Advancing Equitable Risk-based Breast Cancer Screening and Surveillance in Community Practice

乳腺癌仍然是美国妇女癌症死亡的第二大原因，在乳腺癌诊断阶段、第二次乳腺癌发病率和死亡率方面存在种族和民族差异。我们的项目更新遵循的前提是，当所有妇女都能获得高质量的风险评估和乳房成像，当筛查和监测战略以临床有意义的结果为目标时，筛查和监测将是最有效和公平的。我们目前的项目通过以下方式推进了基于风险的筛查和监测科学：(1)确定一般人群和种族和民族群体中最能预测浸润性乳腺癌的临床风险因素；(2)定义和评估晚期癌症作为筛查结果；(3)评估新的筛查技术及其在服务不足人群中的应用；(4)确定影响妇女对基于风险的筛查看法的多层面因素；(5)识别间隔期第二乳腺癌高风险的乳腺癌幸存者。在下一个资助期内，我们建议由三个核心资助的三个互补项目。项目1旨在开发公平的晚期乳腺癌风险模型，包括影像学特征、人工智能（AI）算法和临床因素；并比较基于晚期癌症风险的靶向筛查频率和补充MRI的利弊。项目2采用多层次方法，确定导致乳腺癌筛查绩效和结果不公平的妇女、社区和设施层面因素，并探索有针对性的人工智能使用和其他干预措施是否可以在关注健康公平的情况下改善人口结果。项目3的重点是通过公平地预测监测失败高风险妇女（即第2期乳腺癌），通过人工智能改善监测绩效，并检查健康的社会决定因素，作为监测失败的多层次驱动因素和未来干预措施的目标，改善乳腺癌幸存者的监测成像。行政核心将为一个综合项目提供全面的科学领导和管理。生物统计学和数据管理核心将提供高质量数据收集、管理、分析和共享的集中协调。比较有效性核心将提供决策科学、风险沟通和定性研究方面的多学科专业知识，以及三个已建立的癌症干预和监测建模网络（CISNET）建模小组，以支持项目结果的临床和政策转化。该项目利用了乳腺癌监测联盟，这是一个已建立的研究网络，具有强大的、基于社区的、从地理和社会人口不同环境中收集的前瞻性数据。方案的调查结果将在促进公平、基于风险的筛查和监测以及缩小乳腺癌差异的公共卫生努力中发挥关键作用。

Breast cancer remains the second leading cause of cancer death in United States women, with racial and ethnic disparities in breast cancer stage at diagnosis, rates of second breast cancers, and mortality. Our Program renewal follows the premise that screening and surveillance will be most effective and equitable when all women have access to high-quality risk assessment and breast imaging, and when screening and surveillance strategies are targeted to clinically meaningful outcomes. Our current Program has advanced the science of risk-based screening and surveillance by: (1) identifying clinical risk factors most predictive of invasive breast cancer for the general population and for racial and ethnic groups; (2) defining and evaluating advanced cancer as a screening outcome; (3) assessing new screening technologies and their use in underserved populations; (4) identifying multilevel factors that influence women’s views of risk-based screening; and (5) identifying breast cancer survivors at high risk of an interval second breast cancer. During the next funding period, we propose three complementary Projects supported by three Cores. Project 1 aims to develop equitable advanced breast cancer risk models that incorporate imaging features, artificial intelligence (AI) algorithms, and clinical factors; and compare the benefits and harms of targeted screening frequency and supplemental MRI based on advanced cancer risk. Project 2 takes a multilevel approach to identify woman-, neighborhood-, and facility-level factors that drive inequities in breast cancer screening performance and outcomes, and to explore whether targeted AI use and other interventions can improve population outcomes with attention to health equity. Project 3 focuses on improving surveillance imaging in breast cancer survivors through equitably predicting women at high risk of a surveillance failure (i.e., interval 2nd breast cancer), improving surveillance performance through AI, and examining social determinants of health as multilevel drivers of surveillance failures and targets for future interventions. The Administrative Core will provide overall scientific leadership and administration for an integrated Program. The Biostatistics and Data Management Core will provide centralized coordination of high-quality data collection, management, analysis, and sharing. The Comparative Effectiveness Core will provide specialized multidisciplinary expertise in decision sciences, risk communication, and qualitative research along with three established Cancer Intervention and Surveillance Modeling Network (CISNET) modeling groups to support the clinical and policy translation of Program findings. The Program leverages the Breast Cancer Surveillance Consortium, an established research network with robust, community-based, prospective data collection from geographically and socio-demographically diverse settings. Program findings will play a critical role in public health efforts to promote equitable, risk-based screening and surveillance and reduce breast cancer disparities.