Multi-modal learning for predicting the genotype of glioma

TL;DR

This paper introduces a multi-modal learning framework integrating tumor imaging, geometric, and brain network features from MRI to improve glioma genotype prediction, addressing limitations of traditional CNNs with non-Euclidean data.

Contribution

The study develops a novel multi-modal framework with encoders, a self-supervised brain network generator, and a hierarchical attention module, advancing glioma genotype prediction methods.

Findings

Outperforms baseline deep learning models in genotype prediction

Validates the effectiveness of hierarchical attention in brain network analysis

Provides interpretable results aligned with clinical knowledge

Abstract

The isocitrate dehydrogenase (IDH) gene mutation is an essential biomarker for the diagnosis and prognosis of glioma. It is promising to better predict glioma genotype by integrating focal tumor image and geometric features with brain network features derived from MRI. Convolutions neural networks show reasonable performance in predicting IDH mutation, which, however, cannot learn from non-Euclidean data, e.g., geometric and network data. In this study, we propose a multi-modal learning framework using three separate encoders to extract features of focal tumor image, tumor geometrics and global brain networks. To mitigate the limited availability of diffusion MRI, we develop a self-supervised approach to generate brain networks from anatomical multi-sequence MRI. Moreover, to extract tumor-related features from the brain network, we design a hierarchical attention module for the brain network encoder. Further, we design a bi-level multi-modal contrastive loss to align the multi-modal features and tackle the domain gap at the focal tumor and global brain. Finally, we propose a weighted population graph to integrate the multi-modal features for genotype prediction. Experimental results on the testing set show that the proposed model outperforms the baseline deep learning models. The ablation experiments validate the performance of different components of the framework. The visualized interpretation corresponds to clinical knowledge with further validation. In conclusion, the proposed learning framework provides a novel approach for predicting the genotype of glioma.