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Permanent link (DOI): https://doi.org/10.7939/R39G74

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Theses and Dissertations

Prognosis of Glioblastoma Multiforme Using Textural Properties on MRI Open Access

Descriptions

Other title
Subject/Keyword
prognosis
machine learning
MRI
GBM
texture
survival
glioblastoma
decision tree
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Heydari, Maysam
Supervisor and department
Greiner, Russell (Computing Science)
Examining committee member and department
Brown, Matthew (Psychiatry)
Murtha, Albert (Radiation Oncology)
Zaïane, Osmar (Computing Science)
Department
Department of Computing Science
Specialization

Date accepted
2009-10-08T19:10:29Z
Graduation date
2009-11
Degree
Master of Science
Degree level
Master's
Abstract
This thesis addresses the challenge of prognosis, in terms of survival prediction, for patients with Glioblastoma Multiforme brain tumors. Glioblastoma is the most malignant brain tumor, which has a median survival time of no more than a year. Accurate assessment of prognostic factors is critical in deciding amongst different treatment options and in designing stratified clinical trials. This thesis is motivated by two observations. Firstly, clinicians often refer to properties of glioblastoma tumors based on magnetic resonance images when assessing prognosis. However, clinical data, along with histological and most recently, molecular and gene expression data, have been more widely and systematically studied and used in prognosis assessment than image based information. Secondly, patient survival times are often used along with clinical data to conduct population studies on brain tumor patients. Recursive Partitioning Analysis is typically used in these population studies. However, researchers validate and assess the predictive power of these models by measuring the statistical association between survival groups and survival times. In this thesis, we propose a learning approach that uses historical training data to produce a system that predicts patient survival. We introduce a classification model for predicting patient survival class, which uses texture based features extracted from magnetic resonance images as well as other patient properties. Our prognosis approach is novel as it is the first to use image-extracted textural characteristics of glioblastoma scans, in a classification model whose accuracy can be reliably validated by cross validation. We show that our approach is a promising new direction for prognosis in brain tumor patients.
Language
English
DOI
doi:10.7939/R39G74
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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