Numerical Computing and Optimization in Developing Cancer Prognostic Systems

开发癌症预后系统中的数值计算和优化

• 批准号: 0729080
• 负责人: Dechang Chen
• 金额: $ 15万
• 依托单位: Henry M Jackson Fdn for Advmt of Military Medicine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-10-01 至 2011-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0729080&HistoricalAwards=false
• 关键词: Numerical; Computing; Optimization; Developing; Cancer

The practice of medicine involves the science of prediction. Prediction depends on clinical or laboratory variables or factors that are linked to outcome. The most common predictors in cancer medicine are the three variables: tumor size, regional lymph node status, and distant metastasis. The three variables are combined in a bin model to form the TNM Staging system, which is a major tool used to predict the outcome of cancer patients and guide therapy. However, the TNM system has only three variables. Therefore, its predictive accuracy is limited. This research addresses issues of numerical computing and optimization in developing expanded cancer prognostic systems that can integrate multiple variables. Two sets of closely related tasks will be investigated, and they represent two major aspects of the intellectual merit of the study. One task is focused on how censored survival times and different types of variables are integrated into a clustering framework that works for a large volume of cancer patient data. Another task is to use the developed cluster analysis to establish prognostic systems, which will provide a more accurate prediction of outcome by taking multiple prognostic factors into account. The broader impact of this research includes many aspects. It will have a direct impact on future staging and classification of cancer patients. The work has general applications and can be adapted to studies of any non-cancer health problems. The investigator's study is expected to make significant contributions to advances in medicine. The research will also have an educational impact.

医学实践涉及预测科学。预测取决于临床或实验室变量或与结果相关的因素。肿瘤医学中最常见的预测因子是三个变量：肿瘤大小、区域淋巴结状态和远处转移。这三个变量在bin模型中组合形成TNM分期系统，这是用于预测癌症患者结果和指导治疗的主要工具。然而，TNM系统只有三个变量。因此，它的预测精度是有限的。这项研究解决了在开发扩展的癌症预后系统，可以整合多个变量的数值计算和优化的问题。两组密切相关的任务将被调查，他们代表了两个主要方面的研究知识价值。 一个任务是集中在如何审查生存时间和不同类型的变量被整合到一个聚类框架，适用于大量的癌症患者数据。另一个任务是使用开发的聚类分析建立预后系统，这将提供一个更准确的预测结果，考虑到多个预后因素。这项研究的广泛影响包括许多方面。它将对未来癌症患者的分期和分类产生直接影响。这项工作具有普遍的应用，可以适用于任何非癌症健康问题的研究。研究人员的研究有望对医学的进步做出重大贡献。这项研究还将产生教育影响。