Salvage HDR Brachytherapy: Multiple Hypothesis Testing Versus Machine Learning Analysis.

Journal: International journal of radiation oncology, biology, physics
Published Date: Jul 1, 2018

Abstract

PURPOSE: Salvage high-dose-rate brachytherapy (sHDRB) is a treatment option for recurrences after prior radiation therapy. However, only approximately 50% of patients benefit, with the majority of second recurrences after salvage brachytherapy occurring distantly. Therefore, identification of characteristics that can help select patients who may benefit most from sHDRB is critical. Machine learning may be used to identify characteristics that predict outcome following sHDRB. We aimed to use machine learning to identify patient characteristics associated with biochemical failure (BF) following prostate sHDRB.

Authors

  • Gilmer Valdes
    Department of Radiation Oncology, University of California, San Francisco, California.
  • Albert J Chang
    Department of Radiation Oncology, University of California at San Francisco, San Francisco, California.
  • Yannet Interian
    Data Analytic Program, University of San Francisco, San Francisco, California.
  • Kenton Owen
    Radiation Oncology Department, University of California San Francisco, San Francisco, California.
  • Shane T Jensen
    Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania.
  • Lyle H Ungar
    Department of Computer & Information Science, University of Pennsylvania.
  • Adam Cunha
    Radiation Oncology Department, University of California San Francisco, San Francisco, California.
  • Timothy D Solberg
    U.S. Food and Drug Administration, Silver Spring, Maryland.
  • I-Chow Hsu
    Department of Radiation Oncology, University of California at San Francisco, San Francisco, California.

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