Prostate cancer is a leading cause of cancer-related deaths, with Gleason grading being key for assessing tumor aggressiveness. We propose a Graph Neural Network-based approach to automate Gleason grading using the Automated Gleason Grading Challenge 2022 dataset. Patch-level graphs constructed from Hematoxylin and Eosin-stained Whole-Slide Images were classified into Gleason grades. Our results show that Graph Neural Networks, specifically Graph Attention Networks and Graph Convolutional Networks, effectively distinguish between grades despite class imbalance. Focal Loss improves the classification of the minority Gleason Grade 5, which is crucial for detecting aggressive prostate cancer. Our models outperform state-of-the-art methods, achieving higher F1-scores without scanner generalization techniques.
