Lei Yin, Li Tian Jie, Gu Peng, Yang Yu Kun, Zhao Lei, Gao Chao, Hu Juan, Liu Xiao Dong
Department of Urology, The First People's Hospital of Shuangliu District, Chengdu, China.
School of Clinical Medicine, Tsinghua University, Beijing, China.
Front Oncol. 2022 Sep 23;12:992032. doi: 10.3389/fonc.2022.992032. eCollection 2022.
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/mL, can avoid unnecessary of prostate biopsy and does not miss clinically significant prostate cancer.
在全球范围内,前列腺癌(PCa)是全球男性中第二常见的癌症,但具有临床意义的前列腺癌(CSPCa)更具侵袭性,导致更多死亡。作者旨在基于前列腺影像报告和数据系统第2.1版(PI-RADS v2.1)评分并结合前列腺特异性抗原密度(PSAD)构建风险类别,以改善CSPCa的检测并避免不必要的活检。进行单因素和多因素逻辑回归以及受试者工作特征(ROC)曲线分析,以比较不同预测指标的效能。结果显示,PI-RADS v2.1评分和PSAD是CSPCa的独立预测指标。此外,联合因素对CSPCa诊断的预测价值明显高于每个单一变量。根据基于PI-RADS v2.1评分和PSAD构建的风险分层模型,PI-RADS v2.1评分为≤2,或PI-RADS V2.1评分为3且PSA密度<0.15 ng/mL的患者可避免不必要的前列腺活检,且不会漏诊具有临床意义的前列腺癌。