Modeling Lung Cancer: Risks, Progression, and Screening

描述（由申请人提供）：我们建议构建一个现实的肺癌风险和进展的统计模型，使其能够将当前肺癌发病率和死亡率的趋势与过去美国人口吸烟的趋势联系起来。我们从现有的方法出发，建立了一个模型，包括易感性的遗传和行为决定因素、疾病从前驱病变到早期局部肿瘤到弥散性疾病的进展、各种方式的检测和医疗干预。以模型估计为基础，我们打算预测初级预防、早期发现和干预方案在不同情景下导致的死亡率降低。这包括利用肺癌易感性的遗传指标来确定高危行为人群（吸烟者）的最高风险亚群。为了考虑各种数据源的不确定性，我们将开发使用仿真和贝叶斯分层建模方法的参数估计技术。随着新方法的发展，我们将把我们的技术应用于各种可用的数据集，这将允许模型的校准和验证。为了调查和开发肺癌易感性，我们将使用加州大学圣地亚哥分校开发的烟草影响评估，以及MD安德森癌症中心流行病学部门保存的肺癌病例对照遗传数据。为了调查肺癌的发病率，我们将使用SEER类型的公共注册数据。对于疾病进展、早期发现和干预，我们将使用NCI肺癌胸部x线筛查研究的数据，以及康奈尔大学威尔医学院最近开展的ELCAP ct扫描筛查研究的数据。为这项工作组建的团队包括莱斯大学、MD安德森癌症中心、康奈尔大学威尔医学院和加州大学圣地亚哥分校的研究人员，他们的专业知识涵盖人口研究、癌症自然历史建模、筛查的影响、贝叶斯技术、遗传流行病学、统计遗传学和吸烟风险分析。该项目使用和产生的数据以及软件将提供给CISNET成员。

DESCRIPTION (provided by applicant): We propose to construct a realistic statistical model of lung cancer risk and progression that will make it possible to relate current trends in lung cancer incidence and mortality to past trends in smoking in the US population. We depart from existing approaches by having the model include genetic and behavioral determinants of susceptibility, progression of the disease from precursor lesions through early localized tumors to disseminated disease, detection by various modalities, and medical intervention. Using model estimates as a foundation, we intend to predict mortality reduction caused by primary prevention, and early-detection and intervention programs, under different scenarios. This includes utilization of genetic indicators of susceptibility to lung cancer to define the highest-risk subgroups of the high-risk behavior population (smokers). To allow for uncertainty in the various sources of data we will develop parameter estimation techniques using simulation and Bayesian hierarchical modeling approaches. Along with developing new methodology, we will apply our techniques to a variety of data sets available to us, which will allow calibration and validation of the model. To investigate and develop lung cancer susceptibility, we will use tobacco impact estimates developed at the University of California at San Diego, as well as case-control genetic data on lung cancer maintained by the Epidemiology Department at MD Anderson Cancer Center. To investigate incidence of lung cancer we will use public registry data of the SEER type. For disease progression, early detection and intervention, we will use data from the NCI lung cancer chest X-ray screening studies, and the recent ELCAP CT-scan screening study developed at Weill Medical College of Cornell University. The team assembled for the proposed work includes researchers at Rice University, MD Anderson Cancer Center, Weill Medical College of Cornell University and University of California at San Diego, whose documented expertise spans population studies, modeling of natural history of cancer, impact of screening, Bayesian techniques, genetic epidemiology, statistical genetics and risks analysis of smoking. Data used and generated by the project, as well as software, will be made available to CISNET members.