Numerical Computing and Optimization in Developing Cancer Prognostic Systems

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.