Evaluating risk prediction models for use in lung cancer screening in diverse populations around the world

项目摘要 通过低剂量计算机断层扫描（CT）进行筛查可降低个体肺癌死亡率 有严重吸烟史当资格是基于个人风险时，筛查更有效， 使用包括人口统计、吸烟和健康信息的模型进行预测。个人风险 超过最低限度的人可以进行筛查。 目前的肺癌风险模型和阈值是在健康人群中开发和验证的， 非西班牙裔白色美国人群，其在该背景之外的可移植性未知。这一证据 由于基于风险的肺部筛查工作正在扩大， 在没有必要证据支持有效实施的情况下， 因此，拟议项目将（1）协调全球21个队列的数据， （2）评估肺癌风险模型的性能， 美国种族/民族和不同国家;（3）建立并验证专门针对亚洲人的风险模型;以及 (4)计算美国和国际队列中拟议风险阈值的敏感性。 为达致这些目标，研究小组将（1）善用其现时在肺癌方面的工作， 队列联盟、NCI队列联盟和亚洲队列联盟。统计分析将 (2)美国白人、黑人、西班牙裔、 和亚洲人，以及欧洲，亚洲和澳大利亚的国家;（3）建立和验证灵活的参数 亚洲人5年肺癌风险的生存模型;（4）计算曾经吸烟的比例 根据建议的风险阈值，肺癌病例将符合筛查条件。 这项工作将提供关键数据，以加速实施基于证据的，风险定制的肺癌 筛查计划，从而最大限度地降低肺癌死亡率， 通过有限资源筛选实现。

PROJECT SUMMARY Screening by low-dose computed tomography (CT) can reduce lung cancer mortality among individuals with a heavy smoking history. Screening is more efficient when eligibility is based on individual risk, predicted using a model including demographics, smoking, and health information. Individuals whose risk exceeds a minimum threshold can be offered screening. Current lung cancer risk models and thresholds were developed and validated in healthy, largely non-Hispanic white, US populations, and their portability outside that context is unknown. This evidence gap represents a major and growing concern, because risk-based lung screening efforts are expanding rapidly worldwide without the needed evidence to support effective implementation. Therefore, the proposed project will (1) harmonize data across 21 cohorts worldwide to produce a unique consortium database on lung cancer risk; (2) evaluate the performance of lung cancer risk models across US races/ethnicities and different countries; (3) build and validate a risk model specifically for Asians; and (4) calculate the sensitivity of proposed risk thresholds across US and international cohorts. To achieve these aims, the research team will (1) capitalize on its existing involvement in the Lung Cancer Cohort Consortium, NCI Cohort Consortium, and Asia Cohort Consortium. Statistical analyses will (2) quantify calibration (E/O) and discrimination (AUC) of risk models among US whites, blacks, Hispanics, and Asians, and in countries across Europe, Asia, and Australia; (3) build and validate a flexible parametric survival model for 5-year lung cancer risk among Asians; and (4) calculate the proportion of ever-smoking lung cancer cases that would be screening eligible based on proposed risk thresholds. This work will provide critical data to accelerate implementation of evidence-based, risk-tailored lung cancer screening programs worldwide, thus maximizing the reductions in lung cancer mortality that can be achieved through screening with finite resources.