Objective To demonstrate the proof of principle that machine learning (ML) can be used to quantify Gleason Pattern (GP) 4 on digitized biopsy slides using multiple measurement approaches, allowing direct comparison of their prognostic performance.

Methods We assembled a convenience sample of 726 patients with grade group 2-4 prostate cancer on systematic biopsy who underwent radical prostatectomy between 2014 and 2023. Digitized biopsy slides were analyzed using a machine-learning algorithm (PAIGE-AI) to quantify GP4 using multiple measurement approaches, particularly with respect to how gaps between cancer foci (“interfocal stroma”) were handled. GP4 extent was quantified using linear measurements or a pixel-based area metric. Discrimination of each GP4 quantification approach, along with Grade Group (GG), was assessed for adverse radical prostatectomy pathology and biochemical recurrence.

Results We identified 15 different quantification approaches and observed differences between their discrimination. The highest discrimination was in the pixel-countingmethod (AUC 0.648). GP4 quantification outperformed GG for predicting adverse pathology (AUC 0.627 vs 0.608). Amount of GP3 was non-predictive once GP4 was known. These findings were consistent for BCR.

Conclusions We were able to measure slides using 15 distinct measurement approaches and replicated prior findings using ML to quantify GP4. Our findings support the use of ML as a research tool to compare different GP4 quantification approaches. We intend to use our method on larger cohorts to determine with which measurement approach best predicts oncologic outcome.

Dr. Vickers is named on the patent for the statistical model that has been licensed and commercialized as the 4Kscore by OPKO Diagnostics. Drs. Vickers receives royalties from sales of this test and owns stock options in OPKO. Memorial Sloan Kettering Cancer Center has financial interests in PAIGE-AI.

This work was supported in part by the National Institutes of Health/National Cancer Institute (NIH/NCI) with a Cancer Center Support Grant to Memorial Sloan Kettering Cancer Center [P30 CA008748], a SPORE grant in Prostate Cancer to Dr. H. Scher [P50-CA92629], the Breakthrough Global Foundation and gifts from Michael N Emmerman, and Bob and Beth Mancini.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Review board approval was obtained (Memorial Sloan Kettering Cancer Center reference IRB 24-128)

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Funding: This work was supported in part by the National Institutes of Health/National Cancer Institute (NIH/NCI) with a Cancer Center Support Grant to Memorial Sloan Kettering Cancer Center [P30 CA008748], a SPORE grant in Prostate Cancer to Dr. H. Scher [P50-CA92629], the Breakthrough Global Foundation and gifts from Michael N Emmerman, and Bob and Beth Mancini.

Conflicts of interest: Dr. Vickers is named on the patent for the statistical model that has been licensed and commercialized as the 4Kscore by OPKO Diagnostics. Drs. Vickers receives royalties from sales of this test and owns stock options in OPKO. Memorial Sloan Kettering Cancer Center has financial interests in PAIGE-AI.

Ethical Approval statement: Institutional review board approval was obtained (MSKCC reference IRB 24-128)

All data produced in the present study are available upon reasonable request to the authors