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Updated: May 5, 2026

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

Matei M Buzoianu1, Rebecca Yu1, Melissa Assel1

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

Medrxiv : the Preprint Server for Health Sciences
|May 4, 2026
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This summary is machine-generated.

Machine learning (ML) effectively quantifies Gleason Pattern 4 (GP4) in prostate cancer biopsies. Pixel-based area metrics showed superior prediction of adverse pathology compared to traditional Grade Group (GG).

Area of Science:

  • Digital pathology
  • Machine learning in oncology
  • Prostate cancer research

Background:

  • Accurate quantification of Gleason Pattern 4 (GP4) is crucial for prostate cancer prognostication.
  • Machine learning (ML) offers potential for objective and reproducible assessment of GP4 on digitized slides.
  • Comparing different ML-based quantification methods is essential for clinical translation.

Purpose of the Study:

  • To demonstrate the feasibility of using ML to quantify GP4 on digitized prostate biopsy slides.
  • To compare the prognostic performance of various ML-based GP4 quantification approaches.
  • To evaluate the predictive value of GP4 quantification for adverse pathology and biochemical recurrence.

Main Methods:

  • A cohort of 726 patients with prostate cancer (Grade Group 2-4) was analyzed.
Keywords:
Gleason pattern 4Gleason scoreGrade Groupbiopsymachine learningprostate cancer

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  • Digitized biopsy slides were processed using the PAIGE-AI ML algorithm.
  • GP4 extent was quantified using 15 distinct approaches, including linear and pixel-based metrics, assessing interfocal stroma handling.

    • Main Results:

    • Fifteen different GP4 quantification approaches were evaluated, showing varied discriminatory performance.
    • A pixel-counting method achieved the highest discrimination (AUC 0.648) for predicting adverse pathology.
    • GP4 quantification outperformed Grade Group (GG) in predicting adverse pathology (AUC 0.627 vs 0.608) and biochemical recurrence.

    Conclusions:

    • ML can reliably quantify GP4 on digitized prostate biopsy slides using multiple measurement strategies.
    • Pixel-based GP4 quantification demonstrates superior prognostic value compared to traditional GG.
    • Further validation in larger cohorts is warranted to establish the optimal ML-based measurement approach for predicting oncologic outcomes.