Abstract B029: Confident Filtering in the context of primary Gleason pattern classification

Abstract Objective: Advancements in deep neural network-based models (deep AI) can be used to provide clinical assessments of Gleason patterns in digital prostate pathology slides. We cover the variability in clinical assessments which provides challenges in training a deep neural model with small glandular regions with limited sample sizes. Method: A convolutional neural network (CNN) model with transfer learning was used to train on pathology images with smaller patches that provide representation of a section with multiple glands of similar glandular architecture (primary Gleason pattern) level data (300x300 pixel or 40-140 micron patches). We assembled a cohort of Hematoxylin Eosin (H&E) stained sections with patches (3311 GS3 and 2909 GS4 patches) extracted from whole slide images from a cohort of 58 patients. We used calibrated confidence on the clinical scoring of the pathological slides at the pre-training phase. We conducted seven experiments splitting the data randomly, but with the constraint that all patches in holdout sets were not part of training (at patient biopsy whole slide level). For each experiment the training data was further split into 5 training-validation folds, that results in 35 models for each ablation study. We compare our findings to a naïve baseline. The baseline used in this study comprises: a VGG16 network with all CNN layers (5 stage) but with a smaller 2-stage fully-connected output classification layer (as implemented for all models), trained with an AdamW optimizer (with default TensorFlow settings), original ground-truth labels (no label flipping or removal of training samples), and no confidence filtering. Additionally, the optimal case (averaged over 5 folds) is compared to unoptimized case without ensembling. In both the unoptimized and optimized cases, all models were trained for ∼600 epochs, and the models with lowest validation loss were saved. Results: We found optimized AI models were able to improve classification of the primary score at the patch level (Gleason 3 vs 4) using ensembles, training-label reassignment and ambiguous sample elimination, and most dramatically by filtering on the highest confidence samples. We achieved a mean accuracy of 0.74, F 1 of 0.72 and AUC of 0.79 estimated using holdout sets classifying small patches with Gleason 3 and 4 patterns. Average performance on all metrics were improved by roughly 20%, when accounting for all optimizations Citation Format: Ryan Fogarty, Dmitry Goldgof, Laerence Hall, Jasreman Dhillon, Vaibhav Chumbalkar, Yoganand Balagurunathan. Confident Filtering in the context of primary Gleason pattern classification [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr B029.

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