MRI-Based Nomogram of Prostate Maximum Sectional Area and Its Zone Area for Prediction of Prostate Cancer

Frontiers in Oncology

Zhenlin Chen²  Wenzhong Zheng²  Zhangcheng Huang²  Yaoan Wen²  Bingqiao Liu²  Mengqiang Li²  Yue Xu²  Shaoqin Jiang² 

[1] Department of Urology, Changhai Hospital, Second Military University, Shanghai, China;Laboratory of Urology, Department of Urology, Fujian Union Hospital, Fujian Medical University, Fuzhou, China;

关键词: nomogram;    prostate maximum sectional area;    prostate zone area;    prostate cancer;    prostate biopsy;   

DOI  :  10.3389/fonc.2021.708730

来源: DOAJ

【 摘 要 】

ObjectiveTo reduce unnecessary prostate biopsies, we designed a magnetic resonance imaging (MRI)-based nomogram prediction model of prostate maximum sectional area (PA) and investigated its zone area for diagnosing prostate cancer (PCa).MethodsMRI was administered to 691 consecutive patients before prostate biopsies from January 2012 to January 2020. PA, central gland sectional area (CGA), and peripheral zone sectional area (PZA) were measured on axial T2-weighted prostate MRI. Multivariate logistic regression analysis and area under the receiver operating characteristic (ROC) curve were performed to evaluate and integrate the predictors of PCa. Based on multivariate logistic regression coefficients after excluding combinations of collinear variables, three models and nomograms were generated and intercompared by Delong test, calibration curve, and decision curve analysis (DCA).ResultsThe positive rate of PCa was 46.74% (323/691). Multivariate analysis revealed that age, PSA, MRI, transCGA, coroPZA, transPA, and transPAI (transverse PZA-to-CGA ratio) were independent predictors of PCa. Compared with no PCa patients, transCGA (AUC = 0.801) was significantly lower and transPAI (AUC = 0.749) was significantly higher in PCa patients. Both of them have a significantly higher AUC than PSA (AUC = 0.714) and PV (AUC = 0.725). Our best predictive model included the factors age, PSA, MRI, transCGA, and coroPZA with the AUC of 0.918 for predicting PCa status. Based on this predictive model, a novel nomogram for predicting PCa was conducted and internally validated (C-index = 0.913).ConclusionsWe found the potential clinical utility of transCGA and transPAI in predicting PCa. Then, we firstly built the nomogram based on PA and its zone area to evaluate its diagnostic efficacy for PCa, which could reduce unnecessary prostate biopsies.

【 授权许可】

Unknown