权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Breast Cancer Trend Analysis Using Stochastic Stimulati*

使用随机刺激进行乳腺癌趋势分析*

基本信息

批准号：
7683773
负责人：
SYLVIA KATINA PLEVRITIS
金额：
$ 7.71万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2000
资助国家：
美国
起止时间：
2000-09-01 至 2011-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7683773
关键词：
Address Adopted Advocate Age Agreement Award BRCA1 gene BRCA2 gene Breast Cancer Control Cancer Intervention and Surveillance Modeling Network Collaborations Data Detection Disease Disease Progression Documentation Early Diagnosis Family history of Female Future General Population Genetic Predisposition to Disease Genetic Risk Goals Healthy People 2010 Incidence Intervention Knowledge Magnetic Resonance Imaging Malignant Neoplasms Mammography Medical Modality Modeling Modification Mutation Natural History Noninfiltrating Intraductal Carcinoma Performance Phase Pilot Projects Policy Maker Population Protocols documentation Recurrence Relative (related person)Research Risk Running Screening procedure Simulate Source Code Specific qualifier value Surrogate Markers Technology Translating Treatment Protocols United States Work attributable mortality base chemotherapy high risk innovation malignant breast neoplasm member models and simulation mortality network models programs treatment trial trend web interface

项目摘要

The main goal of our proposed research is to quantify the impact of screeningand treatment interventionson breast cancer incidenceand mortality trends in the United States through a collaborative research agreement with the NCIs Cancer Intervention and Surveillance Modeling Network (CISNET). Under our original CISNET award, we quantified the relativecontributionsof screening mammography and multiagent chemotherapy to the recent decline in breast cancer mortality. In this application, we are proposing to extend our analysis of the current breast cancer trends to include the impact of screen-detected DCIS. We will also identify the component of current trends in breast cancer incidence and mortality attributableto the subpopulation at high genetic risk for developingthe disease. In additionto studying the current trends more closely, we will extend the use of our model to the study of future trends. Through a CISNET/DHHS supplemental award, we have already performed a pilot study on the use of our model in determining whether or not the Healthy People 2010 goals in breast cancer mortalitycould be achieved. This project revealed the need to enhance our existing CISNET model so that it could take as inputs intermediate endpointsfrom screening and treatment trials. More often than not the performance of medical innovations are being evaluated on short term endpoints or surrogate markers. New treatment protocols are now being broadly adopted on the basis of lowered recurrence rates, with little knowledge of their impact on survival. New screening technologies are being advocated on the basis of increased detection rates, with little knowledgeof their impact on survival. We want to extrapolate the intermediate endpoints of breast cancer trials in screening and treatment to long term survival endpoints and then translate these findings to the population level. We will focus a part of our efforts on the study of new screening technologies in the high risk population in order to better understand how these interventions can be translated to the general population.We will make our CISNET model availablefor broader publicconsumption and welcome pressing questions from policy makers during the course of our study.

我们提出的研究的主要目标是量化筛查和治疗干预对乳房的影响。通过与NCIS的合作研究协议，美国的癌症发病率和死亡率趋势癌症干预和监测建模网络(CISNET)。在我们最初的CISNet奖项下，我们量化了筛查乳房X光和多药化疗与近期乳房萎缩的关系癌症死亡率。在这个应用程序中，我们建议将我们对当前乳腺癌趋势的分析扩展到包括屏幕检测到的DCIS的影响。我们还将确定乳腺癌当前趋势的组成部分发病率和死亡率归因于发生该病的遗传风险较高的亚群。除了…之外更密切地研究当前的趋势，我们将把我们的模型的使用扩展到对未来趋势的研究。通过一个我们已经进行了一项试点研究，使用我们的模型确定无论健康人群2010年的乳腺癌死亡率目标能否实现。这个项目揭示了需要增强我们现有的CISNET模型，以便它可以将筛选和治疗试验。医疗创新的绩效往往是短期评估的终结点或代理标记。新的治疗方案现在被广泛采用，其基础是降低复发率，对其对生存的影响知之甚少。正在倡导新的筛查技术发现率上升的基础是，对其对生存的影响知之甚少。我们想要推断出乳腺癌筛查和治疗试验的中间终点到长期生存终点，然后将这些发现转化为人口水平。我们将把一部分精力集中在新筛查的研究上高危人群中的技术，以便更好地了解如何将这些干预措施转化为大众。我们将使我们的CISNET模型可供更广泛的公众消费，并欢迎按在我们研究过程中来自政策制定者的问题。