Abstract

Radiogenomic Prediction of MGMT Promoter Methylation Status in Glioblastoma Using Multi-Modal MRI and EfficientNet

Author(s): Pavan Nathani, Anikait Bharadwaj

Methylguanine DNA Methyltransferase (MGMT) promoter hypermethylation is a critical biomarker for predicting temozolomide sensitivity in aggressive Glioblastoma Multiforme (GBM), a disease with a poor prognosis. A Methylguanine DNA Methyltransferase status prediction without biopsy is mandatory for tailored therapy because MGMT patients have better prognosis. To this end, this paper proposes a radiogenomic model for predicting MGMT status in Glioblastoma patients based on multimodal magnetic resonance imaging (MRI) data and the EfficientNet deep learning architecture. Using sophisticated machine learning-based non-invasive methods of genetic prognosis may decrease risky biopsies. The MRI sequences in the Brain Tumor Segmentation (BraTS) 21 competition dataset include T1-weighted (T1w), T1-weighted contrast-enhanced (T1wCE), T2-weighted (T2w), and Fluid Attenuated Inversion Recovery (FLAIR) scans. Image preprocessing consisted of normalization, scaling, and Fourier transform-based data augmentation for cross-modal alignment. The EfficientNet-B0 model was initially pre-trained on ImageNet and then fine-tuned to the binary classification of methylated and unmethylated MGMT. The models were evaluated based on training and validation scores. The model’s ability to accurately identify MGMT methylation status was moderate, with a validation score score of 0.62393 at its best. Applying multimodal MRI data elevated feature extraction, proving deep learning models have radiogenomic predictive power. This study demonstrates that Automatic Live Control (ALC) and Deep Learning (DL) models can non-invasively assess MGMT promoter methylation status. That being said, such models may suffer from overfitting or generalization, requiring optimization; however, they are still a safer approach to biopsies and can enhance Glioblastoma treatment.