Machine learning and modeling: Data, validation, communication challenges.

Journal: Medical physics
Published Date: Aug 24, 2018

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.

Authors

  • Issam El Naqa
    Department of Machine Learning, Moffitt Cancer Center, Tampa, Florida.
  • Dan Ruan
    Departments of Radiation Oncology, Biomedical Physics and Bioengineering, UCLA, Los Angeles, CA, 90095, USA.
  • Gilmer Valdes
    Department of Radiation Oncology, University of California, San Francisco, California.
  • Andre Dekker
    Department of Radiation Oncology (MAASTRO Clinic), Dr. Tanslaan 12, Maastricht, The Netherlands.
  • Todd McNutt
    Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland.
  • Yaorong Ge
    Department of Software and Information Systems, University of North Carolina at Charlotte, Charlotte, NC, United States.
  • Q Jackie Wu
    Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States.
  • Jung Hun Oh
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Maria Thor
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Wade Smith
    Department of Radiation Oncology, University of Washington, Seattle, WA, USA.
  • Arvind Rao
    Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
  • Clifton Fuller
    Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
  • Ying Xiao
    Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA.
  • Frank Manion
    Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
  • Matthew Schipper
    Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
  • Charles Mayo
    Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
  • Jean M Moran
    Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
  • Randall Ten Haken
    Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.

Keywords

External Resources

Popular Topics
Recent Journals