  Glioblastoma, an aggressive brain cancer, is amongst the most lethal of all
cancers. Expression of the O6-methylguanine-DNA-methyltransferase (MGMT) gene
in glioblastoma tumor tissue is of clinical importance as it has a significant
effect on the efficacy of Temozolomide, the primary chemotherapy treatment
administered to glioblastoma patients. Currently, MGMT methylation is
determined through an invasive brain biopsy and subsequent genetic analysis of
the extracted tumor tissue. In this work, we present novel Bayesian classifiers
that make probabilistic predictions of MGMT methylation status based on
radiomic features extracted from FLAIR-sequence magnetic resonance imagery
(MRIs). We implement local radiomic techniques to produce radiomic activation
maps and analyze MRIs for the MGMT biomarker based on statistical features of
raw voxel-intensities. We demonstrate the ability for simple Bayesian
classifiers to provide a boost in predictive performance when modelling local
radiomic data rather than global features. The presented techniques provide a
non-invasive MRI-based approach to determining MGMT methylation status in
glioblastoma patients.
