Project 3

项目3

• 批准号: 10411223
• 负责人: Janie M Lee
• 金额: $ 21.32万
• 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/10411223
• 关键词: Address; Adopted; Adoption; Affect; Age; Algorithms; Artificial Intelligence; Asian Pacific Islander; Black race; Breast; Breast Cancer Detection; Breast Cancer Surveillance Consortium; Breast Cancer Treatment; Breast Cancer survivor; Clinical; Community Practice; Data; Early Diagnosis; Education; Ensure; Ethnic Origin; Ethnic group; Event; Failure; Funding; Future; Goals; High Risk Woman; Hispanic; Housing; Image; Image Analysis; Incidence; Income; Individual; Intervention; Malignant Neoplasms; Mammary Gland Parenchyma; Mammographic screening; Mammography; Measures; Meta-Analysis; Modeling; Morbidity - disease rate; Neighborhoods; Observational Study; Outcome; Performance; Physicians; Policies; Population; Quality of life; Race; Recording of previous events; Recurrence; Registries; Resources; Risk; Specificity; Surveillance Modeling; Survivors; Technology; Testing; Time; Training; Validation; Woman; Work; artificial intelligence algorithm; base; breast cancer diagnosis; cancer diagnosis; deep learning; head-to-head comparison; health equity; high risk; improved; malignant breast neoplasm; mortality; primary endpoint; programs; quantitative imaging; radiologist; risk prediction; risk prediction model; risk stratification; screening; secondary endpoint; social health determinants; surveillance imaging; surveillance strategy; tool

PROJECT SUMMARY – Project 3 The incidence of second breast cancer in survivors is seven times higher than first breast cancer incidence in women without this history; these cancers are associated with increased morbidity and breast-cancer specific mortality. Annual surveillance mammography is recommended for survivors for early detection of a second breast cancer to reduce morbidity and mortality. However, surveillance mammography is imperfect. Survivors have 4x higher surveillance failure rates (cancers diagnosed within 12 months of a negative mammogram) than women without a history of breast cancer. Project 3’s overarching goal is to reduce surveillance failures in breast cancer survivors through equitably predicting women at high risk of a surveillance failure (i.e., interval 2nd breast cancer), improving cancer detection through artificial intelligence (AI), and examining social determinants of health as multilevel drivers of surveillance failures and targets for future interventions. Having an accurate surveillance failure risk prediction tool across broad populations of survivors would support a future of targeted surveillance imaging. Understanding whether predictive performance differs by race is critical to validating an accurate risk prediction model. External validation is also key to ensuring the generalizability of the model. Additionally, acceptability of future risk-based surveillance strategies for referring physicians and women is important to consider and has not yet been evaluated. In the current P01, we developed the first risk model of surveillance failures that can be applied to individual survivors. In the next funding cycle, our Aim 1 will address feasibility of adopting the BCSC 5-year surveillance failure risk prediction model by assessing performance equity by race group external generalizability, and acceptability. Our Aim 2 will evaluate whether AI algorithms improve sensitivity at a fixed specificity equivalent to that of BCSC radiologists. Commercially available AI algorithms for mammography interpretation are already being implemented. However, these algorithms were developed in screening populations and have not yet been validated in survivors. Lastly, our Aim 3 will identify the relative contribution of selected multilevel social determinants of health that contribute to surveillance failures. Multiple studies have shown social determinants of health including income, education, housing, and race are associated with higher rates of second breast cancers and mortality. No prior study has evaluated how multilevel social determinants of health that may be amenable to intervention are associated with interval second breast cancers in survivors. Understanding the relative contribution of multilevel factors to surveillance failures can be used to guide future targeted interventions to decrease surveillance failures. Together, this Project will generate evidence to improve surveillance through a multipronged effort to reduce surveillance failures at the points of risk stratification and imaging interpretation, and an overall examination of key social determinants of health as multilevel drivers of surveillance failure and targets for future interventions.

项目摘要 – 项目 3 对于没有这种病史的女性，幸存者中第二次乳腺癌的发病率比第一次乳腺癌的发病率高七倍；这些癌症与发病率和乳腺癌特异性死亡率增加有关，建议幸存者每年进行乳房X线检查以早期发现第二次乳腺癌。然而，监测乳房 X 光检查并不完善，幸存者的监测失败率（乳房 X 光检查呈阴性后 12 个月内诊断出癌症）比没有乳腺癌病史的女性高 4 倍。总体目标是通过公平地预测监测失败的高风险女性（即第二期乳腺癌）、通过人工智能 (AI) 改善癌症检测以及将健康的社会决定因素作为多层次驱动因素进行检查，从而减少乳腺癌幸存者的监测失败监测失败和未来干预的目标。在广泛的幸存者群体中拥有准确的监测失败风险预测工具将支持未来有针对性的监测成像，了解预测性能是否因种族而异对于验证准确的风险预测模型至关重要。是此外，未来基于风险的监测策略对于转诊医生和女性的可接受性也很重要，但尚未在当前的 P01 中进行评估，我们开发了第一个监测失败的风险模型。在下一个资助周期中，我们的目标 1 将通过评估种族群体外部普遍性的表现公平性来解决采用 BCSC 5 年监测失败风险预测模型的可行性，我们的目标 2 将评估可接受性。是否有AI算法提高与 BCSC 放射科医生的固定特异性相当的灵敏度。用于乳房 X 光检查的商用人工智能算法已经在实施，但是，这些算法是在筛查人群中开发的，尚未在幸存者中得到验证。多项研究表明，健康的社会决定因素（包括收入、教育、住房和种族）与较高的第二乳腺癌发生率和死亡率相关。多层次可能适合干预的健康社会决定因素与幸存者的间期乳腺癌有关。了解多层次因素对监测失败的相对影响可用于指导未来的有针对性的干预措施，以减少监测失败。通过多管齐下的努力来改善监测，减少风险分层和影像解读方面的监测失败，并全面检查健康的关键社会决定因素，将其作为监测失败的多层次驱动因素和未来干预措施的目标。