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荟萃分析方法，用于评估形状灵活的癌症簇，称为簇形态分析（CMA）。迄今为止，癌症地理研究的两个主要缺陷是，他们经常假设簇具有特定的形状（例如圆形或椭圆形），并且不使用地理、高危人群、人口统计、协变量和数量来评估统计功效。观察正在调查的癌症病例。该项目克服了这些限制。将使用一套不同大小、相对风险和形状的合理集群对 11 种集群技术进行功效分析。然后根据统计功效和假阳性比例对结果进行排名，其基本原理是基于聚类的癌症监测的目标应该是（1）找到真实的聚类，同时（2）避免错误的聚类。然后，CMA 综合那些具有最佳统计性能的聚类方法的结果。该方法适用于密歇根州的胰腺癌发病率和死亡率，重点关注构成从 1950 年至今持续存在并不断增长的重要集群的三个县。 CMA 相对于仅采用一种形状并仅依赖于一种聚类方法的聚类方法来说是一个重大进步。主要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，以准确识别由癌症风险过高的地理亚群定义的灵活形状的集群。 公共健康相关性：该项目的技术和软件将通过以下方式提供对癌症簇的更简洁和准确的描述：（1）基于灵活形状而不是基于任意形状“模板”（例如圆形和椭圆形）的簇的准确检测； (2) 自动评估聚类技术对软件用户正在审查的特定地理、癌症和亚人群的统计功效； (3) 聚类形态分析，综合聚类方法的结果，以更准确地识别真正的聚类。据我们所知，该项目的技术和软件将是第一个在单一综合框架内解决所有这些因素的技术和软件。