Dynamic, multi-cohort prediction modeling of prostate biopsy outcome

前列腺活检结果的动态、多队列预测模型

• 批准号: 8697359
• 负责人: DONNA Pauler ANKERST
• 金额: $ 54.97万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8697359
• 关键词: African American; Age; American; Benign; Benign Prostatic Hypertrophy; Biopsy; Blood; Canada; Caribbean region; Caucasians; Caucasoid Race; Characteristics; Clinical; Collaborations; Core Biopsy; Counseling; Data; Decision Aid; Development; Early Diagnosis; Europe; European; Family; Germany; Gleason Grade for Prostate Cancer; Grant; Hispanics; Individual; Infection; Informatics; International; Italy; Lead; Malignant Neoplasms; Malignant neoplasm of prostate; Methods; Modeling; North America; Outcome; Patients; Probability; Prostate; Prostate-Specific Antigen; Qualifying; Randomized; Recording of previous events; Research Infrastructure; Research Personnel; Risk; Sample Size; Sampling; Scheme; Screening for Prostate Cancer; Site; Statistical Methods; Statistical Models; Testing; Update; Urinary Retention; Validation; Work; aged; base; cancer risk; cohort; community based practice; data sharing; digital; international center; men; molecular marker; mortality; novel; novel marker; prostatitis; public health relevance; rectal; screening; statistical center; tool

DESCRIPTION (provided by applicant): Prostate specific antigen (PSA) is the only molecular marker routinely used for the early detection of a common cancer, with approximately 75% of men aged 50 years or older having had at least one PSA test. The results of the European Randomized Study of Prostate Cancer, which assessed the value of PSA testing in men who would otherwise not be screened, offered qualified support for PSA testing, showing a modest reduction in prostate cancer mortality. Yet PSA remains an imperfect test. Although PSA is highly specific to the prostate gland, a modestly elevated PSA in blood is not specific to cancer as common benign conditions such as benign prostatic hyperplasia and prostatitis also lead to modest PSA elevations. Accordingly, most men with modestly elevated PSA do not have prostate cancer and it has been estimated that at least 750,000 American men are needlessly subjected to prostate biopsy. Prostate biopsy is not only inconvenient, with 1 in 3 men having to take two or more days from work, but can cause infection, urinary retention or other major complications. To aid the clinical decision as to whether biopsy an individual patient, several investigators have proposed risk prediction models that include information such as PSA level, age, family history and the results of the digital rectal exam (DRE). These models provide a predicted probability of a positive biopsy that can be used to aid patient counseling. However, current prediction models have often been shown to be inaccurate on external validation. One possible reason is that all of the numerous models currently available were developed using a single cohort, a highly problematic approach given important differences between cohorts. In this proposal, we will establish a large, international, multicenter collaboration that will share data on biopsy outcome to allow statistical prediction modeling that incorporates cohort characteristics. The collaboration includes centers in the US, Canada, the Caribbean, Germany and Italy; major academic centers as well as more community-based practices; those seeing predominately Caucasians and those with a racially-diverse group of patients. In total, we will have data on approximately 7000 biopsies per year, over 1000 of which will be in African-Americans or Hispanics. We will establish an informatics infrastructure to allow these centers to send harmonized data on biopsy outcomes to the statistical center. We will then create a prediction model that incorporates both patient-level factors (such as age and PSA) and those at the cohort-level (such as biopsy scheme). The model will be evaluated on an independent validation sample. We will formally test the hypothesis that including multiple cohorts in prediction modeling leads to more accurate models than those generated on single cohorts. As an additional aim, we will update the model as new data become available to evaluate "dynamic" modeling in contrast to traditional static models. We will also evaluate statistical methods for incorporating data on novel markers into existing prediction models.

描述（由申请人提供）：前列腺特异性抗原（PSA）是唯一用于早期检测常见癌症的分子标记物，大约有75％的50岁或以上的男性患有至少一个PSA测试。欧洲前列腺癌随机研究的结果评估了否则不会筛选的男性PSA测试的价值，为PSA测试提供了合格的支持，显示了前列腺癌死亡率的适度降低。然而，PSA仍然是不完善的测试。尽管PSA对前列腺高度具有很高的特异性，但血液中的PSA升高并不针对癌症，因为良性前列腺增生和前列腺炎等常见的良性疾病也导致了适度的PSA升高。因此，大多数PSA升高的男性没有前列腺癌，据估计，至少有75万美国男性受到前列腺活检。前列腺活检不仅是不便的，其中三分之一的人不得不上班时间需要两天或更多的时间，而且会导致感染，保留率或其他重大并发症。为了帮助有关单个患者活检的临床决定，几位研究人员提出了风险预测模型，其中包括PSA水平，年龄，家族史和数字直肠检查结果（DRE）等信息。这些模型提供了阳性活检的预测概率，可用于帮助患者咨询。但是，当前的预测模型经常被证明在外部验证上是不准确的。一个可能的原因是，所有当前可用的众多模型都是使用单个队列开发的，这是一种极为有问题的方法，鉴于同伙之间的重要差异。在此提案中，我们将建立一个大型，国际的多中心协作，该协作将共享有关活检结果的数据，以允许统计统计预测建模，以结合队列特征。该合作包括美国，加拿大，加勒比海，德国和意大利的中心；主要的学术中心以及更多基于社区的实践；那些主要是高加索人和有种族多样性患者的人。总的来说，我们每年将获得大约7000个活检的数据，其中1000多个将在非裔美国人或西班牙裔中。我们将建立一个信息学基础设施，以允许这些中心向统计中心发送统一的活检结果数据。然后，我们将创建一个预测模型，该模型既结合了患者级因素（例如年龄和PSA），又结合了队列级别（例如活检方案）的预测模型。该模型将在独立验证样本上进行评估。我们将正式检验以下假设，即在预测建模中包括多个队列会导致比单一同类群体生成的模型更准确。作为另一个目的，我们将更新模型，因为与传统静态模型相比，新数据可以评估“动态”建模。我们还将评估将新标记数据纳入现有预测模型的统计方法。