A selective CutMix approach improves generalizability of deep learning-based grading and risk assessment of prostate cancer.

ABSTRACT: The Gleason score is an important predictor of prognosis in prostate cancer. However, its subjective nature can result in over- or under-grading. Our objective was to train an artificial intelligence (AI)-based algorithm to grade prostate cancer in specimens from patients who underwent radical prostatectomy (RP) and to assess the correlation of AI-estimated proportions of different Gleason patterns with biochemical recurrence-free survival (RFS), metastasis-free survival (MFS), and overall survival (OS). Training and validation of algorithms for cancer detection and grading were completed with three large datasets containing a total of 580 whole-mount prostate slides from 191 RP patients at two centers and 6218 annotated needle biopsy slides from the publicly available Prostate Cancer Grad

SUBMITTER: Patkar S 

PROVIDER: S-EPMC11215954 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature