@article{Liao:2026:10.1016/j.displa.2026.103358,
author = {Liao, Y and Zheng, Y and Zhu, J and Chen, Y and Gao, F and Feng, Y and Yang, W and Yang, G and Lai, X and Li, P},
doi = {10.1016/j.displa.2026.103358},
journal = {Displays},
title = {Self-attention-based mixture-of-experts framework for non-invasive prediction of MGMT promoter methylation in glioblastoma using multi-modal MRI},
url = {http://dx.doi.org/10.1016/j.displa.2026.103358},
volume = {92},
year = {2026}
}

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TY  - JOUR
AB  - Glioblastoma (GBM) is an aggressive brain tumor associated with poor prognosis and limited treatment options. The methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter is a critical biomarker for predicting the efficacy of temozolomide chemotherapy in GBM patients. However, current methods for determining MGMT promoter methylation, including invasive and costly techniques, hinder their widespread clinical application. In this study, we propose a novel non-invasive deep learning framework based on a Mixture-of-Experts (MoE) architecture for predicting MGMT promoter methylation status using multi-modal magnetic resonance imaging (MRI) data. Our MoE model incorporates modality-specific expert networks built on the ResNet18 architecture, with a self-attention-based gating mechanism that dynamically selects and integrates the most relevant features across MRI modalities (T1-weighted, contrast-enhanced T1, T2-weighted, and fluid-attenuated inversion recovery). We evaluate the proposed framework on the BraTS2021 and TCGA-GBM datasets, showing superior performance compared to conventional deep learning models in terms of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Furthermore, Grad-CAM visualizations provide enhanced interpretability by highlighting biologically relevant regions in the tumor and peritumoral areas that influence model predictions. The proposed framework represents a promising tool for integrating imaging biomarkers into precision oncology workflows, offering a scalable, cost-effective, and interpretable solution for non-invasive MGMT methylation prediction in GBM.
AU  - Liao,Y
AU  - Zheng,Y
AU  - Zhu,J
AU  - Chen,Y
AU  - Gao,F
AU  - Feng,Y
AU  - Yang,W
AU  - Yang,G
AU  - Lai,X
AU  - Li,P
DO  - 10.1016/j.displa.2026.103358
PY  - 2026///
SN  - 0141-9382
TI  - Self-attention-based mixture-of-experts framework for non-invasive prediction of MGMT promoter methylation in glioblastoma using multi-modal MRI
T2  - Displays
UR  - http://dx.doi.org/10.1016/j.displa.2026.103358
VL  - 92
ER  - 

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