Abstract

Objectives To develop and validate a Prostate Imaging‐Reporting and Data System (PI‐RADS) version 2.1 (v2.1)‐based predictive model for diagnosis of clinically significant prostate cancer (csPCa), integrating clinical and multiparametric magnetic resonance imaging (mpMRI) data, and compare its performance with existing models. Patients and Methods We retrospectively analysed data from patients who underwent prospective mpMRI assessment using the PI‐RADS v2.1 scoring system and biopsy at our institution between April 2019 and December 2023. A ‘Clinical Baseline’ model using patient demographics and laboratory results and an ‘MRI Added’ model additionally incorporating PI‐RADS v2.1 scores and prostate volumes were created and validated on internal and external patients. Both models were compared against two previously published MRI‐based algorithms for csPCa using area under the receiver operating characteristic curve (AUC) and decision curve analysis. Results A total of 1319 patients across internal and external cohorts were included. Our ‘MRI Added’ model demonstrated significantly improved discriminative ability (AUC internal 0.88, AUC external 0.79) compared to our ‘Clinical Baseline’ model (AUC internal 0.75, AUC external 0.68) ( P < 0.001). The ‘MRI Added’ model also showed higher net benefits across various clinical threshold probabilities and compared to a ‘biopsy all’ approach, it reduced unnecessary biopsies (defined as biopsies without Gleason Grade Group ≥2 csPCa) by 27% in the internal cohort and 10% in the external cohort at a risk threshold of 25%. However, there was no significant difference in predictive ability and reduction in unnecessary biopsies between our model and comparative ones developed for PI‐RADS v2 and v1. Conclusion Our PI‐RADS v2.1‐based mpMRI model significantly enhances csPCa prediction, outperforming the traditional clinical model in accuracy and reduction of unnecessary biopsies. It proves promising across diverse patient populations, establishing an updated, integrated approach for detection and management of prostate cancer.