BTDNet: A Multi-Modal Approach for Brain Tumor Radiogenomic Classification

Abstract

Brain tumors pose significant health challenges worldwide, with glioblastoma being one of the most aggressive forms. The accurate determination of the O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status is crucial for personalized treatment strategies. However, traditional methods are labor-intensive and time-consuming. This paper proposes a novel multi-modal approach, BTDNet, that leverages multi-parametric MRI scans, including FLAIR, T1w, T1wCE, and T2 3D volumes, to predict the MGMT promoter methylation status. BTDNet’s main contribution involves addressing two main challenges: the variable volume lengths (i.e., each volume consists of a different number of slices) and the volume-level annotations (i.e., the whole 3D volume is annotated and not the independent slices that it consists of). BTDNet consists of four components: (i) data augmentation (which performs geometric transformations, convex combinations of data pairs, and test-time data augmentation); (ii) 3D analysis (which performs global analysis through a CNN-RNN); (iii) routing (which contains a mask layer that handles variable input feature lengths); and (iv) modality fusion (which effectively enhances data representation, reduces ambiguities, and mitigates data scarcity). The proposed method outperformed state-of-the-art methods in the RSNA-ASNR-MICCAI BraTS 2021 Challenge by at least 3.3% in terms of the F1 score, offering a promising avenue for enhancing brain tumor diagnosis and treatment.

Keywords

• RSNA-ASNR-MICCAI BraTS 2021 Challenge
• brain tumor radiogenomic classification
• prediction of MGMT promoter methylation status
• BTDNet
• multimodal approach
• routing