Prospective validation of a multi-marker prostate cancer prediction model

描述（由申请人提供）：美国每年进行约一百万例前列腺癌活检。大多数是不必要的：前列腺活检最常见的原因是血液中前列腺特异性抗原（PSA）水平升高，但大多数PSA升高的男性并没有前列腺癌。在七项独立的研究中，涉及超过7500名男性和2250种癌症，我们已经表明，基于测量PSA亚型和激肽释放酶相关肽酶2（hK2）的统计模型是PSA升高男性前列腺活检结果的高度准确预测因子。在我们的初步研究中，应用于独立验证集的模型曲线下面积为0.76，远高于单独PSA（0.64）。我们还进行了决策分析，证明使用统计模型来确定前列腺活检的转诊将减少大约一半的不必要活检数量，但只错过了少数癌症，几乎所有这些癌症都是通常被认为构成过度诊断的低级别和低分期癌症。我们之前的所有研究都是在欧洲人群中回顾性进行的，使用的是在一个研究实验室中分析的冷冻存档样本。在本提案中，我们将首先寻求评估回顾性应用于美国队列时的统计模型。然后，我们将测试独立的临床实验室是否可以使用对照样品准确地测量四种激肽释放酶的面板。然后，我们将在预定的活检前继续前瞻性地收集患者的研究血液。该样本将在当地进行真实的分析，尽管计划的活检将继续进行，而不考虑标志物结果，活检结果与统计模型的预测结果进行比较。最后，我们将探讨该模型的实施将如何影响临床实践，使用决策分析模拟和小插曲研究。

DESCRIPTION (provided by applicant): Approximately one million biopsies for prostate cancer are conducted each year in the US. The majority are unnecessary: the most common reason for a prostate biopsy is an elevated level of prostate-specific antigen (PSA) in the blood, but most men with elevated PSA do not have prostate cancer. In seven separate studies, involving over 7500 men and 2250 cancers, we have shown that a statistical model based on measuring isoforms of PSA, and kallikrein-related peptidase 2 (hK2), is a highly accurate predictor of prostate biopsy outcome in men with elevated PSA. In our primary study, the area-under-the-curve of the model was applied to an independent validation set was 0.76, far higher than PSA alone (0.64). We have also conducted decision analyses demonstrating that use of the statistical model to determine referral for prostate biopsy would reduce the number of unnecessary biopsies by about half, but miss only a small number of cancers, almost all of which would be the sort of low grade and stage cancers typically thought to constitute overdiagnosis. All of our prior studies were retrospectively conducted on European populations using frozen archived samples analyzed in a single research laboratory. In this proposal, we will first seek to evaluate the statistical model when applied retrospectively to a US cohort. We will then test whether independent clinical laboratories can measure the panel of four kallikreins accurately using control samples. We will then go on to prospectively collect research blood from patients before a scheduled biopsy. This sample will be analyzed locally, in real time, although the scheduled biopsy will continue irrespective of marker results, with biopsy outcome compared with the prediction from the statistical model. Finally, we will explore how implementation of the model would affect clinical practice using decision-analytic simulation and a vignette study.