Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK; Joseph Fourier University-Grenoble 1, CNRS, TIMC-IMAG UMR 5525, Grenoble; Medical Evaluation Unit, Grenoble University Hospital, Grenoble, France.
Ann Oncol. 2015 May;26(5):848-864. doi: 10.1093/annonc/mdu525. Epub 2014 Nov 17.
Despite the extensive development of risk prediction models to aid patient decision-making on prostate screening, it is unknown whether these models could improve predictive accuracy of PSA testing to detect prostate cancer (PCa). The objective of this study was to perform a systematic review to identify PCa risk models and to assess the model's performance to predict PCa by conducting a meta-analysis.
A systematic literature search of Medline was conducted to identify PCa predictive risk models that used at least two variables, of which one of the variables was prostate-specific antigen (PSA) level. Model performance (discrimination and calibration) was assessed. Prediction models validated in ≥5 study populations and reported area under the curve (AUC) for prediction of any or clinically significant PCa were eligible for meta-analysis. Summary AUC and 95% CIs were calculated using a random-effects model.
The systematic review identified 127 unique PCa prediction models; however, only six models met study criteria for meta-analysis for predicting any PCa: Prostataclass, Finne, Karakiewcz, Prostate Cancer Prevention Trial (PCPT), Chun, and the European Randomized Study of Screening for Prostate Cancer Risk Calculator 3 (ERSPC RC3). Summary AUC estimates show that PCPT does not differ from PSA testing (0.66) despite performing better in studies validating both PSA and PCPT. Predictive accuracy to discriminate PCa increases with Finne (AUC = 0.74), Karakiewcz (AUC = 0.74), Chun (AUC = 0.76) and ERSPC RC3 and Prostataclass have the highest discriminative value (AUC = 0.79), which is equivalent to doubling the sensitivity of PSA testing (44% versus 21%) without loss of specificity. The discriminative accuracy of PCPT to detect clinically significant PCa was AUC = 0.71. Calibration measures of the models were poorly reported.
Risk prediction models improve the predictive accuracy of PSA testing to detect PCa. Future developments in the use of PCa risk models should evaluate its clinical effectiveness in practice.
尽管已经开发出许多风险预测模型来帮助患者在前列腺筛查方面做出决策，但尚不清楚这些模型是否能够提高 PSA 检测检测前列腺癌 (PCa) 的预测准确性。本研究的目的是进行系统评价，以确定用于预测 PCa 的风险模型，并通过荟萃分析评估模型预测 PCa 的性能。
系统地检索了 Medline 中的文献，以确定使用至少两个变量的 PCa 预测风险模型，其中一个变量是前列腺特异性抗原 (PSA) 水平。评估了模型性能（区分度和校准度）。符合以下条件的预测模型可纳入荟萃分析：在≥5 个研究人群中得到验证，并报告了预测任何或临床显著 PCa 的曲线下面积 (AUC)。使用随机效应模型计算汇总 AUC 和 95%置信区间 (CI)。
系统评价确定了 127 个独特的 PCa 预测模型；然而，只有 6 个模型符合荟萃分析的研究标准，可用于预测任何 PCa：Prostataclass、Finne、Karakiewcz、前列腺癌预防试验 (PCPT)、Chun 和欧洲前列腺癌筛查风险计算器 3 (ERSPC RC3)。汇总 AUC 估计值表明，尽管 PCPT 在同时验证 PSA 和 PCPT 的研究中表现更好，但与 PSA 检测相比，其性能并不不同（0.66）。随着 Finne（AUC=0.74）、Karakiewcz（AUC=0.74）、Chun（AUC=0.76）和 ERSPC RC3 的预测准确性增加，预测区分 PCa 的准确性增加，而 Prostataclass 具有最高的区分值（AUC=0.79），这相当于将 PSA 检测的敏感性提高了一倍（44% 对 21%），而特异性没有损失。PCPT 检测临床显著 PCa 的判别准确性为 AUC=0.71。模型的校准度量值报告较差。
风险预测模型提高了 PSA 检测检测 PCa 的预测准确性。未来应评估 PCa 风险模型在实践中的临床效果。
