前列腺癌
医学
前列腺
前列腺特异性抗原
接收机工作特性
泌尿科
曲线下面积
逻辑回归
单变量分析
经直肠超声检查
多元分析
肿瘤科
内科学
癌症
作者
Song Zheng,Shaoqin Jiang,Zhen-Lin Chen,Zhangcheng Huang,Wenzhen Shi,Bingqiao Liu,Yue Xu,Guo Yinan,Huijie Yang,Mengqiang Li
出处
期刊:PLOS ONE
[Public Library of Science]
日期:2019-11-19
卷期号:14 (11): e0218645-e0218645
被引量:16
标识
DOI:10.1371/journal.pone.0218645
摘要
Prostate biopsies are frequently performed to screen for prostate cancer (PCa) with complications such as infections and bleeding. To reduce unnecessary biopsies, here we designed an improved predictive model of MRI-based prostate volume and associated zone-adjusted prostate-specific antigen (PSA) concentrations for diagnosing PCa and risk stratification. Multiparametric MRI administered to 422 consecutive patients before initial transrectal ultrasonography-guided 13-core prostate biopsies from January 2012 to March 2018 at Fujian Medical University Union Hospital. Univariate and multivariate logistic regression analyses and determination of the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was performed to evaluate and integrate the predictors of PCa and high-risk prostate cancer (HR-PCa). The detection rates of PCa was 43.84% (185/422). And the detection rates of HR-PCa was 71.35% (132/185) in PCa patients. Multivariate analysis revealed that prostate volume(PV), PSA density(PSAD), transitional zone volume(TZV), PSA density of the transitional zone(PSADTZ), and MR were independent predictors of PCa and HR-PCa. PSA, peripheral zone volume(PZV) and PSA density of the peripheral zone(PSADPZ) were independent predictors of PCa but not HR-PCa. The AUC of our best predictive model including PSA + PV + PSAD + MR + TZV or PSA + PV + PSAD + MR + PZV was 0.906 for PCa. The AUC of the best predictive model of PV + PSAD + MR + TZV was 0.893 for HR-PCa. In conclusion, our results will likely improve the detection rate of prostate cancer, avoiding unnecessary prostate biopsies, and for evaluating risk stratification.
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