Cormio Luigi, Cindolo Luca, Troiano Francesco, Marchioni Michele, Di Fino Giuseppe, Mancini Vito, Falagario Ugo, Selvaggio Oscar, Sanguedolce Francesca, Fortunato Francesca, Schips Luigi, Carrieri Giuseppe
Department of Urology and Renal Transplantation, University of Foggia, Foggia, Italy.
Department of Urology, ASL, Chieti, Italy.
Front Oncol. 2018 Oct 16;8:438. doi: 10.3389/fonc.2018.00438. eCollection 2018.
The present study aimed to determine the ability of novel nomograms based onto readily-available clinical parameters, like those related to benign prostatic obstruction (BPO), in predicting the outcome of first prostate biopsy (PBx). To do so, we analyzed our Internal Review Board-approved prospectively-maintained PBx database. Patients with PSA>20 ng/ml were excluded because of their high risk of harboring prostate cancer (PCa). A total of 2577 were found to be eligible for study analyses. The ability of age, PSA, digital rectal examination (DRE), prostate volume (PVol), post-void residual urinary volume (PVR), and peak flow rate (PFR) in predicting PCa and clinically-significant PCa (CSPCa)was tested by univariable and multivariable logistic regression analysis. The predictive accuracy of the multivariate models was assessed using receiver operator characteristic curves analysis, calibration plot, and decision-curve analyses (DCA). Nomograms predicting PCa and CSPCa were built using the coefficients of the logit function. Multivariable logistic regression analysis showed that all variables but PFR significantly predicted PCA and CSPCa. The addition of the BPO-related variables PVol and PVR to a model based on age, PSA and DRE findings increased the model predictive accuracy from 0.664 to 0.768 for PCa and from 0.7365 to 0.8002 for CSPCa. Calibration plot demonstrated excellent models' concordance. DCA demonstrated that the model predicting PCa is of value between ~15 and ~80% threshold probabilities, whereas the one predicting CSPCa is of value between ~10 and ~60% threshold probabilities. In conclusion, our novel nomograms including PVR and PVol significantly increased the accuracy of the model based on age, PSA and DRE in predicting PCa and CSPCa at first PBx. Being based onto parameters commonly assessed in the initial evaluation of men "prostate health," these novel nomograms could represent a valuable and easy-to-use tool for physicians to help patients to understand their risk of harboring PCa and CSPCa.
本研究旨在确定基于易于获得的临床参数(如与良性前列腺梗阻[BPO]相关的参数)的新型列线图预测首次前列腺穿刺活检(PBx)结果的能力。为此,我们分析了经内部审查委员会批准的前瞻性维护的PBx数据库。由于前列腺癌(PCa)风险较高,PSA>20 ng/ml的患者被排除。共发现2577例符合研究分析条件。通过单变量和多变量逻辑回归分析测试年龄、PSA、直肠指检(DRE)、前列腺体积(PVol)、排尿后残余尿量(PVR)和峰值流速(PFR)预测PCa和临床显著性PCa(CSPCa)的能力。使用受试者工作特征曲线分析、校准图和决策曲线分析(DCA)评估多变量模型的预测准确性。使用逻辑函数系数构建预测PCa和CSPCa的列线图。多变量逻辑回归分析表明,除PFR外,所有变量均显著预测PCA和CSPCa。将与BPO相关的变量PVol和PVR添加到基于年龄、PSA和DRE结果的模型中,PCa的模型预测准确性从0.664提高到0.768,CSPCa的模型预测准确性从0.7365提高到0.8002。校准图显示模型具有良好的一致性。DCA表明,预测PCa的模型在阈值概率约15%至约80%之间具有价值,而预测CSPCa的模型在阈值概率约10%至约60%之间具有价值。总之,我们包含PVR和PVol的新型列线图显著提高了基于年龄、PSA和DRE的模型在预测首次PBx时PCa和CSPCa的准确性。基于男性前列腺健康初始评估中常用的参数,这些新型列线图可能是医生帮助患者了解其患PCa和CSPCa风险的有价值且易于使用的工具。