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

基本信息

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
项目类别：
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

项目摘要

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（由适用提供）：Cisnet乳房工作组（BWG）提出创新建模研究，重点是早期检测和临床管理中的挑战，这些研究有望重新定义乳腺癌控制最佳实践。这项研究的具体目的是：1）评估使用多基因风险为筛查策略提供信息的人口影响； 2）评估新兴成像技术进行人口筛查的使用； 3）评估使用主动监视对屏幕检测DCI的临床管理的使用； 4）评估在可调和复发设置中新分子途径和基因组靶向治疗范例的影响； 5）合成量化这些新范式筛查和临床管理对美国死亡率趋势的相对贡献的方法和风险分层的亚群。这些目的包括四个RFA优先领域，我们拨出资金来解决其他三个新兴领域（国际癌症控制计划，癌症分布和特定于癌症的优先领域[例如，当地复发试验]）。根据独特的功能，调查人员将在3-4的团队中工作解决这些目标的模型，以便可以在项目期间有效评估主题。如果没有六个独特的BWG模型的可用性，这项工作范围将是不可行的。 BWG模型包括：Dana Farber（D），Erasmus（E），Georgetown-Einstein（GE），MD Anderson（M），Stanford（S）和Wisconsin-Harvard（W）。在过去的14年中，这些建模团队一直在不断资助。他们的合作使我们非常有生产力，包括发表162篇研究论文，以及进行建模以告知公共卫生政策决策。对于此提案，BWG将与：乳腺癌监测财团（BCSC）合作；美国放射学成像网络（ACRIN）；肿瘤学（游戏）研究人员的遗传关联和机制；加拿大反对癌症的伙伴关系；对实践和预防中基因组应用的评估（EGAPP）；雅典娜DCIS注册表；基于人群的研究优化了个性化方案（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了支持项目目标的基础设施。这个极强的协作环境为空前的协同和利用资源提供了解决新的研究问题，这些问题是在这种情况之外不可能的。我们将采用新颖的统计方法来共同估计DCIS自然病史参数，并计算出无进展的生存和生存后的过程。一个独特的组件包括提供模型结果，以用于额外的DCIS临床管理决策援助。最后，我们包括正式的培训机会。总体而言，这项研究将推动建模研究并继续指导乳腺癌控制政策。