The impact of integrating PRIMARY score or SUVmax with MRI-based risk models for the detection of clinically significant prostate cancer

An MRI-based risk calculator (RC) has been recommended for diagnosing clinically significant prostate cancer (csPCa). PSMA PET/CT can detect lesions that are not visible on MRI, and the addition of PSMA PET/CT to MRI may improve diagnostic performance. The aim of this study was to incorporate the PRIMARY score or SUVmax derived from [⁶⁸Ga]Ga-PSMA-11 PET/CT into the RC and compare these models with MRI-based RC to assess whether this can further reduce unnecessary biopsies.

A total of 683 consecutive biopsy-naïve men who underwent both [⁶⁸Ga]Ga-PSMA-11 PET/CT and MRI before biopsy were temporally divided into a development cohort (n = 552) and a temporal validation cohort (n = 131). Three logistic regression RCs were developed and compared: MRI-RC, MRI-SUVmax-RC and MRI-PRIMARY-RC. Discrimination, calibration, and clinical utility were evaluated. The primary outcome was the clinical utility of the risk calculators for detecting csPCa and reducing the number of negative biopsies.

The prevalence of csPCa was 47.5% (262/552) in the development cohort and 41.9% (55/131) in the temporal validation cohort. In the development cohort, the AUC of MRI-PRIMARY-RC was significantly higher than that of MRI-RC (0.924 vs. 0.868, p < 0.001) and MRI-SUVmax-RC (0.924 vs. 0.904, p = 0.002). In the temporal validation cohort, MRI-PRIMARY-RC also showed the best discriminative ability with an AUC of 0.921 (95% CI: 0.873–0.969). Bootstrapped calibration curves revealed that the model fit was acceptable. MRI-PRIMARY-RC exhibited near-perfect calibration within the range of 0–40%. DCA showed that MRI-PRIMARY-RC had the greatest net benefit for detecting csPCa compared with MRI-RC and MRI-SUVmax-RC at a risk threshold of 5–40% for csPCa in both the development and validation cohorts.

The addition of the PRIMARY score to MRI-based multivariable model improved the accuracy of risk stratification prior to biopsy. Our novel MRI-PRIMARY prediction model is a promising approach for reducing unnecessary biopsies and improving the early detection of csPCa.