Predicting Isocitrate Dehydrogenase Mutation Status in Glioma Using Structural Brain Networks and Graph Neural Networks

Yiran Wei, Yonghao Li, Xi Chen, Carola Bibiane Schönlieb, Chao Li (Lead / Corresponding author), Stephen J. Price

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)
35 Downloads (Pure)

Abstract

Glioma is a common malignant brain tumor with distinct survival among patients. The isocitrate dehydrogenase (IDH) gene mutation provides critical diagnostic and prognostic value for glioma. It is of crucial significance to non-invasively predict IDH mutation based on pre-treatment MRI. Machine learning/deep learning models show reasonable performance in predicting IDH mutation using MRI. However, most models neglect the systematic brain alterations caused by tumor invasion, where widespread infiltration along white matter tracts is a hallmark of glioma. Structural brain network provides an effective tool to characterize brain organisation, which could be captured by the graph neural networks (GNN) to more accurately predict IDH mutation. Here we propose a method to predict IDH mutation using GNN, based on the structural brain network of patients. Specifically, we firstly construct a network template of healthy subjects, consisting of atlases of edges (white matter tracts) and nodes (cortical/subcortical brain regions) to provide regions of interest (ROIs). Next, we employ autoencoders to extract the latent multi-modal MRI features from the ROIs of edges and nodes in patients, to train a GNN architecture for predicting IDH mutation. The results show that the proposed method outperforms the baseline models using the 3D-CNN and 3D-DenseNet. In addition, model interpretation suggests its ability to identify the tracts infiltrated by tumor, corresponding 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.

Original languageEnglish
Title of host publicationBrainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
Subtitle of host publication 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part I
EditorsAlessandro Crimi, Spyridon Bakas
Place of PublicationSwitzerland
PublisherSpringer
Pages140-150
Number of pages11
Volume1
Edition1
ISBN (Electronic)9783031089992
ISBN (Print)9783031089985
DOIs
Publication statusPublished - 2022
Event7th International Brain Lesion Workshop, BrainLes 2021, held in conjunction with the Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online
Duration: 27 Sept 202127 Sept 2021

Publication series

NameLecture Notes in Computer Science
Volume12962
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Brain Lesion Workshop, BrainLes 2021, held in conjunction with the Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
CityVirtual, Online
Period27/09/2127/09/21

Keywords

  • Glioma
  • Graph neural network
  • Isocitrate dehydrogenase
  • MRI
  • Structural brain network

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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