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  • [Med Phys.] Machine learning and modeling: Data, validation, communication challenges.

    University of Michigan / Issam El Naqa*

  • 출처
    Med Phys.
  • 등재일
    2018 Aug 24.
  • 저널이슈번호
    doi: 10.1002/mp.12811. [Epub ahead of print]
  • 내용

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    Abstract
    With the era of big data, the utilization of machine learning algorithms in radiation oncology is rapidly growing with applications including: treatment response modeling, treatment planning, contouring, organ segmentation, image-guidance, motion tracking, quality assurance, and more. Despite this interest, practical clinical implementation of machine learning as part of the day-to-day clinical operations is still lagging. The aim of this white paper is to further promote progress in this new field of machine learning in radiation oncology by highlighting its untapped advantages and potentials for clinical advancement, while also presenting current challenges and open questions for future research. The targeted audience of this paper includes newcomers as well as practitioners in the field of medical physics/radiation oncology. The paper also provides general recommendations to avoid common pitfalls when applying these powerful data analytic tools to medical physics and radiation oncology problems and suggests some guidelines for transparent and informative reporting of machine learning results.

     


    Author information

    El Naqa I1, Ruan D2, Valdes G3, Dekker A4, McNutt T5, Ge Y6, Wu QJ7, Oh JH8, Thor M8, Smith W9, Rao A10,11, Fuller C10, Xiao Y12, Manion F1, Schipper M1, Mayo C1, Moran JM1, Ten Haken R1.
    1
    Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
    2
    Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA, USA.
    3
    Department of Radiation Oncology, University of California Los San Francisco, San Francisco, CA, USA.
    4
    GROW-School for Oncology and Developmental Biology, Department of Radiation Oncology (MAASTRO), Maastricht University Medical Center, Maastricht, The Netherlands.
    5
    Department of Radiation Oncology, John Hopkins University, Baltimore, MD, USA.
    6
    Department of Software and Information Systems, University of North Carolina, Charlotte, NC, USA.
    7
    Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA.
    8
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
    9
    Department of Radiation Oncology, University of Washington, Seattle, WA, USA.
    10
    Department of Radiation Oncology, MD Anderson, Houston, TX, USA.
    11
    Department of Bioinformatics and Computational Biology, MD Anderson, Houston, TX, USA.
    12
    Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA.

  • 키워드
    big data; machine learning; radiation oncology
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