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

• 批准号: 10201836
• 负责人: Jinani Jayasekera
• 金额: $ 7.8万
• 依托单位: GEORGETOWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10201836
• 关键词: Address; Advocacy; Age; Aromatase Inhibitors; Awareness; BRCA mutations; Big Data; Breast; Breast Cancer Early Detection; Breast Cancer Prevention; 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; simulation; success; supplemental screening; tool; tumor

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： 在这个空前的发现和“大数据”时代，个性化护理很复杂。拟议的研究重点 每年面临超过100,000名美国妇女的现实选择，她们的平均风险高于平均风险 由于乳房密度和遗传易感性等危险因素而发展的乳腺癌。女性高 患乳腺癌的风险有资格获得各种乳腺癌预防和早期检测选择。 当前的临床指南建议这些妇女被提供降低药物的风险，并且 除年度乳房X线摄影外，还具有磁共振成像（MRI）的补充成像。每个 这些选择具有不同的福利和危害概况，这将取决于个人风险因素。年度的 乳房X线摄影和MRI可以早日检测肿瘤，导致早期诊断并提高生存率，但具有 与乳房密度相关的假阳性有关的危害。降低风险的药物减少了 将乳腺癌增加几乎一半，但是这些药物可以根据年龄诱导更年期症状， 在一小部分女性中，增加了子宫内膜癌或其他疾病的风险。最终，一个 女人选择干预可能取决于她将如何加重这些不同的利益 选择和结果给予个人风险。为了解决这些复杂性，过去的研究重点是 关于单个风险因素，具有选定因素的风险预测工具或仅筛选策略。 我们建议使用外癌的癌症干预和监视建模网络（CISNET）模拟 模型以合成有关临床风险因素的数据以及通过筛查和原发性检测的影响 通过降低风险药物的预防，提供个性化数据，以帮助识别妇女 更有可能受益于各种干预措施或干预措施和最小损害的组合。这 目的是：目标1：a）提供有关益处的数据（例如避免乳腺癌；早期发现并改善 各种组合的生存）和危害（降低风险药物的副作用；带筛查的假阳性） 降低药物和筛查策略的风险，该策略是由个人5年风险的个性化的乳房风险 癌症，乳房密度和偏好（效用重量），用于不同的体验； b）传导灵敏度 分析以估计模型输入或假设对结果的不确定性影响。目标2：探索 将PRS添加到5年风险估算中的影响，以进一步个性化有关利益平衡和 危害各种降低风险的药物和筛查策略。目标3：进行关键的线人访谈 随着医疗保健提供者指导未来使用模型结果来支持共享决策。 这项研究的结果将提供新的数据，以指导高风险女性的个性化护理。将来 研究，可以将这些数据集成到对话援助中，以促进在 临床相遇。这项研究有助于国家癌症研究所的使命支持进步 预防和控制癌症，并使用“大数据”来将研究转化为临床实践。