A Prostate Imaging-Reporting and Data System version 2.1-based predictive model for clinically significant prostate cancer diagnosis

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.

Keywords: biopsy; magnetic resonance imaging; prostatic neoplasms; risk assessment; statistical model.

Fig. 1

Flowchart demonstrating final combined internal and external patient population with exclusion of patients that had missing data.

Fig. 2

The ROC curves comparing our Clinical Baseline and MRI Added models to the Mehralivand MRI [5] and van Leeuwen Advanced (VL) [6] models in (A) the internal validation cohort and (B) the external validation cohort. Area under the ROC curve (AUC) is listed next to each model in the figure legend.

Fig. 3

Graph of net benefit results for (A) the internal validation cohort and (B) the external validation cohort and net reduction of interventions results for (C) the internal validation cohort and (D) the external validation cohort given the four comparison models (Clinical Baseline, MRI Added, Mehralivand MRI [5], and van Leeuwen [VL] Advanced [6]) and a biopsy‐all strategy (shown as ‘all’) and a biopsy‐none strategy (shown as ‘none’ at the line y = 0, not visible in A and B).