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.

该研究提出了一种多模态学习框架，用于从医学图像中提取有关 Isocitrate Dehydrogenase (IDH) 基因突变的特征，以预测胶质瘤的基因型。实验结果表明，该模型优于基线深度学习模型，提供了一种预测胶质瘤基因型的新方法。