Benchmarking Radiomics-Based Machine Learning Pipelines for Clinically Significant Prostate Cancer Detection

Accurate detection of clinically significant prostate cancer (csPCa) remains a major challenge in prostate MRI interpretation. Radiomics provides a noninvasive approach by extracting quantitative descriptors from imaging data, such as Apparent Diffusion Coefficient (ADC) maps. In this study, we systematically benchmarked radiomics-based machine learning pipelines across two validation scenarios: (1) repeated cross-validation on a multicenter dataset, and (2) nested cross-validation with external testing on the public PROSTATEx dataset. We evaluated eight feature selection methods, fifteen classifiers, and the impact of ComBat harmonization. The best-performing pipeline-Recursive Feature Elimination (RFE) with 20 features and a Random Forest classifier, without ComBat-achieved an internal AUCPR of 0.880 ± 0.061 and an external AUC-PR of 0.717, with an F1-score of 0.748. Although ComBat improved calibration (F1score) in some cases, its effect on external discrimination was limited. Notably, we found that selecting 20 features yielded optimal generalization performance, supporting the 'rule of 10 ' guideline for feature-to-sample ratio. These findings highlight the importance of rigorous feature selection and classifier design in developing robust and generalizable radiomics models for csPCa detection.