Construction and Validation of a Clinical Predictive Nomogram for Improving the Cancer Detection of Prostate Naive Biopsy Based on Chinese Multicenter Clinical Data

Frontiers in Oncology

Oncology

Lei Yuan¹  Weiyong Liu²  Lang Zhang³  Shuqiu Chen³  Biming He⁴  Haifeng Wang⁴  Lei Wang⁵  Ling Wang⁵  Tao Tao⁵  Jun Xiao⁵  Qingyu Ge⁵  Caiping Xiang⁵  Changming Wang⁵ 

[1] Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China;Department of Ultrasound, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China;Department of Urology, Affiliated Zhongda Hospital of Southeast University, Nanjing, China;Department of Urology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China;Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai, China;Department of Urology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China;

关键词: prostate cancer;    prostate biopsy;    mpMRI;    PI-RADS score;    PSAD;    nomogram;   

DOI  :  10.3389/fonc.2021.811866

 received in 2021-11-09, accepted in 2021-12-28,  发布年份 2022

来源: Frontiers

【 摘 要 】

ObjectivesProstate 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 two effective nomograms for predicting the diagnosis of PCa and clinically significant PCa (csPCa) prior to biopsy.Materials and MethodsThe data of 1,428 patients who underwent prostate biopsy in three Chinese medical centers from January 2018 to June 2021 were used to conduct this retrospective study. The KD cohort, which consisted of 701 patients, was used for model construction and internal validation; the DF cohort, which consisted of 385 patients, and the ZD cohort, which 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, decision curve analysis was also performed.ResultsThe results of univariate and multivariate logistic regression analysis showed prostate-specific antigen density (PSAD) (P<0.001, OR:2.102, 95%CI:1.687-2.620) and prostate imaging-reporting and data system (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 the developed cohort showed that the nomogram had good discrimination (AUC=0.804), and the calibration curve indicated that the predicted incidence was consistent with the observed incidence of PCa; the brier score was 0.172. External validation was performed in the DF and ZD cohorts. The AUC values were 0.884 and 0.882, in the DF and ZD cohorts, respectively. Calibration curves elucidated greatly predicted the accuracy of PCa in the two validation cohorts; the brier scores were 0.129 in the DF cohort and 0.131 in the ZD cohort. Decision curve analysis showed that our model can add net benefits for patients. A separated predicted model for csPCa was also established and validated. The apparent performance of our nomogram for PCa was also assessed in three different PSA groups, and the results were as good as we expected.ConclusionsIn this study, we put forward two simple and convenient clinical predictive models comprised of PSAD and PI-RADS grade with excellent reproducibility and generalizability. They provide a novel calculator for the prediction of the diagnosis of an individual patient with suspicious PCa.

【 授权许可】

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Copyright © 2022 Tao, Wang, Liu, Yuan, Ge, Zhang, He, Wang, Wang, Xiang, Wang, Chen and Xiao