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September 17, 2025

Explainable Radiomics-Based ML for Predicting Clinically Significant Prostate Cancer in Biparametric MRI

MMMahmoud MorafeghShahid Beheshti University of Medical Sciences GVGelareh ValizadehIslamic Azad University, Science and Research Branch FMFarzan MoodiIran University of Medical Sciences

Key Points

An AUC of 0.85 demonstrates the effectiveness of machine learning for classifying clinically significant prostate cancer.
SHAP analysis enhances model transparency by clarifying the contributions of radiomic features in diagnosis.
This approach utilizes biparametric MRI-derived features, providing a framework for explainable AI in prostate cancer diagnosis.
The study involved MRI examinations from 344 patients, contributing to the development of transparent AI tools for clinical decision-making.

Abstract

Motivation: Prostate MRI interpretation is complex and subjective, facing challenges in diagnosis Prostate Cancer (PCa). Interpretable AI models are needed to aid diagnostic process. Goal(s): To develop explainable Machine Learning (ML) models using MRI-derived radiomic features to classify clinically significant prostate cancer (csPCa). Approach: A retrospective study using MRI exams of 344 patients was conducted. Radiomic features were extracted from T2-weighted and Apparent diffusion coefficient maps. ML classifiers were trained and evaluated, with SHapley Additive exPlanations (SHAP) analysis used for interpretability. Results: Random Forest model achieved an AUC of 0.85, with SHAP analysis clarifying radiomic feature contributions and enhancing model transparency. Impact: The development of explainable ML models using biparametric MRI radiomic features enhances csPCa classification, proposing a framework that connects prediction and interpretability. This approach can lead further research into transparent AI tools, benefiting clinical decision-making.

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