课题基金 / 基金详情

Esophageal Cancer from Cells to Population: A Multiscale Approach

Esophageal Cancer from Cells to Population: A Multiscale Approach
从细胞到群体的食管癌:多尺度方法
批准号:
8919740
负责人:
Chin Hur
金额:
$66.54万
依托单位国家:
美国
项目类别:
财政年份:
2013
资助国家:
美国
项目状态:
已结题
起止时间:
2013-09-17 至 2016-08-31

项目摘要

项目成果

Chin Hur的其他基金

相似基金

相关文献

中文摘要
翻译
点击翻译按钮获取中文摘要
英文摘要
DESCRIPTION (provided by applicant): Esophageal Cancer from Cells to Population: A Multiscale Approach The goal of the proposed research is to reduce the burden of esophageal adenocarcinoma (EAC) by optimizing surveillance of patients with Barrett's esophagus (BE) using cutting-edge endoscopic imaging and advanced epigenetic profiling of neoplastic tissues in combination with standard endoscopic techniques. To accomplish this goal we will establish a multidisciplinary collaboration between cancer biologists, epidemiologists, clinicians and computational and mathematical modelers. This research team will develop a multiscale modeling framework that synthesizes and integrates data generated from diverse sources and at different scales to provide a coherent and informative portrayal of the natural history of EAC. Simulation models of EAC actively supported by the NCI's CISNET (U01 CA152926) and data from the Barrett's Esophagus Translational Research Network (BETRNet, U54 CA163060) will serve as the foundation for a new biologically-motivated Multiscale Esophageal Adenocarcinoma Model (MEMo). This new model will be informed by data that span numerous scales including: molecular level DNA methylation data, cellular level volumetric laser endomicroscopy (VLE) data, patient level endoscopic surveillance data, and population level cancer registry SEER data. We will use MEMo as an analytic tool to assess the clinical effectiveness of BE surveillance protocols for early esophageal neoplasia detection and prevention. By the end of the award period, we will have an improved and more comprehensive understanding of the biological and natural history of EAC that provides a platform to design better strategies to control its population burden.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
Domain-Knowledge Informed Deep Learning for Early Detection of Pancreatic Cancer
Comparative modeling of gastric cancer disparities and prevention in the US and globally
Optimal Colorectal Cancer Surveillance Strategy for Lynch Syndrome by Genotype
Comparative modeling of gastric cancer disparities and prevention in the US and globally
海外基金