Objective:Based on multi-parametric prostate magnetic resonance imaging (mpMRI) and related clinical indicators, a nomogram model for patients with PI-RADS ≥3 and PSA 4-20ng/ml was developed and validated, and the predictive value of the model in diagnosing clinically significant prostate cancer was evaluated.Methods:The clinical and pathological data of 865 patients who underwent ultrasound-guided transperineal prostate biopsy for the first time at the Department of Urology, Second Hospital of Tianjin Medical University from January 2020 to August 2023, with PI-RADS scores ≥3 and PSA levels between 4-20 ng/ml were retrospectively analyzed. These 865 patients were included in Cohort A, and from them, 437 patients with PHI were selected in Cohort B. In Cohort A, the median age was 68(64, 73); the median f/tPSA was 14.36 (10.63, 19.74); the median PSAD was 0.17(0.11, 0.25); 375 cases (43.35%) with PV≤50 ml and 490 cases (56.65%) with PV>50 ml; PSA fluctuation <-50% 84 cases (9.71%), -50%--20% in 206 cases (23.82%), and >-20% in 575 cases (66.47%); PI-RADS v2.1 3 scores 546 cases (63.12%), 4 in 230 cases (23.59%), and 5 in 89 cases (10.29%); localization of suspicious lesions on mpMRI in the peripheral zone in 619 cases (71.56%), transitional zone in 181 cases (20.92%), others in 42 cases (4.86%), and both peripheral and transitional zones in 23 cases (2.66%). In Cohort B, the median PSAD was 0.17 (0.12, 0.25); the median D-dimer was 310.00 (230.00, 411.48); the median PHI was 49.75 (35.90, 73.27); with 198 cases (45.31%) with PV≤50 ml and 239 cases (54.69%) with PV>50 ml; PSA fluctuation<-50% was in 40 cases (9.15%), -50%--20% in 107 cases (24.49%), and>-20% in 290 cases (66.39%); PI-RADS v2.1 scores 3 was in 289 cases (66.13%), 4 in 103 cases (23.57%), and 5 in 45 cases (10.30%).Patients in cohorts A and B were randomly assigned to the training set and validation set using R language with " 123" as the random number seed, at a ratio of 7∶3.There was no statistically significant difference between the clinical data of the training and validation sets for both groups ( P>0.05).Univariate and multivariate logistic regression analyses were used to identify independent risk factors for CsPCa, and a nomogram model was constructed using R. The diagnostic performance of the prediction model was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis(DCA).External validation of the model was conducted in the validation set. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and missed diagnosis rate analyses were performed on nomogram models A and B, as well as PSAD and PHI, under different thresholds. Results:Cohort A training set has 608 cases, and the validation set has 257 cases.The results of multivariate backward regression analysis in the training set show that age( OR=1.06, P=0.001), f/tPSA( OR=0.96, P=0.008), prostate volume (PV)>50ml( OR=0.36, P<0.01), prostate-specific antigen density(PSAD)( OR=145.19, P<0.01), PSA fluctuation(-50%--20%: OR=1.97, P=0.234; >-20%: OR=6.81, P<0.01), PI-RADS v2.1 score(4: OR=10.65, P<0.01; 5: OR=21.20, P<0.01), and localization of suspicious lesions on mpMRI(TZ: OR=0.57, P=0.074; Others: OR=0.26, P=0.022) were all risk factors for CsPCa. Nomogram A was developed based on these risk factors and had an area under the ROC curve (AUC) of 0.905 (95% CI 0.881-0.928) for the training set and 0.893 (95% CI 0.854-0.931) for the validation set. Cohort B training set developed based on age( OR=1.05, P=0.053), PV>50ml( OR=0.18, P<0.01), PSAD( OR=54.14, P=0.021), PSA fluctuation(-50%--20%: OR=4.78, P=0.100; >-20%: OR=20.37, P=0.001), PHI( OR=1.02, P=0.002), D-Dimer( OR=1.00, P=0.031), and PI-RADS scores(4: OR=11.35, P<0.01; 5: OR=57.61, P<0.01) as risk factors for CsPCa. Nomogram B had an AUC of 0.933(95% CI 0.906-0.959) for the training set and 0.908 (95% CI 0.859-0.958) for the validation set.The two nomogram models mentioned above both have excellent discrimination, and the calibration curves also indicated that the calibration of the two models were good.Moreover, both nomogram A and nomogram B demonstrate good clinical net benefits in the DCA curves of the training and validation sets, especially when applying nomogram B to predict CsPCa, with an accuracy rate of up to 85.82%. Conclusions:The two nomogram models developed in study, based on mpMRI and related clinical indicators, both have excellent predictive value for the diagnosis of clinically significant prostate cancer prior to prostate biopsy in patients with PI-RADS≥3 and PSA 4-20ng/ml.