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Glioblastoma, an aggressive brain cancer, is amongst the most lethal of allcancers. Expression of the O6-methylguanine-DNA-methyltransferase (MGMT) genein glioblastoma tumor tissue is of clinical importance as it has a significanteffect on the efficacy of Temozolomide, the primary chemotherapy treatmentadministered to glioblastoma patients. Currently, MGMT methylation isdetermined through an invasive brain biopsy and subsequent genetic analysis ofthe extracted tumor tissue. In this work, we present novel Bayesian classifiersthat make probabilistic predictions of MGMT methylation status based onradiomic features extracted from FLAIR-sequence magnetic resonance imagery(MRIs). We implement local radiomic techniques to produce radiomic activationmaps and analyze MRIs for the MGMT biomarker based on statistical features ofraw voxel-intensities. We demonstrate the ability for simple Bayesianclassifiers to provide a boost in predictive performance when modelling localradiomic data rather than global features. The presented techniques provide anon-invasive MRI-based approach to determining MGMT methylation status inglioblastoma patients.

Novel Local Radiomic Bayesian Classifiers for Non-Invasive Prediction of MGMT Methylation Status in Glioblastoma