Document Type
Thesis
Date of Degree
Spring 2014
Degree Name
MS (Master of Science)
Degree In
Biomedical Engineering
First Advisor
Thomas L. Casavant
Abstract
Head and Neck cancers account for approximately 3.2% of the estimated 1,660,290 new cancer cases for the year 2013 and roughly 1.9% of cancer-related deaths in 2013. In this research, machine learning techniques were employed to predict outcome in cancer patients supporting more objective assessment of the treatments, including surgery, radiation therapy, or chemotherapy. Selection of features capable of distinguishing between the possible outcomes was accomplished by using a highly selective cohort of 61 patients with similar treatment and location of the primary tumor. An accuracy of 80.33% (compared to a baseline majority classifier of 60.66%) was achieved utilizing this cohort. Further, it is shown that this limited cohort has the power to provide valuable information on outcome prediction utilizing as few as four features. Feature selection was drawn from both clinical features and quantitative imaging features including the site of cancer, primary tumor volume, and race.
Pages
vi, 31 pages
Bibliography
Includes bibliographical references (pages 30-31).
Copyright
Copyright 2014 David John Dellsperger