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

评估用于世界各地不同人群肺癌筛查的风险预测模型

• 批准号: 10045047
• 负责人: Hilary A. Robbins
• 金额: $ 11.22万
• 依托单位: INTERNATIONAL AGENCY FOR RES ON CANCER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-08 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10045047
• 关键词: Asia; Asians; Australia; Budgets; Calibration; Cancer Etiology; Cessation of life; Country; Data; Databases; Discrimination; Effectiveness; Eligibility Determination; Equilibrium; Ethnic Origin; Ethnic group; Europe; Funding; Hispanics; Individual; International; Knowledge; Lung; Malignant neoplasm of lung; Minority; Minority Groups; Modeling; National Comprehensive Cancer Network; Not Hispanic or Latino; Performance; Population; Population Heterogeneity; Prospective cohort; Race; Research; Resources; Risk; Risk Factors; Smoker; Smoking; Smoking History; Smoking and Health Research; Statistical Data Interpretation; Translating; Work; base; cancer risk; cohort; computed tomography screening; data harmonization; data resource; demographics; evidence base; flexibility; follow-up; low dose computed tomography; lung basal segment; lung cancer screening; model development; mortality; never smoker; portability; predictive modeling; programs; randomized trial; risk prediction model; screening; screening guidelines; screening program; tool; validation studies

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.

项目摘要 通过低剂量计算机断层扫描（CT）筛查可以降低个体的肺癌死亡率 有大量的吸烟史。当资格基于个人风险时，筛查更有效 使用包括人口统计学，吸烟和健康信息在内的模型进行预测。风险的个人 可以提供超过最小阈值筛选。 当前的肺癌风险模型和阈值是在健康中开发和验证的 非西班牙裔白人，美国人口及其在这种情况以外的便携性是未知的。这个证据 差距代表着一个主要且日益严重的关注，因为基于风险的肺部筛查工作正在扩大 在全球范围内迅速没有必要的证据来支持有效的实施。 因此，拟议的项目将（1）在全球21个同伙之间进行协调，以产生独特的数据 肺癌风险的财团数据库； （2）评估遍布肺癌风险模型的性能 美国种族/种族和不同国家； （3）专门为亚洲人建立和验证风险模型；和 （4）计算我们和国际队列中建议的风险阈值的敏感性。 为了实现这些目标，研究团队将（1）利用其现有参与肺癌的参与 队列联盟，NCI队列联盟和亚洲队列联盟。统计分析将 （2）量化美国白人，黑人，西班牙裔，校准（E/O）和歧视（AUC）（AUC） 亚洲人以及欧洲，亚洲和澳大利亚的国家； （3）构建和验证灵活的参数 亚洲人5年肺癌风险的生存模型； （4）计算不断吸烟的比例 肺癌病例将根据拟议的风险阈值筛查符合条件。 这项工作将提供关键的数据，以加速实施循证，风险标准的肺癌 全球筛查计划，从而最大程度地提高肺癌死亡率的降低 通过使用有限资源进行筛选实现。