Construction and Validation of a Clinical Predictive Nomogram for Improving the Cancer Detection of Prostate Naive Biopsy Based on Chinese Multicenter Clinical Data - Tao, Tao; Wang, Changming; Liu, Weiyong; Yuan, Lei; Ge, Qingyu; Zhang, Lang; He, Biming; Wang, Lei; Wang, Ling; Xiang, Caiping; Wang, Haifeng; Chen, Shuqiu; et al. (Frontiers in Oncology, 2022)
Objectives: Prostate biopsy is a common approach for the diagnosis of prostate cancer (PCa) in patients with suspicious PCa. In order to increase the detection rate of prostate naive biopsy, we constructed an effective nomogram for predicting the diagnosis of PCa prior to biopsy. Materials and Methods: The data of 1428 patients who underwent prostate biopsy in three Chinese medical centers from January 2018 to June 2021 was used to conduct this retrospective study. KD cohort consisted of 701 patients was used for model construction and internal validation; DF cohort consisted of 385 patients and ZD cohort consisted of 342 patients were used for external validation. Independent predictors were selected by univariate and multivariate binary logistic regression analysis and adopted for establishing the predictive nomogram. The apparent performance of the model was evaluated via internal validation and geographically external validation. For assessing the clinical utility of our model, the decision curve analysis was also performed. Results: The results of univariate and multivariate logistic regression analysis showed PSAD (P<0.001, OR:2.102, 95%CI:1.687-2.062) and PI-RADS grade (P<0.001, OR:4.528, 95%CI:2.752-7.453) were independent predictors of PCa before biopsy. Therefore, a nomogram composed of PSAD and PI-RADS grade was constructed. Internal validation in developed cohort showed that the nomogram had good discrimination (AUC=0.804) and calibration curve indicated the predicted incidence was consistent with observed incidence of PCa, the brier score was 0.172. External validation was performed in DF cohort and ZD cohort. AUC value was 0.884, 0.882 in DF cohort and ZD cohort respectively. Calibration curves elucidated great predicted accuracy of PCa in two validation cohorts, brier score was 0.129 in DF cohort and 0.131 in ZD cohort. Decision curve analysis showed our model can add net benefits for patients. The apparent performance of our nomogram was also be assessed in three different PSA group and the results were as good as we expected. Conclusions: In this study, we put forward a simple and convenient clinical predictive model comprised of PSAD and PI-RADS grade with excellent reproducibility and generalizability. Which provides a novel indicator for prediction of the diagnosis of individual patient with suspicious PCa.