Multimodal Sheaf-based Network for Glioblastoma Molecular Subtype Prediction
This work addresses the problem of invasive tissue extraction for glioblastoma classification by developing a non-invasive diagnostic tool, though it is incremental as it builds on existing multimodal approaches.
The paper tackles glioblastoma molecular subtype prediction by proposing a sheaf-based framework for fusing MRI and histopathology data, which outperforms baseline methods and shows robustness with incomplete data, contributing to virtual biopsy tools.
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/.