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

TL;DR

BTDNet is a novel multi-modal deep learning framework that accurately predicts MGMT promoter methylation status in brain tumors using multi-parametric MRI, addressing challenges of variable volume lengths and weak annotations.

Contribution

Introduces BTDNet, a multi-modal approach combining CNN-RNN, data augmentation, and modality fusion to improve brain tumor methylation classification from MRI scans.

Findings

Outperforms state-of-the-art methods in BraTS 2021 Challenge

Effectively handles variable volume lengths and weak annotations

Enhances brain tumor diagnosis accuracy

Abstract

Brain tumors pose significant health challenges worldwide, with glioblastoma being one of the most aggressive forms. 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, leveraging multi-parametric MRI scans, including FLAIR, T1w, T1wCE, and T2 3D volumes, to predict MGMT promoter methylation status. BTDNet addresses 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) the data augmentation one (that performs geometric transformations, convex combinations of data pairs and test-time data augmentation); ii) the 3D analysis one (that performs global analysis through a CNN-RNN); iii) the routing one (that contains a mask layer that handles variable input feature lengths), and iv) the modality fusion one (that effectively enhances data representation, reduces ambiguities and mitigates data scarcity). The proposed method outperforms by large margins the state-of-the-art methods in the RSNA-ASNR-MICCAI BraTS 2021 Challenge, offering a promising avenue for enhancing brain tumor diagnosis and treatment.