A Hybrid Human-Machine Learning Approach for Screening Prostate Biopsies Can Improve Clinical Efficiency Without Compromising Diagnostic Accuracy.

Journal Article (Journal Article;Review)

CONTEXT.—: Prostate cancer is a common malignancy, and accurate diagnosis typically requires histologic review of multiple prostate core biopsies per patient. As pathology volumes and complexity increase, new tools to improve the efficiency of everyday practice are keenly needed. Deep learning has shown promise in pathology diagnostics, but most studies silo the efforts of pathologists from the application of deep learning algorithms. Very few hybrid pathologist-deep learning approaches have been explored, and these typically require complete review of histologic slides by both the pathologist and the deep learning system. OBJECTIVE.—: To develop a novel and efficient hybrid human-machine learning approach to screen prostate biopsies. DESIGN.—: We developed an algorithm to determine the 20 regions of interest with the highest probability of malignancy for each prostate biopsy; presenting these regions to a pathologist for manual screening limited the initial review by a pathologist to approximately 2% of the tissue area of each sample. We evaluated this approach by using 100 biopsies (29 malignant, 60 benign, 11 other) that were reviewed by 4 pathologists (3 urologic pathologists, 1 general pathologist) using a custom-designed graphical user interface. RESULTS.—: Malignant biopsies were correctly identified as needing comprehensive review with high sensitivity (mean, 99.2% among all pathologists); conversely, most benign prostate biopsies (mean, 72.1%) were correctly identified as needing no further review. CONCLUSIONS.—: This novel hybrid system has the potential to efficiently triage out most benign prostate core biopsies, conserving time for the pathologist to dedicate to detailed evaluation of malignant biopsies.

Full Text

Duke Authors

Cited Authors

  • Dov, D; Assaad, S; Syedibrahim, A; Bell, J; Huang, J; Madden, J; Bentley, R; McCall, S; Henao, R; Carin, L; Foo, W-C

Published Date

  • June 1, 2022

Published In

Volume / Issue

  • 146 / 6

Start / End Page

  • 727 - 734

PubMed ID

  • 34591085

Electronic International Standard Serial Number (EISSN)

  • 1543-2165

Digital Object Identifier (DOI)

  • 10.5858/arpa.2020-0850-OA


  • eng

Conference Location

  • United States