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.

项目概要-项目3 幸存者中第二次乳腺癌的发病率比无此病史的女性中第一次乳腺癌的发病率高7倍;这些癌症与发病率和乳腺癌特异性死亡率的增加有关。建议对幸存者进行年度监测乳房X线检查，以便早期发现第二次乳腺癌，以降低发病率和死亡率。然而，监测乳房X线摄影是不完美的。幸存者的监测失败率（在阴性乳房X光检查的12个月内诊断出癌症）比没有乳腺癌病史的女性高4倍。项目3的总体目标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（AI）改善癌症检测，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。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的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。外部验证也是确保模型通用性的关键。此外，未来转诊医生和女性基于风险的监测策略的可接受性值得考虑，但尚未进行评估。在当前的P01中，我们开发了第一个可应用于个体幸存者的监测失败风险模型。在下一个资助周期，我们的目标1将解决采用BCSC 5年监督故障风险预测模型的可行性，通过种族群体外部概括性和可接受性评估性能公平性。我们的目标2将评估AI算法是否在与BCSC放射科医生相同的固定特异性下提高灵敏度。用于乳房X线摄影解释的商用AI算法已经在实施中。然而，这些算法是在筛选人群中开发的，尚未在幸存者中得到验证。最后，我们的目标3将确定选定的多层次的健康的社会决定因素，有助于监测失败的相对贡献。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，与第二次乳腺癌和死亡率的高发病率有关。没有先前的研究已经评估了多层次的社会健康决定因素，可能是适合干预与间隔第二乳腺癌的幸存者。了解多层次因素对监测失败的相对贡献，可以用来指导未来有针对性的干预措施，以减少监测失败。总之，该项目将产生证据，通过多管齐下的努力，以减少在风险分层和成像解释点的监测失败，并作为监测失败的多层次驱动因素和未来干预措施的目标，健康的关键社会决定因素的全面检查，以改善监测。