Numerical Computing and Optimization in Developing Cancer Prognostic Systems

医学实践涉及预测科学。预测取决于与结果相关的临床或实验室变量或因素。癌症医学中最常见的预测因素是三个变量：肿瘤大小、局部淋巴结状态和远处转移。这三个变量组合在一个bin模型中形成TNM分期系统，这是预测癌症患者预后和指导治疗的主要工具。然而，TNM系统只有三个变量。因此，其预测精度是有限的。本研究解决了数值计算和优化的问题，在发展扩展的癌症预后系统，可以整合多个变量。两组密切相关的任务将被调查，它们代表了研究的智力价值的两个主要方面。其中一项任务的重点是如何将剔除的生存时间和不同类型的变量整合到一个适用于大量癌症患者数据的聚类框架中。另一项任务是利用已开发的聚类分析来建立预后系统，该系统将通过考虑多种预后因素来提供更准确的结果预测。本研究的广泛影响包括许多方面。这将直接影响到未来癌症患者的分期和分类。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。研究者的研究有望对医学进步做出重大贡献。这项研究还将对教育产生影响。

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.