Numerical Computing and Optimization in Developing Cancer Prognostic Systems

医学实践涉及到预测科学。预测取决于临床或实验室变量或与结果相关的因素。癌症医学中最常见的预测因子是三个变量：肿瘤大小、区域淋巴结状况和远处转移。这三个变量被组合在一个bin模型中，形成了TNM分期系统，该系统是用于预测癌症患者结局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可以集成多个变量的扩展癌症预后系统时的数值计算和优化问题。将调查两组密切相关的任务，它们代表了这项研究的智力价值的两个主要方面。其中一个任务是如何将审查后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集群框架中。另一项任务是使用已开发的聚类分析来建立预测系统，该系统将通过考虑多个预测因素来提供更准确的结果预测。这项研究的更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价值，并可适用于任何非癌症健康问题的研究。这位研究人员的研究有望为医学进步做出重大贡献。这项研究还将产生教育影响。

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.