Comparing Gleason Pattern 4 Measurement Approaches on Prostate Biopsy Using Machine Learning: A Proof-of-Principle Study.

Matei M Buzoianu¹, Rebecca Yu¹, Melissa Assel¹, Alican Bozkurt², Hamed Aghdam², Samson W Fine³, Andrew J Vickers¹

¹Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center.

²PAIGE-AI.

³Department of Pathology & Laboratory Medicine, Memorial Sloan Kettering Cancer Center.

PubMed PMC DOI PDF

Abstract

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 prostatecto…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free Account Sign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.