Cancer Cluster Morphology

癌簇形态学

• 批准号: 7600303
• 负责人: Geoffrey M. Jacquez
• 金额: $ 37.54万
• 依托单位: BIOMEDWARE
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-14 至 2010-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7600303
• 关键词: Accounting; Address; Area; Arts; Atlas of Cancer Mortality in the United States; Biological Markers; Businesses; Cancer Burden; Cancer Cluster; Cancer Control; Cancer Etiology; Case Study; Censuses; Characteristics; Classification; Cluster Analysis; Complex; Computer software; Count; County; Data; Demography; Detection; Economics; Educational process of instructing; Environment; Environmental Risk Factor; Epidemiologic Studies; Epidemiology; Evaluation; Gender; Genetic Research; Geographic Information Systems; Geography; Health; Imagery; Incidence; Information Systems; Intelligence; Investigation; Knowledge; Lead; Life; Literature; Location; Malignant Neoplasms; Malignant neoplasm of pancreas; Maps; Medical Surveillance; Meta-Analysis; Methodology; Methods; Michigan; Modeling; Morphology; National Cancer Institute; Numbers; Outcome; Paper; Pattern; Peer Review; Performance; Phase; Population; Population Distributions; Population Study; Populations at Risk; Preparation; Principal Investigator; Probability; Public Health; Publications; Race; Randomized; Registries; Relative (related person); Relative Risks; Reporting; Research; Research Personnel; Resolution; Risk; Risk Factors; Scanning; Sensitivity and Specificity; Shapes; Simulate; Small Business Funding Mechanisms; Small Business Innovation Research Grant; Software Tools; Specific qualifier value; System; Techniques; Technology; Testing; Thinking; Time; Uncertainty; United States National Institutes of Health; Universities; Use of New Techniques; Variant; Waxes; Work; analytical tool; animation; base; cancer risk; commercial application; commercialization; community based participatory research; data modeling; data structure; day; demographics; design; falls; improved; innovation; lectures; male; method development; metropolitan; mortality; next generation; novel; novel strategies; programs; prototype; rapid growth; simulation; size; social; software development; software systems; statistics; symposium; technological innovation; tool

DESCRIPTION (provided by applicant): This project will develop a new, meta-analytic approach for evaluating cancer clusters of flexible shape called Cluster Morphology Analysis (CMA). To date, two of the major deficiencies of geographic studies of cancer are that they often assume clusters have a specific shape (e.g. circle or ellipse) and do not evaluate statistical power using the geography, at-risk population, demographics, covariates and numbers of observed cases of the cancer under investigation. These limitations are overcome by this project. Power analyses will be conducted for 11 clustering techniques using a suite of plausible clusters of different sizes, relative risks and shapes. The results are then ranked by statistical power and by the proportion of false positives, under the rationale that the objective of cluster-based cancer surveillance should be to (1) find true clusters while (2) avoiding false clusters. CMA then synthesizes the results of those clustering methods found to have the best statistical performance. This approach is applied to pancreatic cancer incidence and mortality in Michigan, focusing on three counties that comprise a significant cluster that persists and grows from 1950 to the present day. CMA is a significant advance over clustering approaches that assume just one shape and rely on only one clustering method. The major innovation is the creation of methods and software for analyzing cancer incidence and mortality data to accurately identify flexibly shaped clusters defined by geographic sub-population of excess cancer risk. PUBLIC HEALTH RELEVANCE: The techniques and software from this project will provide a more concise and accurate description of cancer clusters via (1) the accurate detection of clusters founded on flexible shapes, rather than on arbitrary shape "templates" such as circles and ellipses; (2) the automated evaluation of the statistical power of clustering techniques for the specific geography, cancer and sub-population being scrutinized by the software user; and (3) Cluster Morphology Analysis that synthesizes results across clustering approaches to more accurately identify true clusters. To our knowledge the techniques and software from this project will be the first to address all of these factors within a single, comprehensive framework.

描述（由申请人提供）：该项目将开发一种新的，元分析的方法，用于评估称为群集形态分析（CMA）的柔性形状的癌症簇。迄今为止，癌症地理研究的两个主要缺陷是，他们通常认为簇具有特定形状（例如圆圈或椭圆形），并且没有使用地理位置，高危人群，人口统计学，协变量和观察到的癌症病例的数量来评估统计能力。该项目克服了这些限制。将使用一套不同尺寸，相对风险和形状的合理簇进行11种聚类技术的功率分析。然后，根据统计能力和假阳性的比例对结果进行排名，这是基于集群的癌症监测的目的应为（1）找到真正的簇，而（2）避免使用假簇。然后，CMA综合了这些聚类方法的结果，发现具有最佳的统计性能。这种方法应用于密歇根州的胰腺癌的发病率和死亡率，重点是三个县，其中包括一个持续并从1950年到今天的重要集群。 CMA是仅采用一种形状并仅依靠一种聚类方法的聚类方法的重大进步。主要的创新是创建用于分析癌症发病率和死亡率数据的方法和软件，以准确识别由多余癌症风险的地理亚群来定义的灵活形状的簇。 公共卫生相关性：该项目的技术和软件将通过（1）（1）对柔性形状上建立的群集的准确检测，而不是在任意形状的“模板”（例如圆圈和椭圆机上），通过（1）准确地检测到癌症簇的技术和软件； （2）软件用户对聚类技术的统计能力进行自动评估； （3）群集形态分析，综合了跨聚类方法的结果，以更准确地识别真正的簇。据我们所知，该项目的技术和软件将是第一个在一个全面的框架内解决所有这些因素的技术。