Evaluating the Feasibility of Lung Cancer Screening in High-Risk Black Women

项目摘要。每年，美国大约有6,800和37,000名黑人妇女死亡， 分别是肺癌和心血管疾病。大多数黑人女性被诊断患有肺癌 和心血管疾病的诊断是在晚期，这与显着较高的 死亡率和更高的医疗保健相关费用。因此，增加肺部感染的早期发现的策略 癌症和心血管疾病的黑人妇女迫切需要。 低剂量计算机断层扫描（LDCT）的肺癌筛查已被证明可以早期发现 在两项大型随机试验中，此外，本发明还 研究表明，LDCT扫描可用于预测心血管疾病风险。因此，LDCT肺 癌症筛查为增加肺癌和心血管疾病的早期发现提供了机会 在黑人女性中。然而，虽然LDCT筛查是由美国预防建议， 服务工作组（USPSTF），用于被归类为高风险的个人，当前USPSTF筛选资格 标准排除了许多实际上是高风险但不符合标准的黑人妇女。在我们以前的研究中 在黑人妇女健康研究（BWHS）的参与者中，我们发现超过66%的黑人妇女 在2021年，被诊断患有肺癌的人将没有资格接受肺癌筛查。 USPSTF指南。 肺癌风险预测模型可以考虑除年龄和吸烟以外的肺癌风险因素（例如， 慢性阻塞性肺病），是一个有希望的战略，以增加高风险的黑人妇女有资格进行筛查的人数。 然而，这种模式在选择高风险黑人妇女进行筛查方面的有效性从来没有被证明过。 前瞻性研究。鉴于LDCT筛查在增加肺部疾病早期发现方面的巨大潜力， 癌症和心血管疾病，研究更有效的方法治疗肺癌 需要在黑人妇女中进行筛查，以提高筛查在这一人群中的效益。 我们的总体目标是对参加研究的美国黑人妇女进行一项单臂（n=400）试点研究。 BWHS是一个由来自美国各地的58,973名自认为是黑人女性组成的队列，为了检查可行性， 可接受性和LDCT筛查在该人群中的表现。我们将招募年龄> 有50年吸烟史。我们的目的是：1）评估LDCT筛查的可行性和可接受性 在美国黑人妇女中; 2）评估LDCT在我们的队列中用于肺癌检测的性能 并将其与美国黑人妇女在国家肺部筛查试验（历史对照）中进行比较，3）验证 深度学习图像分析管道用于我们队列中的心血管疾病检测。 这项试点研究的结果将为进一步评估 最佳的基于风险的筛查策略，以选择高风险的黑人妇女进行肺癌筛查。此外，本发明还提供了一种方法， 这项试点研究的结果将提供初步数据，可用于为未来的大规模研究提供信息 使用深度学习网络来预测LDCT扫描的心血管风险。

PROJECT SUMMARY. Every year, approximately 6,800 and 37,000 Black women in the United States die from lung cancer and cardiovascular diseases, respectively. Most Black women diagnosed with lung cancer and cardiovascular diseases are diagnosed at advanced stages, which are associated with significantly higher mortality and greater healthcare-related costs. As such, strategies to increase the early detection of lung cancer and cardiovascular diseases among Black women are urgently needed. Lung cancer screening with low-dose computed tomography (LDCT) has been shown to result in earlier detection of lung cancer and significant reductions in lung cancer mortality in two large randomized trials. Additionally, research has shown that LDCT scans can be used to predict cardiovascular disease risk. Thus, LDCT lung cancer screening offers an opportunity to increase the early detection of lung cancer and cardiovascular diseases among Black women. However, while LDCT screening is recommended by the United States Preventive Services Task Force (USPSTF) for individuals classified as high-risk, current USPSTF screening eligibility criteria exclude many Black women who are actually high-risk but do not meet the criteria. In our previous study of participants in the Black Women’s Health Study (BWHS), we found that over 66% of Black women diagnosed with lung cancer would have been ineligible for lung cancer screening under the 2021 USPSTF guidelines. Lung cancer risk-prediction models can account for lung cancer risk factors other than age and smoking (e.g., COPD) and are a promising strategy to increase the number of high-risk Black women eligible for screening. However, the effectiveness of such models in selecting high-risk Black women for screening has never been studied prospectively. Given the tremendous potential for LDCT screening to increase the early detection of lung cancer and cardiovascular diseases, studies investigating more effective approaches for lung cancer screening among Black women are needed to enhance the benefits of screening among this population. Our overall objective is to conduct a single-arm (n=400) pilot study of U.S. Black women participating in the BWHS—a cohort of 58,973 self-identified Black women from across the U.S.—to examine the feasibility, acceptability, and performance of LDCT screening among this population. We will recruit Black women aged > 50 years with any history of smoking. We aim to 1) evaluate the feasibility and acceptability of LDCT screening among U.S. Black Women; 2) evaluate the performance of LDCT for lung cancer detection among our cohort and compare it to U.S. Black women in the National Lung Screening Trial (historical control), and 3) validate a deep learning image analysis pipeline for cardiovascular disease detection among our cohort. The findings of this pilot study will provide a strong foundation for a large-scale study that further evaluates the optimal risk-based screening strategies to select high-risk Black women for lung cancer screening. In addition, the findings of this pilot study will provide preliminary data that can be used to inform a future large-scale study using deep learning networks to predict cardiovascular risk on LDCT scans.