Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study.

Journal: The Lancet. Oncology
Published Date: Jan 1, 2021

Abstract

BACKGROUND: Detecting microsatellite instability (MSI) in colorectal cancer is crucial for clinical decision making, as it identifies patients with differential treatment response and prognosis. Universal MSI testing is recommended, but many patients remain untested. A critical need exists for broadly accessible, cost-efficient tools to aid patient selection for testing. Here, we investigate the potential of a deep learning-based system for automated MSI prediction directly from haematoxylin and eosin (H&E)-stained whole-slide images (WSIs).

Authors

  • Rikiya Yamashita
    Artera, Inc., Los Altos, CA.
  • Jin Long
    Center for Artificial Intelligence in Medicine and Imaging, Stanford University, 1701 Page Mill Road, Palo Alto, CA, 94304, USA.
  • Teri Longacre
    Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
  • Lan Peng
    Department of Pathology, University of Texas, Southwestern Medical Center, Dallas, TX, USA.
  • Gerald Berry
    Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
  • Brock Martin
    Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
  • John Higgins
    Dartmouth College, Hanover, NH, USA.
  • Daniel L Rubin
    Department of Biomedical Data Science, Stanford University School of Medicine Medical School Office Building, Stanford CA 94305-5479.
  • Jeanne Shen
    Center for Artificial Intelligence in Medicine and Imaging, Stanford University, 1701 Page Mill Road, Palo Alto, CA, 94304, USA. jeannes@stanford.edu.

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