Comparative Modeling: Informing Breast Cancer Control Practice and Policy

比较模型：为乳腺癌控制实践和政策提供信息

• 批准号: 9329292
• 负责人: DONALD A BERRY
• 金额: $ 176.04万
• 依托单位: GEORGETOWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9329292
• 关键词: Address; Adjuvant; Affect; Age; American College of Radiology Imaging Network; Area; Biopsy; Breast; Breast Cancer Surveillance Consortium; Canada; Cancer Control; Cancer Intervention and Surveillance Modeling Network; Cancer Research Network; Caring; Cessation of life; Clinical; Clinical Management; Clinical Trials; Collaborations; Communities; Community Practice; Community of Practice; Data; Decision Aid; Diffuse; Digital Mammography; Disease-Free Survival; ERBB2 gene; Early Diagnosis; Effectiveness; Epidemiologist; Estrogen receptor negative; Estrogen receptor positive; Evaluation; Evolution; Family; Focus Groups; Funding; Future; General Population; Genetic; Genetic Risk; Genomics; Goals; Health Policy; Hormonal; Imaging Techniques; Imaging technology; International; Knowledge; Laboratories; Life; Malignant Neoplasms; Methods; Modeling; Molecular; Natural History; Noninfiltrating Intraductal Carcinoma; Oncologist; Online Systems; Outcome; Paper; Pathologic; Pathologist; Pathway interactions; Patient Preferences; Policies; Population; Population Research; Prevention; Process; Progression-Free Survivals; Public Health; Publications; Quality-Adjusted Life Years; Radiation Oncologist; Radiation-Induced Cancer; Recommendation; Recording of previous events; Recurrence; Recurrent disease; Regimen; Registries; Research; Research Infrastructure; Research Personnel; Resources; Risk; Schedule; Science; Scientist; Structure; Subgroup; Surgeon; Testing; Therapeutic Effect; Training Support; Variant; Wisconsin; Woman; Work; age group; base; burden of illness; cancer genome; cancer health disparity; collaborative environment; comparative; cost; digital; experience; experimental study; genetic association; innovation; malignant breast neoplasm; models and simulation; mortality; next generation; novel; novel strategies; novel therapeutic intervention; oncology; personalized screening; practical application; programs; public health relevance; radiologist; receptor; screening; synergism; targeted treatment; tomosynthesis; tool; training opportunity; trend; tumor; uptake; virtual laboratory; working group

 DESCRIPTION (provided by applicant): The CISNET Breast Working Group (BWG) proposes innovative modeling research focused on challenges in early detection and clinical management that are expected to re-define breast cancer control best practices. The specific aims of this research are to: 1) evaluate the population impact of using polygenic risk to inform screening strategies; 2) assess use of emerging new imaging technologies for population screening; 3) evaluate use of active surveillance for the clinical management of screen-detected DCIS; 4) evaluate the impact of new molecular pathway- and genomic-targeted treatment paradigms in the adjuvant and recurrence settings; and 5) synthesize the methods to quantify the relative contributions of these new paradigms for screening and clinical management on US mortality trends for the general population and risk-stratified subpopulations. These aims encompass four RFA priority areas, and we have set aside funds to address three additional emerging areas (international cancer control planning, cancer disparities, and cancer-specific priority areas [e.g, trials of local recurrence]). Based on unique features, the investigators will work in teams of 3-4 models to address these aims so that the topics can be evaluated efficiently within the project period. This scope of work would not be feasible without the availability of six distinctive BWG models. The BWG models include: Dana Farber (D), Erasmus (E), Georgetown-Einstein (GE), MD Anderson (M), Stanford (S) and Wisconsin-Harvard (W). These modeling teams have been continuously funded for the past 14 years. Their collaboration has been very productive, including publication of 162 research papers, and conduct of modeling to inform public health policy decisions. For this proposal, the BWG will partner with: the Breast Cancer Surveillance Consortium (BCSC); the American College of Radiology Imaging Network (ACRIN); the Genetic Associations and Mechanisms in Oncology (GAME-ON) researchers; the Canadian Partnership Against Cancer; the Evaluation of Genomic Applications in Practice and Prevention (EGAPP); the Athena DCIS registry; the Population-based Research Optimizing Screening though Personalized Regimens (PROSPR) program; the Cancer Research Network (CRN); and international groups. An experienced Coordinating Center provides the infrastructure to support the project goals. This exceedingly strong collaborative environment provides for unprecedented synergy and leveraging of resources to address new research questions that would not be possible outside of this setting. We will deploy novel statistical approaches to jointly estimate DCIS natural history parameters and calculate progression-free survival and survival post-progression. A unique component includes providing model results for use in an extant DCIS clinical management decision aid. Finally, we include formal training opportunities. Overall, this research will advance modeling research and continue to guide breast cancer control policy.

 描述（由申请人提供）：CISNET 乳腺工作组 (BWG) 提出创新模型研究，重点关注早期检测和临床管理方面的挑战，预计将重新定义乳腺癌控制最佳实践。本研究的具体目的是：1）评估使用多基因风险来指导筛查策略对人群的影响； 2) 评估新兴成像技术在人群筛查中的使用情况； 3) 评估主动监测在筛查检测的 DCIS 临床管理中的应用； 4）评估新的分子途径和基因组靶向治疗模式在辅助和复发环境中的影响； 5) 综合方法来量化这些新范式对美国普通人群和风险分层亚人群死亡率趋势的筛查和临床管理的相对贡献。这些目标涵盖四个 RFA 优先领域，并且我们已拨出资金来解决另外三个新兴领域（国际癌症控制规划、癌症差异和癌症特定优先领域 [例如局部复发试验]）。根据独特的特征，调查人员将分成 3-4 人的小组进行工作 模型来解决这些目标，以便可以在项目期间有效地评估主题。如果没有六种独特的 BWG 模型，这一工作范围就不可行。 BWG 模型包括：Dana Farber (D)、Erasmus (E)、Georgetown-Einstein (GE)、MD Anderson (M)、Stanford (S) 和 Wisconsin-Harvard (W)。这些建模团队在过去 14 年里一直得到持续资助。他们的合作非常富有成效，包括发表 162 篇研究论文，并进行建模来为公共卫生政策决策提供信息。对于该提案，BWG 将与以下机构合作： 乳腺癌监测联盟 (BCSC)；美国放射成像网络学院 (ACRIN)；肿瘤学遗传关联和机制 (GAME-ON) 研究人员；加拿大抗癌伙伴关系；基因组在实践和预防中的应用评估（EGAPP）； Athena DCIS 注册表；基于人群的研究通过个性化方案优化筛查 (PROSPR) 计划；癌症研究网络（CRN）；和国际团体。经验丰富的协调中心提供基础设施来支持项目目标。这种极其强大的协作环境提供了前所未有的协同作用和资源利用，以解决在这种环境之外不可能实现的新研究问题。我们将采用新颖的统计方法来共同估计 DCIS 自然史参数并计算无进展生存期和进展后生存期。一个独特的组件包括提供模型结果以用于现有的 DCIS 临床管理决策辅助。最后，我们提供正式的培训机会。总体而言，这项研究将推进模型研究并继续指导乳腺癌控制政策。