Current preoperative assessment faces limitations, including PI-RADS scoring subjectivity and diagnostic uncertainty in distinguishing high-risk prostate cancer from benign and low-risk lesions. To develop an interpretable ensemble learning framework integrating habitat-based radiomics and peritumoral analysis from multiparametric MRI for preoperative high-risk prostate cancer prediction. This retrospective, multi-institutional study included 896 patients with suspected prostate lesions and histopathologically confirmed diagnoses across three centers (January 2018-December 2024). Intratumoral habitat analysis used K-means clustering; peritumoral analysis evaluated 1 mm, 3 mm, and 5 mm expansion rings. Feature selection used minimum Redundancy Maximum Relevance (mRMR) and LASSO regression. Models were validated externally with SHAP analysis for interpretability. The cohort comprised 398 training, 171 internal validation, and 327 external validation patients. The habitat signature achieved superior performance with AUCs of 0.827 (95% CI: 0.768–0.886) and 0.855 (95% CI: 0.795–0.915) in external validation cohorts, significantly outperforming intratumoral signatures (AUCs: 0.774 and 0.629, p 70% of selected features. Integrated habitat and peritumoral radiomics provide robust preoperative risk stratification for prostate cancer, with superior performance from ADC-derived habitat features. Not applicable. This was a retrospective observational study without prospective trial registration.
Yuan et al. (Sat,) studied this question.