Construction of a nomogram model for clinically significant prostate cancer based on biparametric magnetic resonance...

To evaluate and compare the diagnostic performance of clinical features and PI-RADS v2.1 for identifying clinically significant prostate cancer (csPCa) and to assess whether a combined model improves detection.

This retrospective study analyzed patients who underwent biparametric MRI (bpMRI). csPCa was defined as ISUP grade ≥ 2 with pathological T-stage ≥ pT2a (prostatectomy) or PI-RADS score ≥ 4 (biopsy). PI-RADS v2.1 scores were assigned independently. PSAD was calculated as tPSA/prostate volume. Univariate and multivariable logistic regression identified predictors, with performance assessed by ROC analysis. Subgroup analyses were conducted for tPSA 4-10 ng/mL and 10-20 ng/mL.

Of 192 patients, 73 were classified as csPCa. Multivariable analysis confirmed PSAD (OR = 1.80, P = 0.008) and PI-RADS (OR = 4.50, p < 0.001) as independent predictors. The combined PSAD + PI-RADS model achieved an AUC of 0.830 (95% CI 0.801-0.869), with 83.6% sensitivity and 78.2% specificity. Subgroup analyses showed consistent high performance in both the 4-10 ng/mL (AUC = 0.805) and 10-20 ng/mL (AUC = 0.855) PSA ranges.

PSAD and PI-RADS v2.1 are independent predictors of csPCa under a composite definition. Their combination provides superior diagnostic accuracy, offering an effective strategy for risk stratification and biopsy decision-making in patients with tPSA 4-20 ng/mL.