Collaborative learning of images and geometrics for predicting isocitrate dehydrogenase status of glioma

Yiran Wei, Chao Li, Xi Chen, Carola-Bibiane Schönlieb, Stephen J. Price

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

3 Citations (SciVal)
79 Downloads (Pure)

Abstract

The isocitrate dehydrogenase (IDH) gene mutation status is an important biomarker for glioma patients. The gold standard of IDH mutation detection requires tumour tissue obtained via invasive approaches and is usually expensive. Recent advancement in radiogenomics provides a non-invasive approach for predicting IDH mutation based on MRI. Meanwhile, tumor geometrics encompass crucial information for tumour phenotyping. Here we propose a collaborative learning framework that learns both tumor images and tumor geometrics using convolutional neural networks (CNN) and graph neural networks (GNN), respectively. Our results show that the proposed model outperforms the baseline model of 3D-DenseNet121. Further, the collaborative learning model achieves better performance than either the CNN or the GNN alone. The model interpretation shows that the CNN and GNN could identify common and unique regions of interest for IDH mutation prediction. In conclusion, collaborating image and geometric learners provides a novel approach for predicting genotype and characterising glioma.
Original languageEnglish
Title of host publicationISBI 2022 - Proceedings
Subtitle of host publication2022 IEEE International Symposium on Biomedical Imaging
Place of PublicationU. S. A.
PublisherIEEE
ISBN (Electronic)9781665429238
ISBN (Print)9781665429245
DOIs
Publication statusE-pub ahead of print - 26 Apr 2022

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2022-March
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Bibliographical note

Accepted by International Symposium on Biomedical Imaging 2022

Keywords

  • collaborative learning
  • geometric deep learning
  • glioma
  • graph neural networks
  • isocitrate dehydrogenase

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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