Development and Validation of a Deep Learning Algorithm for Gleason Grading of Prostate Cancer From Biopsy Specimens.

Abstract

Importance:

For prostate cancer, Gleason grading of the biopsy specimen plays a pivotal role in determining case management. However, Gleason grading is associated with substantial interobserver variability, resulting in a need for decision support tools to improve the reproducibility of Gleason grading in routine clinical practice.

Objective:

To evaluate the ability of a deep learning system (DLS) to grade diagnostic prostate biopsy specimens.

Design, Setting, and Participants:

The DLS was evaluated using 752 deidentified digitized images of formalin-fixed paraffin-embedded prostate needle core biopsy specimens obtained from 3 institutions in the United States, including 1 institution not used for DLS development. To obtain the Gleason grade group (GG), each specimen was first reviewed by 2 expert urologic subspecialists from a multi-institutional panel of 6 individuals (years of experience: mean, 25 years; range, 18-34 years). A third subspecialist reviewed discordant cases to arrive at a majority opinion. To reduce diagnostic uncertainty, all subspecialists had access to an immunohistochemical-stained section and 3 histologic sections for every biopsied specimen. Their review was conducted from December 2018 to June 2019.

Main Outcomes and Measures:

The frequency of the exact agreement of the DLS with the majority opinion of the subspecialists in categorizing each tumor-containing specimen as 1 of 5 categories: nontumor, GG1, GG2, GG3, or GG4-5. For comparison, the rate of agreement of 19 general pathologists' opinions with the subspecialists' majority opinions was also evaluated.

Results:

For grading tumor-containing biopsy specimens in the validation set (n = 498), the rate of agreement with subspecialists was significantly higher for the DLS (71.7%; 95% CI, 67.9%-75.3%) than for general pathologists (58.0%; 95% CI, 54.5%-61.4%) (P < .001). In subanalyses of biopsy specimens from an external validation set (n = 322), the Gleason grading performance of the DLS remained similar. For distinguishing nontumor from tumor-containing biopsy specimens (n = 752), the rate of agreement with subspecialists was 94.3% (95% CI, 92.4%-95.9%) for the DLS and similar at 94.7% (95% CI, 92.8%-96.3%) for general pathologists (P = .58).

Conclusions and Relevance:

In this study, the DLS showed higher proficiency than general pathologists at Gleason grading prostate needle core biopsy specimens and generalized to an independent institution. Future research is necessary to evaluate the potential utility of using the DLS as a decision support tool in clinical workflows and to improve the quality of prostate cancer grading for therapy decisions.

• Authors: Kunal Nagpal, Davis Foote, Fraser Tan, Yun Liu, Po-Hsuan Cameron Chen, David F Steiner, Naren Manoj, Niels Olson, Jenny L Smith, Arash Mohtashamian, Brandon Peterson, Mahul B Amin, Andrew J Evans, Joan W Sweet, Carol Cheung, Theodorus van der Kwast, Ankur R Sangoi, Ming Zhou, Robert Allan, Peter A Humphrey, Jason D Hipp, Krishna Gadepalli, Greg S Corrado, Lily H Peng, Martin C Stumpe, Craig H Mermel
• Journal: JAMA oncology (JAMA Oncol) Vol. 6 Issue 9 Pg. 1372-1380 (09 01 2020) ISSN: 2374-2445 [Electronic] United States
• PMID: 32701148 (Publication Type: Journal Article)

Topics

• Adolescent
• Adult
• Algorithms
• Artificial Intelligence
• Biopsy, Large-Core Needle (methods)
• Deep Learning
• Humans
• Image Interpretation, Computer-Assisted
• Male
• Neoplasm Grading (standards)
• Prostatic Neoplasms (diagnosis, epidemiology, pathology)
• Specimen Handling
• United States (epidemiology)
• Young Adult