Predicting isocitrate dehydrogenase mutationstatus in glioma using
structural brain networksand graph neural networks
- URL: http://arxiv.org/abs/2109.01854v1
- Date: Sat, 4 Sep 2021 12:19:33 GMT
- Title: Predicting isocitrate dehydrogenase mutationstatus in glioma using
structural brain networksand graph neural networks
- Authors: Yiran Wei, Yonghao Li, Xi Chen, Carola-Bibiane Sch\"onlieb, Chao Li,
and Stephen J. Price
- Abstract summary: The isocitrate dehydrogenase (IDH) gene mutation status provides critical diagnostic and prognostic value for glioma.
Machine learning and deep learning models show reasonable performance in predicting IDH mutation status.
We propose a method to predict the IDH mutation using graph neural networks (GNN) based on the structural brain network of patients.
- Score: 6.67232502899311
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Glioma is a common malignant brain tumor that shows distinct survival among
patients. The isocitrate dehydrogenase (IDH) gene mutation status provides
critical diagnostic and prognostic value for glioma and is now accepted as the
standard of care. A non-invasive prediction of IDH mutation based on the
pre-treatment MRI has crucial clinical significance. Machine learning and deep
learning models show reasonable performance in predicting IDH mutation status.
However, most models neglect the systematic brain alterations caused by tumor
invasion, where the infiltration along white matter tracts throughout the brain
is identified as a hallmark of glioma. Structural brain network provides an
effective tool to characterise brain organisation, which could be captured by
the graph neural networks (GNN) for a more accurate prediction of IDH mutation
status.
Here we propose a method to predict the IDH mutation using GNN, based on the
structural brain network of patients. Specifically, we firstly construct a
network template of healthy subjects, which consists of atlases of edges (white
matter tracts) and nodes (cortical and subcortical brain regions) to provide
regions of interest (ROI). Next, we employ autoencoders to extract the latent
multi-modal MRI features from the ROIs of the edge and node in patients. These
features of edge and node of brain networks are used to train a GNN
architecture in predicting IDH mutation status. The results show that the
proposed method outperforms the baseline models using 3D-CNN and 3D-DenseNet.
In addition, the model interpretation suggests its ability to identify the
tracts infiltrated by tumor and corresponds to clinical prior knowledge. In
conclusion, integrating brain networks with GNN offers a new avenue to study
brain lesions using computational neuroscience and computer vision approaches.
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