Predicting radiation pneumonitis in locally advanced stage II-III non-small cell lung cancer using machine learning.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
PMID: 30935565

Abstract

BACKGROUND AND PURPOSE: Radiation pneumonitis (RP) is a radiotherapy dose-limiting toxicity for locally advanced non-small cell lung cancer (LA-NSCLC). Prior studies have proposed relevant dosimetric constraints to limit this toxicity. Using machine learning algorithms, we performed analyses of contributing factors in the development of RP to uncover previously unidentified criteria and elucidate the relative importance of individual factors.

Authors

  • José Marcio Luna
    Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States.
  • Hann-Hsiang Chao
    Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA.
  • Eric S Diffenderfer
    Department of Radiation Oncology, University of Pennsylvania, Philadelphia, United States.
  • Gilmer Valdes
    Department of Radiation Oncology, University of California, San Francisco, California.
  • Chidambaram Chinniah
    Department of Radiation Oncology, Hospital of the University of Pennsylvania, Philadelphia, PA, 19104, USA.
  • Grace Ma
    Department of Radiation Oncology, University of Pennsylvania, Philadelphia, United States.
  • Keith A Cengel
    Department of Radiation Oncology, University of Pennsylvania, Philadelphia, United States.
  • Timothy D Solberg
    U.S. Food and Drug Administration, Silver Spring, Maryland.
  • Abigail T Berman
    Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA.
  • Charles B Simone
    Department of Radiation Oncology, University of Maryland Medical Center.

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