  Glioblastoma Multiforme (GBM) is a malignant brain cancer forming around 48%
of al brain and Central Nervous System (CNS) cancers. It is estimated that
annually over 13,000 deaths occur in the US due to GBM, making it crucial to
have early diagnosis systems that can lead to predictable and effective
treatment. The most common treatment after GBM diagnosis is chemotherapy, which
works by sending rapidly dividing cells to apoptosis. However, this form of
treatment is not effective when the MGMT promoter sequence is methylated, and
instead leads to severe side effects decreasing patient survivability.
Therefore, it is important to be able to identify the MGMT promoter methylation
status through non-invasive magnetic resonance imaging (MRI) based machine
learning (ML) models. This is accomplished using the Brain Tumor Segmentation
(BraTS) 2021 dataset, which was recently used for an international Kaggle
competition. We developed four primary models - two radiomic models and two CNN
models - each solving the binary classification task with progressive
improvements. We built a novel ML model termed as the Intermediate State
Generator which was used to normalize the slice thicknesses of all MRI scans.
With further improvements, our best model was able to achieve performance
significantly ($p < 0.05$) better than the best performing Kaggle model with a
6% increase in average cross-validation accuracy. This improvement could
potentially lead to a more informed choice of chemotherapy as a treatment
option, prolonging lives of thousands of patients with GBM each year.
