Addressing racial disparities in lung cancer screening

解决肺癌筛查中的种族差异

基本信息

批准号：
10210043
负责人：
Melinda Aldrich
金额：
$ 65.86万
依托单位：
VANDERBILT UNIVERSITY MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-04-05 至 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10210043
关键词：
Accounting Address Advisory Committees African American Age Algorithms Biometry Calibration Cessation of life Clinical Clinical Data Cohort Studies Communities Data Decision Aid Development Diagnostic Eligibility Determination Etiology Failure Focus Groups Gold Guidelines Individual Knowledge Lead Life Lung Machine Learning Malignant Neoplasms Malignant neoplasm of lung Measures Mission Modeling National Cancer Institute National Health Interview Survey Natural History Online Systems Patients Population Precision Health Preventive service Property Prospective cohort Provider Public Health Informatics Publishing Race Research Risk Risk Factors Rural Screening for cancer Smoking Smoking Behavior Smoking History Statistical Methods Statistical Models Surveys Techniques Testing Translating United States Validation Variant Visit Woman Women&apos s Health Work age group base black women cancer epidemiology cancer risk clinical implementation cohort community engagement comorbidity data harmonization design experience health disparity high risk improved innovation low socioeconomic status lung cancer screening machine learning algorithm mortality multi-ethnic multidisciplinary novel patient oriented personalized predictions personalized risk prediction population based predictive modeling prospective racial difference racial disparity racial diversity real world application risk prediction model rural area screening screening guidelines screening program shared decision making simulation statistical and machine learning study population tool urban area web-based tool

项目摘要

PROJECT SUMMARY/ABSTRACT Screening promotes early detection of cancer to decrease mortality. Unfortunately, significant racial disparities exist in lung cancer screening. Recently published findings by our team show that under current national screening guidelines African Americans have reduced eligibility for lung cancer screening compared to whites. These screening guidelines are based on a combination of age and smoking pack-year criteria derived from a national lung screening trial that was primarily (91%) white. Importantly, smoking behaviors and baseline risks for lung cancer differ greatly between African Americans and whites. Because of this, a risk-based screening strategy may provide a more equitable assessment of eligibility than current screening guidelines. However, the development of personalized risk prediction models for lung cancer in African Americans has been limited. To address this gap and to improve equity in screening eligibility, we propose building a personalized prediction tool using the combined data from three large-scale population-based prospective cohorts with substantial African American representation. The combined cohorts have over 336,000 individuals (44% African American) and 9,132 incident lung cancer cases from across the United States. We propose the following three aims: 1) construct a well-calibrated natural-history model of lung cancer risk for screening in African Americans, 2) evaluate lung cancer screening strategies by simulation and identify sub-populations who would benefit from screening, accounting for comorbidities and false-positives, and 3) develop a web-based decision aid for screening that is culturally appropriate. We will employ innovative machine learning techniques and state-of-the- art statistical methods to build a well-calibrated lung cancer prediction model for African Americans. Careful examination will identify sub-populations (such as women, low socioeconomic status, rural, age groups, etc.) that will benefit from screening. A key innovative aspect of this proposal is its community-engaged approach and partnership with a Community Advisory Board, both of which will help translate our empirical findings into the design of a patient-oriented decision aid. This project is relevant to the mission of the National Cancer Institute since it focuses on establishing equity in lung cancer screening eligibility. Our findings will have sustained impact on precision health and motivate improved clinical strategies for the early detection of lung cancer for African Americans.

项目概要/摘要筛查促进癌症的早期发现，从而降低死亡率。不幸的是，种族差异显着存在于肺癌筛查中。我们团队最近发表的研究结果表明，在当前的国家筛查指南与白人相比，非裔美国人接受肺癌筛查的资格较低。这些筛查指南基于年龄和吸烟年数标准的结合，这些标准源自一项研究全国肺部筛查试验主要是（91%）白人。重要的是，吸烟行为和基线风险非裔美国人和白人之间的肺癌差异很大。因此，基于风险的筛查策略可能会比当前的筛选指南提供更公平的资格评估。然而，非裔美国人肺癌个性化风险预测模型的开发受到限制。到为了解决这一差距并提高筛选资格的公平性，我们建议构建个性化预测工具使用来自三个大规模人口前瞻性队列的综合数据，其中有大量非洲人美国代表。合并后的队列有超过 336,000 人（44% 为非裔美国人）来自美国各地的 9,132 例肺癌病例。我们提出以下三个目标：1）构建一个经过良好校准的肺癌风险自然史模型，用于非裔美国人的筛查，2) 通过模拟评估肺癌筛查策略并确定将从中受益的亚人群筛查、考虑合并症和假阳性，以及 3) 开发基于网络的决策辅助工具适合文化的筛查。我们将采用创新的机器学习技术和最先进的技术艺术统计方法为非裔美国人建立一个经过良好校准的肺癌预测模型。小心检查将确定亚人群（例如妇女、社会经济地位低下、农村、年龄组等）这将从筛查中受益。该提案的一个关键创新方面是其社区参与的方法和与社区咨询委员会合作，两者都将有助于将我们的实证研究结果转化为设计以患者为导向的决策辅助工具。该项目与国家癌症研究所的使命相关因为它的重点是建立肺癌筛查资格的公平性。我们的研究结果将产生持续的影响促进非洲肺癌早期检测的精准健康并改进临床策略美国人。