Development and validation of a nomogram prediction model for clinically significant prostate cancer combined with PI-RADS V2.1, MRI quantitative parameters and clinical indicators: a two-center study.

Objective: To develop and validate a multi-index nomogram prediction model for clinically significant prostate cancer(CSPCa) by combining the PI-RADS V2.1, quantitative magnetic resonance imaging (MRI) parameters and clinical indicators.

Methods: A total of 1740 patients (75% in the derivation cohort and 25% in the internal validation cohort) and 342 patients (the external validation cohort) were retrospectively included in the MRI follow-up database of the First Affiliated Hospital of Kunming Medical University between January 2015 and April 2021,and Gejiu People's Hospital between January 2020 and December 2022.Important predictors of CSPCa in MRI-related quantitative parameters, PSA-derived indicators, and clinical indicators, such as age, were screened. The Net Reclassification Improvement Index(NRI),Integrated Discrimination Improvement Index(IDI), and clinical decision curve analysis (DCA) were calculated to compare the performances of the different models. Receiver operating characteristic(ROC) curves and clinical calibration curves were used to analyze and compare diagnostic effects.

Results: The AUC value, best cut-off value, specificity, sensitivity and accuracy of model 1(PI-RADS + PSAD) derivation cohort were 0.935, 0.304, 0.861, 0.895 and 0.872, respectively. The AUC values of the internal and external validation cohorts for model 1 were 0.956 and 0.955, respectively. The AUC value, best cut-off value, specificity, sensitivity and accuracy of model 2(PI-RADS +PSAD + ADCmean) derivation cohort were 0.939, 0.401, 0.895, 0.853 and 0.882, respectively. The AUC values of the internal and external validation cohorts for model 2 were 0.940 and 0.960,respectively. After adding the ADCmean to the model, the NRI(categorical), NRI(continuous) and IDI values were 0.0154, 0.3498 and 0.0222, respectively. There was no significant difference between the predicted probability and actual probability (p> 0.05).

Conclusion: Models 1 and 2 had reliable, efficient and visual predictive value for CSPCa. The ADCmean is an important predictive indicator.