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) promotor hypermethylation is the most important biomarker for predicting temozolomide sensitivity in aggressive Glioblastoma (GBM), a disease with a poor prognosis. An Methylguanine DNA Methyltransferase (MGMT) status prediction without biopsy is mandatory for tailored therapy because methylated MGMT patients have better prognosis. To this end, this paper proposes the radiogenomic model for predicting the status of MGMT Promoter methylation in glioblastoma patients based on the multi-modal Magnetic Resonance Imaging (MRI) data and the EfficientNet deep learning architecture. Perhaps using sophisticated machine learning-based non-invasive methods of genetic prognosis may decrease risky biopsies. The Magnetic Resonance Imaging (MRI) sequences in the Brain Tumor Segmentation (BraTS) 21 competition dataset include T1- weighted and contrast-enhanced images, T2-weighted images and Fluid Attenuated Inversion Recovery (FLAIR) images. Image preprocessing consisted of normalization, scaling and data augmentation fourier transformations for cross-modal alignment. The EfficientNet-B0 model was initially pre-trained on ImageNet and then fine-tuned to the binary classification of methylation and unmethylated MGMT. The models were evaluated based on Area Under the Curve-Receiver Operating Characteristic (AUC-ROC), accuracy and the F1 score. The model’s capability of accurately identifying MGMT methylation status was moderate, with an AUCROC score of 0.62393 at its best. These differences influenced validation findings: overfit and resulted in accuracy variations for the small or unbalanced datasets. Applying multimodal MRI data elevated feature extraction, proving that deep learning models have radiogenic predictive power. Therefore, this study demonstrates that Automatic Live Control (ALC) and Deep Learning (DL) models can noninvasively estimate MGMT promoter methylation status. They also suffer from problems of overfitting or generalization, which will require some optimization. It might be safer than biopsies and can enhance approaches to glioblastoma treatment.