Multi-View MRI Approach for Classification of MGMT Methylation in Glioblastoma Patients

TL;DR

This study presents a novel multi-view MRI deep learning approach to non-invasively determine MGMT methylation status in glioblastoma, improving diagnostic accuracy while reducing computational complexity.

Contribution

We introduce a multi-view deep learning method that leverages spatial relationships in MRI scans without complex 3D models, along with a new tumor slice extraction technique.

Findings

Our method outperforms existing models on multiple metrics.

The approach is non-invasive and reduces computational demands.

Reproducible pipeline supports further research.

Abstract

The presence of MGMT promoter methylation significantly affects how well chemotherapy works for patients with Glioblastoma Multiforme (GBM). Currently, confirmation of MGMT promoter methylation relies on invasive brain tumor tissue biopsies. In this study, we explore radiogenomics techniques, a promising approach in precision medicine, to identify genetic markers from medical images. Using MRI scans and deep learning models, we propose a new multi-view approach that considers spatial relationships between MRI views to detect MGMT methylation status. Importantly, our method extracts information from all three views without using a complicated 3D deep learning model, avoiding issues associated with high parameter count, slow convergence, and substantial memory demands. We also introduce a new technique for tumor slice extraction and show its superiority over existing methods based on multiple evaluation metrics. By comparing our approach to state-of-the-art models, we demonstrate the efficacy of our method. Furthermore, we share a reproducible pipeline of published models, encouraging transparency and the development of robust diagnostic tools. Our study highlights the potential of non-invasive methods for identifying MGMT promoter methylation and contributes to advancing precision medicine in GBM treatment.