Prospective validation of a multi-marker prostate cancer prediction model

描述(由申请人提供)：在美国，每年大约有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升高的男性没有前列腺癌。在七项单独的研究中，涉及超过7500名男性和2250名癌症患者，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的预测因子。在我们的初步研究中，模型应用于独立验证集的曲线下面积为0.76，远远高于单独使用PSA(0.64)。我们还进行了决策分析，表明使用统计模型来确定转诊为前列腺活检将减少约一半的不必要的活检数量，但只遗漏了一小部分癌症，几乎所有这些癌症都是通常被认为构成过度诊断的那种低级别和分期的癌症。我们之前的所有研究都是在欧洲人群中使用在单一研究实验室分析的冷冻存档样本进行的。在这项建议中，我们将首先寻求评估统计模型时，追溯应用于美国队列。然后，我们将测试独立的临床实验室是否可以使用对照样本准确地测量四种激肽释放酶的组合。然后，我们将在预定的活组织检查之前，前瞻性地收集患者的研究血液。该样本将在当地进行实时分析，尽管无论标记结果如何，预定的活组织检查将继续进行，活组织检查结果与统计模型预测的结果进行比较。最后，我们将使用决策分析模拟和Vignette研究来探索模型的实施将如何影响临床实践。

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.