Prostate Cancer Risk Stratification by Digital Histopathology and Deep Learning.

Journal: JCO clinical cancer informatics
PMID: 38900978

Abstract

PURPOSE: Prostate cancer (PCa) represents a highly heterogeneous disease that requires tools to assess oncologic risk and guide patient management and treatment planning. Current models are based on various clinical and pathologic parameters including Gleason grading, which suffers from a high interobserver variability. In this study, we determine whether objective machine learning (ML)-driven histopathology image analysis would aid us in better risk stratification of PCa.

Authors

  • Yanan Shao
    Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.
  • Roozbeh Bazargani
    Electrical and Computer Engineering, University of British Columbia, 2332 Main Mall, Vancouver, BC V6T 1Z4, Canada. Electronic address: roozbehb@ece.ubc.ca.
  • Davood Karimi
    Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada.
  • Jane Wang
    Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, CA 95616, USA.
  • Ladan Fazli
    Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada.
  • S Larry Goldenberg
    University of British Columbia, Vancouver, BC, Canada.
  • Martin E Gleave
    The Vancouver Prostate Centre, Vancouver, BC, Canada.
  • Peter C Black
    Department of Urologic Sciences, University of British Columbia, Vancouver, BC.
  • Ali Bashashati
    Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada.
  • Septimiu Salcudean
    Electrical and Computer Engineering, University of British Columbia, 2329 West Mall, Vancouver, BC, V6T 1Z4, Canada.

Keywords

External Resources

Popular Topics
Recent Journals