Development of a Deep Learning Algorithm for the Histopathologic Diagnosis and Gleason Grading of Prostate Cancer Biopsies: A Pilot Study.

Journal: European urology focus
Published Date: Nov 22, 2019

Abstract

BACKGROUND: The pathologic diagnosis and Gleason grading of prostate cancer are time-consuming, error-prone, and subject to interobserver variability. Machine learning offers opportunities to improve the diagnosis, risk stratification, and prognostication of prostate cancer.

Authors

  • Ohad Kott
    Minimally Invasive Urology Institute, The Miriam Hospital, Providence, RI, USA.
  • Drew Linsley
    Carney Institute for Brain Science, Department of Cognitive, Linguistic & Psychological Sciences, Brown University, Providence, RI, USA.
  • Ali Amin
    Department of Pathology and Laboratory Medicine, The Miriam Hospital, Providence, RI, USA; Warren Alpert Medical School of Brown University, Providence, RI, USA.
  • Andreas Karagounis
    Carney Institute for Brain Science, Department of Cognitive, Linguistic & Psychological Sciences, Brown University, Providence, RI, USA.
  • Carleen Jeffers
    Carney Institute for Brain Science, Department of Cognitive, Linguistic & Psychological Sciences, Brown University, Providence, RI, USA.
  • Dragan Golijanin
    Minimally Invasive Urology Institute, The Miriam Hospital, Providence, RI, USA; Warren Alpert Medical School of Brown University, Providence, RI, USA; Division of Urology, Rhode Island Hospital and The Miriam Hospital, Providence, RI, USA.
  • Thomas Serre
    Carney Institute for Brain Science, Brown University, USA.
  • Boris Gershman
    Division of Urology, Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.

Keywords

External Resources

Popular Topics
Recent Journals