Date of Graduation

5-2016

Document Type

Thesis

Degree Name

Master of Science in Industrial Engineering (MSIE)

Degree Level

Graduate

Department

Industrial Engineering

Advisor/Mentor

Shengfan Zhang

Committee Member

Manuel D. Rossetti

Second Committee Member

Ashlea B. Milburn

Keywords

Pure sciences, Applied sciences, Health and environmental sciences, Low grade glioma, Natural history model, Risk estimation

Abstract

Glioma is a common type of primary brain tumor that represents 28% of all brain tumors and 80% of malignant tumors. According to a recent study by the Centers for Disease Control and Prevention (CDC), gliomas account for 53%, 35% and 29% of all brain tumors (68%, 74% and 81% of malignant brain tumors) among children (aged 0-14), teenagers (aged 15-19) and young adults, respectively. Gliomas are often diagnosed through radiological imaging and histopathology. There are two main groups of gliomas following World Health Organization’s classification: Low grade gliomas (LGG), or grade I and II gliomas; and high grade gliomas (HGG), or Grade III and IV gliomas. This study focuses mainly on LGG due to its long term risks, such as recurrences and malignant transformations. Although the 5-year mortality rate for LGG patients is relatively high (17.6%), several studies reported that the average 5-year recurrence rate is up to 55%. However, there is currently limited guidelines for post-treatment management for LGG patients. This research aims to estimate the recurrence, malignancy transformation, and mortality risks for LGG patients who have had an initial treatment in order to have a better understanding of disease progression. These risk estimates can be incorporated in the development of a natural history model that can then be used in evaluating and optimizing post-treatment management strategies for LGG patients in future research.

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