Large-scale deep learning for metastasis detection in pathology reports.

Journal: JAMIA open
Published Date: Jul 11, 2025

Abstract

OBJECTIVES: No existing algorithm can reliably identify metastasis from pathology reports across multiple cancer types and the entire US population. In this study, we develop a deep learning model that automatically detects patients with metastatic cancer by using pathology reports from many laboratories and of multiple cancer types.

Authors

  • Patrycja Krawczuk
    Advanced Computing for Health Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830, United States.
  • Zachary R Fox
    Advanced Computing for Health, Oak Ridge National Laboratory, Oak Ridge, TN 37830, United States.
  • Valentina Petkov
    National Cancer Institute, National Institutes of Health, Washington, DC 20830, United States.
  • Serban Negoita
    National Cancer Institute, National Institutes of Health, Washington, DC 20830, United States.
  • Jennifer Doherty
    Utah Cancer Registry, University of Utah School of Medicine, Salt Lake City, UT 84132, United States of America. Electronic address: Jen.Doherty@hci.utah.edu.
  • Antoinette Stroup
    New Jersey State Cancer Registry, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, 08901, United States of America. Electronic address: nan.stroup@rutgers.edu.
  • Stephen Schwartz
    Fred Hutchinson Cancer Research Center, Epidemiology Program, Seattle, WA 98109, USA.
  • Lynne Penberthy
    Surveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland, USA.
  • Elizabeth Hsu
    Surveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD, USA.
  • John Gounley
    Advanced Computing for Health Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830, United States.
  • Heidi A Hanson
    Advanced Computing for Health Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830, United States.

Keywords

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