Multimodal Sheaf-based Network for Glioblastoma Molecular Subtype Prediction

TL;DR

This paper introduces a sheaf-based multimodal network that effectively fuses MRI and histopathology data for glioblastoma subtype prediction, improving robustness and enabling virtual biopsy applications.

Contribution

The novel sheaf-based framework enhances multimodal data fusion for glioblastoma classification, addressing limitations of existing graph-based models and handling incomplete data effectively.

Findings

Outperforms baseline methods in glioblastoma subtype prediction

Demonstrates robustness with missing or incomplete data

Enables rapid virtual biopsy diagnostics

Abstract

Glioblastoma is a highly invasive brain tumor with rapid progression rates. Recent studies have shown that glioblastoma molecular subtype classification serves as a significant biomarker for effective targeted therapy selection. However, this classification currently requires invasive tissue extraction for comprehensive histopathological analysis. Existing multimodal approaches combining MRI and histopathology images are limited and lack robust mechanisms for preserving shared structural information across modalities. In particular, graph-based models often fail to retain discriminative features within heterogeneous graphs, and structural reconstruction mechanisms for handling missing or incomplete modality data are largely underexplored. To address these limitations, we propose a novel sheaf-based framework for structure-aware and consistent fusion of MRI and histopathology data. Our model outperforms baseline methods and demonstrates robustness in incomplete or missing data scenarios, contributing to the development of virtual biopsy tools for rapid diagnostics. Our source code is available at https://github.com/basiralab/MMSN/.