Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study.

Journal: The Lancet. Oncology
PMID: 31926805

Abstract

BACKGROUND: The Gleason score is the strongest correlating predictor of recurrence for prostate cancer, but has substantial inter-observer variability, limiting its usefulness for individual patients. Specialised urological pathologists have greater concordance; however, such expertise is not widely available. Prostate cancer diagnostics could thus benefit from robust, reproducible Gleason grading. We aimed to investigate the potential of deep learning to perform automated Gleason grading of prostate biopsies.

Authors

  • Wouter Bulten
    Diagnostic Image Analysis Group and the Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands.
  • Hans Pinckaers
    Artera, Inc., Los Altos, CA.
  • Hester van Boven
    Department of Pathology, Antoni van Leeuwenhoek Hospital, The Netherlands Cancer Institute, Amsterdam, Netherlands.
  • Robert Vink
    Laboratory of Pathology East Netherlands, Hengelo, Netherlands.
  • Thomas de Bel
    Departments of Pathology and.
  • Bram van Ginneken
    Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands; Fraunhofer Mevis, Bremen, Germany.
  • Jeroen van der Laak
    Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Christina Hulsbergen-van de Kaa
    Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Geert Litjens
    Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands.

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