Mitigating Algorithmic Bias in Prostate Cancer Risk Stratification with Responsible Artificial Intelligence and Machine Learning

Prostate cancer (PCa) is the second most prevalent cancer among men worldwide, the majority affecting those over the age of 65. The Gleason Score (GS) remains the gold standard for diagnosing clinically significant prostate cancer (csPCa); however, traditional biopsy can lead to patient discomfort. Algorithmic bias in medical diagnostic models remains a critical challenge, impacting model reliability and generalizability across diverse patient populations. This study explores the potential of Machine Learning (ML) models—Logistic Regression (LR) and multiple DL models—as non-invasive alternatives for predicting the GS using Prostate Imaging Cancer AI challenge dataset . To the best of our knowledge, this is the first attempt to use two modalities with this dataset for risk stratification. We developed a LR model, excluding biopsy-derived features like GS, to predict clinically significant prostate cancer, alongside an image triage approach with convolutional neural networks to reduce biases in the ML workflow. Preliminary results from LR and ResNet50, showed test accuracies of 69.79% and 60%, respectively. These findings demonstrate the potential for explainable, trustworthy, and responsible risk stratification enhancing the robustness and generalizability of the prostate cancer risk stratification model.