前列腺癌
医学
前列腺
接收机工作特性
逻辑回归
前列腺特异性抗原
单变量
前列腺活检
单变量分析
活检
多元分析
肿瘤科
泌尿科
内科学
癌症
多元统计
统计
数学
作者
Yin Lei,Tian Jie Li,Peng Gu,Yu kun Yang,Lei Zhao,Chao Gao,Juan Hu,Xiaodong Liu
标识
DOI:10.3389/fonc.2022.992032
摘要
Globally, Prostate cancer (PCa) is the second most common cancer in the male population worldwide, but clinically significant prostate cancer (CSPCa) is more aggressive and causes to more deaths. The authors aimed to construct the risk category based on Prostate Imaging Reporting and Data System score version 2.1 (PI-RADS v2.1) in combination with Prostate-Specific Antigen Density (PSAD) to improve CSPCa detection and avoid unnecessary biopsy. Univariate and multivariate logistic regression and receiver-operating characteristic (ROC) curves were performed to compare the efficacy of the different predictors. The results revealed that PI-RADS v2.1 score and PSAD were independent predictors for CSPCa. Moreover, the combined factor shows a significantly higher predictive value than each single variable for the diagnosis of CSPCa. According to the risk stratification model constructed based on PI-RADS v2.1 score and PSAD, patients with PI-RADS v2.1 score of ≤2, or PI-RADS V2.1 score of 3 and PSA density of <0.15 ng/mL2, can avoid unnecessary of prostate biopsy and does not miss clinically significant prostate cancer.
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