Semi-Supervised, Attention-Based Deep Learning for Predicting TMPRSS2:ERG Fusion Status in Prostate Cancer Using Whole Slide Images.

Journal: Molecular cancer research : MCR
PMID: 38284821

Abstract

Our study illuminates the potential of deep learning in effectively inferring key prostate cancer genetic alterations from the tissue morphology depicted in routinely available histology slides, offering a cost-effective method that could revolutionize diagnostic strategies in oncology.

Authors

  • Mohamed Omar
    Trauma Department, Hannover Medical School, Hanover, Germany.
  • Zhuoran Xu
    1 Dalio Institute of Cardiovascular Imaging New York-Presbyterian Hospital New York NY.
  • Sophie B Rand
    Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York.
  • Mohammad K Alexanderani
    Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York.
  • Daniela C Salles
    Department of Pathology, Johns Hopkins University, Baltimore, Maryland.
  • Itzel Valencia
    Multiparametric In Situ Imaging Laboratory, Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY.
  • Edward M Schaeffer
    Department of Urology, Northwestern University Feinberg School of Medicine, Chicago.
  • Brian D Robinson
    Departments of Pathology and Laboratory Medicine and Urology, Weill Cornell Medicine, New York, New York.
  • Tamara L Lotan
    Department of Pathology, Johns Hopkins University School of Medicine; Department of Oncology, Johns Hopkins University School of Medicine; Department of Urology, Johns Hopkins University School of Medicine. Electronic address: tlotan1@jhmi.edu.
  • Massimo Loda
    Dana-Farber Cancer Institute, Boston, MA, USA.
  • Luigi Marchionni
    Weill Cornell Medicine, New York, NY, USA.

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