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The added value of percentage of free to total prostate-specific antigen, PCA3, and a kallikrein panel to the erspc risk calculator for prostate cancer in prescreened men

机译:预先筛查的男性前列腺癌的erspc风险计算器中游离总前列腺特异性抗原,PCA3和激肽释放酶面板的百分比相对于前列腺癌的附加价值

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Objective To assess the value added by percentage of free to total PSA (%fPSA), prostate cancer antigen 3 (PCA3), and a kallikrein panel (4k-panel) to the European Randomised Study of Screening for Prostate Cancer (ERSPC) multivariable prediction models: risk calculator (RC) 4, including transrectal ultrasound, and RC 4 plus digital rectal examination (4+DRE) for prescreened men. Design, setting, and participants Participants were invited for rescreening between October 2007 and February 2009 within the Dutch part of the ERSPC study. Biopsies were taken in men with a PSA level ≥3.0 ng/ml or a PCA3 score ≥10. Additional analyses of the 4k-panel were done on serum samples.Outcome measurements and statistical analysis Outcome was defined as PCa detectable by sextant biopsy. Receiver operating characteristic curve and decision curve analyses were performed to compare the predictive capabilities of %fPSA, PCA3, 4k-panel, the ERSPC RCs, and their combinations in logistic regression models.Results and limitations PCa was detected in 119 of 708 men. The %fPSA did not perform better univariately or added to the RCs compared with the RCs alone. In 202 men with an elevated PSA, the 4k-panel discriminated better than PCA3 when modelled univariately (area under the curve [AUC]: 0.78 vs 0.62; p = 0.01). The multivariable models with PCA3 or the 4k-panel were equivalent (AUC: 0.80 for RC 4+DRE). In the total population, PCA3 discriminated better than the 4k-panel (univariate AUC: 0.63 vs 0.56; p = 0.05). There was no statistically significant difference between the multivariable model with PCA3 (AUC: 0.73) versus the model with the 4k-panel (AUC: 0.71; p = 0.18). The multivariable model with PCA3 performed better than the reference model (0.73 vs 0.70; p = 0.02). Decision curves confirmed these patterns, although numbers were small.Conclusions Both PCA3 and, to a lesser extent, a 4k-panel have added value to the DRE-based ERSPC RC in detecting PCa in prescreened men.Patient summary We studied the added value of novel biomarkers to previously developed risk prediction models for prostate cancer. We found that inclusion of these biomarkers resulted in an increase in predictive ability.Background Prostate-specific antigen (PSA) testing has limited accuracy for the early detection of prostate cancer (PCa).
机译:目的评估游离PSA占总PSA(%fPSA),前列腺癌抗原3(PCA3)和激肽释放酶面板(4k-panel)的百分比增加的价值,用于欧洲前列腺癌筛查随机研究(ERSPC)多变量预测型号:风险计算器(RC)4(包括经直肠超声检查)和RC 4加上数字直肠检查(4 + DRE)用于预筛查的男性。设计,设置和参与者我们邀请了参与者在ERSPC研究的荷兰部分进行的2007年10月至2009年2月之间进行重新筛查。对PSA≥3.0 ng / ml或PCA3评分≥10的男性进行活检。对4k面板的其他分析在血清样本上进行。结果测量和统计分析结果定义为六分仪活检可检测到的PCa。进行受试者工作特征曲线和决策曲线分析,以比较%fPSA,PCA3、4k面板,ERSPC RC及其组合在logistic回归模型中的预测能力。在708名男性中,有119名检测到PCa的结果和局限性。与单独的RC相比,%fPSA的性能没有单变量更好,也没有添加到RC中。在202名PSA升高的男性中,单变量建模时4k面板的分辨力优于PCA3(曲线下的面积[AUC]:0.78 vs 0.62; p = 0.01)。具有PCA3或4k面板的多变量模型是等效的(AUC:RC 4 + DRE为0.80)。在总人群中,PCA3的区分要好于4k面板(单变量AUC:0.63对0.56; p = 0.05)。 PCA3(AUC:0.73)的多变量模型与4k面板(AUC:0.71; p = 0.18)的模型之间没有统计学显着差异。 PCA3的多变量模型的性能优于参考模型(0.73 vs 0.70; p = 0.02)。决策曲线证实了这些模式,尽管数量很少。结论PCA3和4k面板在基于DRE的ERSPC RC中检测预筛查的男性PCa方面均增加了价值。以前开发的前列腺癌风险预测模型的新生物标记。我们发现包含这些生物标志物可提高预测能力。背景前列腺特异性抗原(PSA)检测对前列腺癌(PCa)的早期检测准确性有限。

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