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

摘要： 在这个前所未有的发现和大数据时代，个性化护理是复杂的。拟议的研究重点是 关于每年超过10万名美国女性面临的现实世界选择，她们面临的风险高于平均水平 由于乳房密度和遗传易感性等危险因素而患上乳腺癌。女人在高处 患乳腺癌的风险符合各种乳腺癌预防和早期发现的选择。 目前的临床指南建议向这些妇女提供降低风险的药物，以及 除年度乳房X光检查外，还需补充磁共振成像(MRI)。每一个 这些选择有不同的益处和危害，这将取决于个人的风险因素。每年一次 乳房X光检查和核磁共振成像可以早期发现肿瘤，导致早期诊断和提高存活率，但 与乳房密度相关的假阳性相关的危害。降低风险的药物降低了发生 患乳腺癌的几率接近一半，但这些药物会根据年龄引发更年期症状， 而在一小部分女性中，会增加患子宫内膜癌或其他疾病的风险。最终，一个 女性的干预选择可能取决于她将如何权衡这些不同 在考虑到个人风险的情况下，选择和结果。为了解决这些复杂性，过去的研究集中在 关于单一风险因素、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视建模网络(CISNET)模拟 综合临床危险因素和早期检测与筛查和初诊影响的数据的模型 通过降低风险的药物进行预防，以提供个性化数据，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。这个 目的是：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 存活率)和危害(降低风险药物的副作用；筛查假阳性) 根据个体5年乳房发育风险个体化的降低风险药物治疗和筛查策略 癌症、乳房密度和对不同体验的偏好(效用权重)；以及b)进行敏感性 评估模型输入或假设中的不确定性对结果的影响的分析。目标2：探索 在5年风险估计中加入PR以进一步个性化利益平衡信息和 各种降低风险的药物和筛查策略的危害。目标3：进行关键线人面谈 与卫生保健提供者一起指导未来使用模型结果来支持共享决策。 这项研究的结果将为指导高危妇女的个性化护理提供新的数据。在未来 研究，这些数据可以集成到对话辅助工具中，以便于在 临床上的接触。这项研究为国家癌症研究所的使命做出了贡献，以支持在 癌症预防和控制，并使用“大数据”将研究转化为临床实践。

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.