AUTHOR=Jiang Shaoqin , Huang Zhangcheng , Liu Bingqiao , Chen Zhenlin , Xu Yue , Zheng Wenzhong , Wen Yaoan , Li Mengqiang 

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

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.708730

DOI=10.3389/fonc.2021.708730

ISSN=2234-943X

ABSTRACT=To reduce unnecessary prostate biopsies, we designed a magnetic resonance imaging (MRI)-based nomogram prediction model of prostate maximum area (PA) and investigated its zone area for diagnosing prostate cancer (PCa). MRI was administered to 691 consecutive patients before prostate biopsies from January 2012 to January 2019. PA and central gland area (CGA) 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, 3 models and nomograms were generated and intercompared by Delong-test, Calibration curve and Decision curve analysis (DCA). The positive rate of PCa was 46.74% (323/691). Multivariate analysis revealed that age, PSA, PA, CGA, PZA, and MRI were independent predictors of PCa. Our best predictive model included the factors of Age, PSA, MRI, transCGA, coroPZA with the area under the curve 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). 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.