首页> 美国卫生研究院文献>Frontiers in Oncology >Development and Internal Validation of Novel Nomograms Based on Benign Prostatic Obstruction-Related Parameters to Predict the Risk of Prostate Cancer at First Prostate Biopsy
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Development and Internal Validation of Novel Nomograms Based on Benign Prostatic Obstruction-Related Parameters to Predict the Risk of Prostate Cancer at First Prostate Biopsy

机译:基于良性前列腺梗阻相关参数的新型线型图的开发和内部验证以预测首次前列腺活检时前列腺癌的风险

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摘要

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.
机译:本研究旨在根据可得的临床参数(如与良性前列腺梗阻(BPO)相关的参数)确定新列线图在预测首次前列腺活检(PBx)结果中的能力。为此,我们分析了内部审查委员会批准的前瞻性维护的PBx数据库。 PSA> 20 ng / ml的患者因患有前列腺癌(PCa)的风险较高而被排除在外。共有2577个样本符合研究分析的条件。测试了年龄,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和临床上重要PCa(CSPCa)的能力通过单变量和多变量逻辑回归分析。使用接收机操作员特征曲线分析,校准图和决策曲线分析(DCA)评估了多元模型的预测准确性。使用logit函数的系数建立了预测PCa和CSPCa的线型图。多变量logistic回归分析显示,除PFR外,所有变量均显着预测PCA和CSPCa。将基于年龄,PSA和DRE发现的BPO相关变量PVol和PVR添加到模型中,PCa的模型预测准确性从0.664增至0.768,CSPCa的模型预测准确性从0.7365增至0.8002。校准图显示了出色的模型一致性。 DCA证明,预测PCa的模型的值在〜15%到〜80%阈值概率之间,而预测CSPCa的模型的值在〜10%到〜60%阈值概率之间。总之,我们的新列线图(包括PVR和PVol)大大提高了基于年龄,PSA和DRE的模型在预测第一个PBx时PCa和CSPCa的准确性。这些新颖的列线图基于对男性“前列腺健康”进行初步评估时通常评估的参数,可以代表医生一种有价值且易于使用的工具,以帮助患者了解其携带PCa和CSPCa的风险。

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