A Simulation Modeling Study to Support Personalized Breast Cancer Prevention and Early Detection in High-Risk Women

支持高危女性个性化乳腺癌预防和早期检测的模拟模型研究

基本信息

批准号：
10371141
负责人：
Jeanne Mandelblatt
金额：
$ 7.8万
依托单位：
GEORGETOWN UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-04-01 至 2024-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10371141
关键词：
Address Advocacy Age Aromatase Inhibitors Awareness BRCA mutations Big Data Breast Breast Cancer Early Detection Breast Cancer Prevention Breast Cancer Risk Factor Cancer Control Cancer Intervention and Surveillance Modeling Network Characteristics Clinical Complex Data Data Discovery Disclosure Dissemination and Implementation Early Diagnosis Endometrial Carcinoma Environment Equilibrium Foundations Future Genetic Predisposition to Disease Guidelines Health Health Personnel High Risk Woman Image Individual Intervention Interview Knowledge Laws Lesion Life Link Magnetic Resonance Imaging Mammographic screening Mammography Menopausal Symptom Methods Mission Modeling National Cancer Institute Outcome Patients Pharmaceutical Preparations Prevention Prevention approach Primary Prevention Probability Procedures Research Risk Risk Estimate Risk Factors Stage at Diagnosis Study models Tamoxifen Translating Translational Research Translations Uncertainty Weight Woman base breast cancer diagnosis breast density cancer care cancer genomics cancer prevention cancer therapy care providers clinical encounter clinical practice clinical risk density diverse data experience flexibility follow-up high risk improved informant innovation knowledge base malignant breast neoplasm models and simulation network models novel personalized care personalized strategies polygenic risk score preference programs risk prediction screening shared decision making side effect success supplemental screening tool tumor

项目摘要

Abstract: Personalized care is complex in this unprecedented era of discovery and ‘big data’. The proposed study focuses on the real-world choices facing over 100,000 US women each year who are at higher than average risk of developing breast cancer due to risk factors such as breast density and genetic predisposition. Women at high risk of developing breast cancer are eligible for various breast cancer prevention and early detection options. Current clinical guidelines recommend that these women are offered risk reducing medication, and supplemental imaging with magnetic resonance imaging (MRI) in addition to annual mammography. Each of these choices has a different profile of benefits and harms that will depend on individual risk factors. Annual mammography and MRI can detect tumors early, leading to early diagnosis and improved survival, but have harms related to false positives linked to breast density. Risk-reducing medications reduce the likelihood of developing breast cancer by nearly half, but these medications can induce menopausal symptoms based on age, and in a small percent of women, increase the risk of endometrial cancer or other conditions. Ultimately, a woman’s choice of intervention may depend on how she will weigh harms against benefits for these different options and outcomes given individual risk. To address these complexities, past studies have focused on either on single risk factors, risk prediction tools with selected factors, or screening strategies alone. We propose to use an extant Cancer Intervention and Surveillance Modeling Network (CISNET) simulation model to synthesize data on clinical risk factors and the impact of early detection with screening and primary prevention with risk-reducing medication to provide personalized data that will help identify women who are more likely to benefit from various interventions or combinations of interventions with the least harms. The aims are to: Aim 1: a) Provide data on the benefits (e.g. avoiding breast cancer; early detection and improved survival) and harms (side effects of risk-reducing drugs; false positives with screening) of various combinations of risk reducing medication and screening strategies personalized by individual 5-year risk of developing breast cancer, breast density, and preferences (utility weights) for different experiences; and b) Conduct sensitivity analysis to estimate the effects of uncertainty in model inputs or assumptions on results. Aim 2: Explore the impact of adding PRS to 5-year risk estimates to further personalize information on the balance of benefits and harms of various risk-reducing medication and screening strategies. Aim 3: Conduct key informant interviews with health care providers to guide the future use of model results to support shared decision making. The results of this study will provide novel data to guide personalized care for high-risk women. In future research, these data could be integrated into a conversation aid to facilitate shared decision making during clinical encounters. This study contributes to the National Cancer Institute’s mission to support advances in cancer prevention and control, and use ‘big data’ to enable the translation of research into clinical practice.

抽象的：在这个前所未有的发现和“大数据”时代，个性化护理非常复杂。拟议的研究重点是。每年有超过 100,000 名面临高于平均水平的风险的美国女性面临现实世界的选择由于乳腺密度和遗传倾向等危险因素而患乳腺癌的女性。患有乳腺癌的风险符合各种乳腺癌预防和早期检测方案的资格。当前的临床指南建议为这些女性提供降低风险的药物，并且除了每年一次的乳房X光检查之外，还进行磁共振成像（MRI）的补充成像。这些选择具有不同的益处和危害，具体取决于个人的风险因素。乳房 X 光检查和 MRI 可以及早发现肿瘤，从而实现早期诊断并提高生存率，但与乳房密度相关的误报相关的危害可降低风险降低的可能性。近一半的人患乳腺癌，但这些药物可能会根据年龄诱发更年期症状，在一小部分女性中，子宫内膜癌或其他疾病的风险会增加。女性对干预措施的选择可能取决于她如何权衡这些不同因素的利弊为了解决这些复杂性，过去的研究主要集中于其中一个。单一风险因素、具有选定因素的风险预测工具或单独的筛选策略。我们建议使用现有的癌症干预和监测建模网络（CISNET）模拟模型综合临床危险因素的数据以及通过筛查和初级检测进行早期检测的影响通过降低风险的药物进行预防，提供个性化数据，帮助识别哪些女性更有可能从危害最小的各种干预措施或干预措施组合中受益。目标是：目标 1：a) 提供有关益处的数据（例如避免乳腺癌；及早发现和改善各种组合的生存率）和危害（降低风险药物的副作用；筛查误报）根据个人 5 年乳房发育风险个性化降低风险的药物和筛查策略癌症、乳腺密度和不同经历的偏好（效用权重）；b) 进行敏感性；分析以估计模型输入或假设的不确定性对结果的影响。将 PRS 添加到 5 年风险评估中以进一步个性化有关收益与收益平衡的信息的影响目标 3：进行关键知情人访谈。与医疗保健提供者一起指导未来使用模型结果来支持共同决策。这项研究的结果将为指导未来高危女性的个性化护理提供新的数据。研究中，这些数据可以集成到对话辅助工具中，以促进在这项研究有助于实现国家癌症研究所支持进展的使命。癌症预防和控制，并利用“大数据”将研究转化为临床实践。