Department of Urology, Icahn School of Medicine at Mount Sinai Hospital, New York, NY, USA.
Department of Pathology, Icahn School of Medicine at Mount Sinai Hospital, New York, NY, USA.
Urol J. 2022 Nov 8;19(5):379-385. doi: 10.22037/uj.v18i.6852.
Prostate biopsies are associated with infectious complications and approximately 80% are either benign or clinically insignificant prostate cancer. Our aim is to develop and independently validate prediction model to avoid unnecessary prostate biopsies by predicting clinically significant prostate cancer (csPCa) Materials and Methods: Retrospective analysis of single-center cohort (Mount Sinai Hospital, NY) of 1632 men who underwent systematic or combined systematic and Magnetic Resonance Imaging (MRI)/ultrasound fusion targeted prostate biopsy between 2014-2020. External cohort (University of Miami) included 622 men that underwent biopsy. Outcome for predicting csPCa was defined as International Society of Urologic Pathology (ISUP) Gleason grade ≥ 2 on biopsy. Multivariable logistic regression analysis was performed to build nomogram using coefficients of logit function. Nomogram validation was performed in external cohort by plotting receiver operating characteristics (ROC). We also plotted decision curve analysis (DCA) and compared nomogram-predicted probabilities with actual rates of csPCa probabilities in external cohort.
Of 1632 men, 43% showed csPCa on biopsy. PSA density, prior negative biopsy, and Prostate Imaging and Reporting Data System (PI-RADS) scores 3, 4, and 5 were significant predictors for csPCa. ROC for prediction of csPCa was 0.88 in external cohort. There was agreement between predicted and actual rate of csPCa in external cohort. DCA demonstrated net benefit using the model. Using the prediction model at threshold of 30, 35% of biopsies and 46% of diagnosed indolent PCa could be avoided, while missing 5% of csPCa.
Using our prediction model can help reduce unnecessary prostate biopsies with minimal impact on csPCa detection rates.
前列腺活检与感染并发症有关，约 80%的前列腺活检为良性或临床意义不大的前列腺癌。我们的目的是开发并独立验证预测模型，通过预测临床显著前列腺癌（csPCa）来避免不必要的前列腺活检。
回顾性分析了 2014 年至 2020 年间在纽约西奈山医院（Mount Sinai Hospital）接受系统或系统联合磁共振成像（MRI）/超声融合靶向前列腺活检的 1632 名男性的单中心队列（迈阿密大学）的 622 名男性进行了活检。预测 csPCa 的结果定义为活检时国际泌尿病理学会（ISUP）Gleason 分级≥2。使用逻辑函数的系数进行多变量逻辑回归分析，建立列线图。在外部队列中通过绘制接受者操作特征（ROC）来验证列线图。我们还绘制了决策曲线分析（DCA），并将列线图预测的概率与外部队列中实际的 csPCa 概率进行了比较。
在 1632 名男性中，43%的人在活检中显示出 csPCa。前列腺特异性抗原密度、先前阴性活检和前列腺成像和报告数据系统（PI-RADS）评分 3、4 和 5 是 csPCa 的显著预测因子。外部队列中预测 csPCa 的 ROC 为 0.88。在外部队列中，预测的 csPCa 率与实际率之间存在一致性。DCA 表明该模型具有净收益。使用预测模型的阈值为 30、35%的活检和 46%的诊断为惰性 PCa 可以避免，而错过 5%的 csPCa。
使用我们的预测模型可以帮助减少不必要的前列腺活检，同时对 csPCa 的检测率影响最小。
