS) is conceptualized that uses clinically acquired data with machine learning to advance the initiatives of precision medicine. The LHS is comprehensive and can be used for clinical decision support, discovery, and hypothesis derivation. These developing uses can positively impact the ultimate management and therapeutic course for patients. The conceptual model for each use of clinical data, however, is different, and an overview of the implications is discussed. With advancements in technologies and culture to improve the efficiency, accuracy, and breadth of measurements of the patient condition, the concept of an LHS may be realized in precision radiation therapy.

Authors

  • Todd R McNutt
    Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland. Electronic address: tmcnutt1@jhmi.edu.
  • Stanley H Benedict
    Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, CA, United States.
  • Daniel A Low
    Department of Radiation Oncology, UCLA, 200 Medical Plaza, Suite B265, Los Angeles, CA, 90095, USA.
  • Kevin Moore
    Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, California.
  • Ilya Shpitser
    Department of Computer Science, Johns Hopkins University, Baltimore, Maryland.
  • Wei Jiang
    Department of Civil Engineering, Johns Hopkins System Institute, Johns Hopkins University, Baltimore, Maryland.
  • Pranav Lakshminarayanan
    Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland.
  • Zhi Cheng
    Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland.
  • Peijin Han
    Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland.
  • Xuan Hui
    Department of Public Health Sciences, University of Chicago, Chicago, Illinois; and.
  • Minoru Nakatsugawa
    Canon Medical Systems Corporation.
  • Junghoon Lee
    Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland.
  • Joseph A Moore
    Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland.
  • Scott P Robertson
    Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland.
  • Veeraj Shah
    Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland.
  • Russ Taylor
    Department of Computer Science, Johns Hopkins University, Baltimore, Maryland.
  • Harry Quon
    Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland.
  • John Wong
  • Theodore DeWeese
    Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland.

Keywords

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