New Diagnostic Model for Clinically Significant Prostate Cancer in Biopsy-Naïve Men With PIRADS 3

PURPOSE: The aim of this study was to explore a new model of clinical decision-making to predict the occurrence of clinically significant prostate cancer (csPCa). PATIENTS AND METHODS: The demographic and clinical characteristics of 152 patients were recorded. Prostate-specific antigen (PSA), PSA density (PSAD), adjusted PSAD of peripheral zone (aPSADPZ), and peripheral zone volume ratio (PZ ratio) were calculated and subjected to receiver operating characteristic (ROC) curve analysis. The calibration and discrimination abilities of new nomograms were verified with calibration curve and area under the ROC curve (AUC). The clinical benefits of these models were evaluated by decision curve analysis and clinical impact curves. RESULTS: The AUCs of PSA, PSAD, aPSADPZ, and PZ ratio were 0.521, 0.645, 0.745, and 0.717 for prostate cancer (PCa) diagnosis, while the corresponding values were 0.590, 0.678, 0.780, and 0.731 for csPCa diagnosis, respectively. All nomograms displayed higher net benefit and better overall calibration than the scenarios for predicting the occurrence of csPCa. The new model significantly improved the diagnostic accuracy of csPCa (0.865 vs. 0.741, p = 0.0284) compared with the base model. In addition, the new model was better than the base model for predicting csPCa in the low or medium probability while the number of patients with csPCa predicted by the new model was in good agreement with the actual number of patients with csPCa in the high-risk threshold. CONCLUSIONS: This study demonstrates that aPSADPZ has a higher predictive accuracy for csPCa diagnosis than the conventional indicators. Including aPSADPZ, PZ ratio, and age can improve csPCa diagnosis and avoid unnecessary biopsies.