External evaluation of an open-source deep learning model for prostate cancer detection on bi-parametric MRI.

Journal: European radiology
Published Date: Aug 3, 2025

Abstract

OBJECTIVES: This study aims to evaluate the diagnostic accuracy of an open-source deep learning (DL) model for detecting clinically significant prostate cancer (csPCa) in biparametric MRI (bpMRI). It also aims to outline the necessary components of the model that facilitate effective sharing and external evaluation of PCa detection models.

Authors

  • Patricia M Johnson
    Imaging Research Laboratories, Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada.
  • Angela Tong
    Department of Radiology, NYU Langone Health, 660 1st Avenue, 3rd Floor, New York, NY, 10016, USA.
  • Luke Ginocchio
    Department of Radiology, NYU Grossman School of Medicine, New York, NY.
  • Juan Lloret Del Hoyo
    Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA.
  • Paul Smereka
    Department of Radiology, NYU Langone Health, 660 1st Avenue, 3rd Floor, New York, NY, 10016, USA.
  • Stephanie A Harmon
    Clinical Research Directorate, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, Maryland, USA.
  • Baris Turkbey
    Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
  • Hersh Chandarana
    Department of Radiology, NYU Langone Health, 660 1st Avenue, 3rd Floor, New York, NY, 10016, USA.

Keywords

No keywords available for this article.

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