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

在社区实践中推进基于风险的公平乳腺癌筛查和监测

• 批准号: 10411220
• 负责人: KARLA M KERLIKOWSKE
• 金额: $ 381.82万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-27 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10411220
• 关键词: Advanced Malignant Neoplasm; Artificial Intelligence; Attention; Biometry; Breast Cancer Detection; Breast Cancer Risk Factor; Breast Cancer Surveillance Consortium; Breast Cancer survivor; Cancer Etiology; Cancer Intervention and Surveillance Modeling Network; Caring; Cessation of life; Clinical; Communication Research; Communities; Community Practice; Data; Data Collection; Decision Making; Digital Breast Tomosynthesis; Early Diagnosis; Education; Effectiveness; Ethnic Origin; Ethnic group; Failure; Frequencies; Funding; Future; General Population; Geography; Goals; Harm Reduction; Health Personnel; High Risk Woman; Housing; Image; Income; Individual; Infrastructure; Intervention; Leadership; Magnetic Resonance Imaging; Malignant Neoplasms; Mammography; Modeling; Neighborhoods; Outcome; Outcome Study; Performance; Play; Policies; Population; Population Heterogeneity; Provider; Public Health; Qualitative Research; Race; Radiology Specialty; Registries; Research; Resources; Risk; Risk Assessment; Risk Factors; Role; Science; Services; Stage at Diagnosis; Structural Racism; System; Technology; Testing; Time; Training; Translations; Underserved Population; United States; United States National Institutes of Health; Woman; advanced breast cancer; algorithmic bias; artificial intelligence algorithm; base; breast imaging; cancer health disparity; cancer risk; clinical risk; comparative effectiveness; data management; evidence base; health care delivery; health disparity; health equity; high risk; imaging facilities; improved; innovation; malignant breast neoplasm; mortality; multidisciplinary; network models; patient oriented; population based; programs; prospective; public health priorities; racial and ethnic; racial and ethnic disparities; radiologist; risk prediction; risk prediction model; screening; social health determinants; sociodemographics; socioeconomics; surveillance imaging; surveillance strategy; tool

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.

乳腺癌仍然是美国妇女癌症死亡的第二大原因，在诊断时的乳腺癌分期、第二次乳腺癌的发病率和死亡率方面存在种族和族裔差异。我们的方案更新遵循的前提是，当所有妇女都能获得高质量的风险评估和乳腺成像，以及当筛查和监测策略针对临床有意义的结果时，筛查和监测将是最有效和最公平的。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）确定一般人群以及种族和民族群体中浸润性乳腺癌最具预测性的临床风险因素;（2）将晚期癌症定义和评估为筛查结果;（3）评估新的筛查技术及其在服务不足人群中的使用;（4）确定影响妇女对基于风险的筛查的看法的多层次因素;（5）确定乳腺癌幸存者中间隔第二乳腺癌的高风险。在下一个资助期内，我们建议进行三个相辅相成的项目，并由三个核心项目提供支援。项目1旨在开发公平的晚期乳腺癌风险模型，其中包括成像特征，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的益处和危害。项目2采用多层次的方法来确定导致乳腺癌筛查表现和结果不公平的妇女、社区和设施层面的因素，并探讨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的高风险妇女（即，间隔第二次乳腺癌），通过人工智能提高监测性能，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。行政核心将为综合计划提供全面的科学领导和管理。生物统计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专业多学科专业知识，沿着三个已建立的癌症干预和监测建模网络（CISNET）建模组，以支持项目结果的临床和政策翻译。该计划利用乳腺癌监测联盟，这是一个成熟的研究网络，具有强大的，基于社区的，前瞻性的数据收集，来自地理和社会人口的不同设置。该计划的研究结果将在公共卫生工作中发挥关键作用，以促进公平，基于风险的筛查和监测，并减少乳腺癌的差异。