Glickman Urological and Kidney Institute and Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio 44195, USA.
J Urol. 2010 Feb;183(2):529-33. doi: 10.1016/j.juro.2009.10.007. Epub 2009 Dec 14.
Statistical models such as the Prostate Cancer Prevention Trial risk calculator have been developed to estimate the cancer risk in an individual and help determine indications for biopsy. We assessed risk calculator performance in a large contemporary cohort of patients sampled by extended biopsy schemes.
The validation cohort comprised 3,482 men who underwent a total of 4,515 prostate biopsies. Calculator performance was evaluated by ROC AUC and calibration plots. A multivariate regression model was fitted to address important predictor variables in the validation data set. Prediction error was calculated as the response variable in another multivariate regression model.
Using an average of 13 cores per biopsy prostate cancer was detected in 1,862 patients. The calculator showed an AUC of 0.57 to predict all cancers and 0.60 for high grade cancer. Multivariate analysis of the predictive ability of various clinical factors revealed that race and the number of biopsy cores did not predict overall or high grade cancer at biopsy. Prior negative biopsy, patient age and free prostate specific antigen were significantly associated with prediction error for overall and high grade cancer. Race and family history had a significant association with prediction error only for high grade disease.
To our knowledge our external validation of the Prostate Cancer Prevention Trial risk calculator was done in the largest cohort of men screened for prostate cancer to date. Results suggest that the current calculator remains predictive but does not maintain initial accuracy in contemporary patients sampled by more extensive biopsy schemes. Data suggest that the predictive ability of the calculator in current clinical practice may be improved by modeling contemporary data and/or incorporating additional prognostic variables.
统计模型（如前列腺癌预防试验风险计算器）已被开发出来，以估计个体的癌症风险，并帮助确定活检的指征。我们评估了风险计算器在通过扩展活检方案抽样的大型当代患者队列中的性能。
验证队列包括 3482 名接受总共 4515 次前列腺活检的男性。通过 ROC AUC 和校准图评估计算器性能。拟合多变量回归模型以解决验证数据集中的重要预测变量。预测误差作为另一个多变量回归模型中的响应变量进行计算。
使用平均每个活检 13 个核心，在 1862 名患者中检测到前列腺癌。该计算器预测所有癌症的 AUC 为 0.57，预测高级别癌症的 AUC 为 0.60。对各种临床因素预测能力的多变量分析表明，种族和活检核心数量不能预测活检时的总体或高级别癌症。先前的阴性活检、患者年龄和游离前列腺特异性抗原与总体和高级别癌症的预测误差显著相关。种族和家族史与高级别疾病的预测误差有显著关联。
据我们所知，我们对前列腺癌预防试验风险计算器的外部验证是在迄今为止筛查前列腺癌的最大男性队列中进行的。结果表明，当前的计算器仍然具有预测性，但在通过更广泛的活检方案抽样的当代患者中无法保持初始准确性。数据表明，通过建模当前数据和/或纳入其他预后变量，计算器在当前临床实践中的预测能力可能会得到改善。
