UCLA

Glioblastoma multiforme (GBM) is the most common type of primary brain tumor, characterized by a short survival period after diagnosis. As with most other cancers, treatment and follow-up decisions are made largely based on observed changes in tumor size and appearance during imaging studies.

The quantification of tumor measurements is problematic due to the systematic variability introduced while attempting to quantify tumor characteristics in uncertain regions. This issue is primarily observed around the tumor boundary, where it is often hard to differentiate whether a given region is part of the tumor (e.g., active, necrotic, edema, etc.) or part of normal brain tissue (e.g., grey matter, white matter). This problem has significant implications because this uncertainty can affect ensuing quantitative/computational analyses. Current approaches for the segmentation of glioblastoma multiforme still face multiple challenges, often failing to consistently identify the tumor region so as to be clinically useful and reliable; moreover, these different techniques tend to produce results that differ significantly from each other (i.e., measurement variability).

To address these problems, this dissertation describes a framework to help characterize factors that influence variability in brain tumor boundaries and to optimize their performance through methods that calculate an estimate of expected variability arising from different automated segmentation approaches, setting the bases for the development of better knowledge-based methods. Additionally, a novel automated method was developed to generate more robust brain tumor segmentations by taking into consideration the inherent variability of brain tumors and statistical priors that provide context-relevant information about the different brain and tumor tissues.

Altogether, this dissertation project provides further understanding of the sources of variability that arise in GBM across different image analysis methodologies and the integration of these insights into the development of tumor variability maps that can provide a better characterization of tumors.