Explaining MRI radiomics-based detection of prostate cancer using clinical concepts

Motivation: Clinical use of computer-aided diagnosis systems for prostate cancer is currently hindered by their internal complexity. Explainability tools can give insight into the functioning of these machine learning (ML) models. Goal(s): Our goal was to supplement the predictions of an MRI radiomics-based ML model for prostate cancer detection with explanations based on clinical concepts currently used in radiological assessment. Approach: We clustered correlating MRI radiomics features into groups representing clinical concepts underlying the PI-RADS system. We used SHAP analysis to explain the importance of these concepts in each predicted lesion. Results: Explainability based on clinical concepts gives insight into ML model predictions. Impact: Our machine learning pipeline combines accurate prostate cancer detection on MRI with intrinsic explainability, potentially resulting in an easier integration into clinical use.