Challenges in predicting glioma survival time in multi-modal deep networks

Abdulrhman Aljouie, Yunzhe Xue, Meiyan Xie, Usman Roshan

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

Abstract

Prediction of cancer survival time is of considerable interest in medicine as it leads to better patient care and reduces health care costs. In this study, we propose a multi-path multimodal neural network that predicts Glioblastoma Multiforme (GBM) survival time at the 14 months threshold. We obtained image, gene expression, and SNP variants from whole-exome sequences all from the The Cancer Genome Atlas portal for a total of 126 patients. We perform a 10-fold cross-validation experiment on each of the data sources separately as well as the model with all data combined. From post-contrast Tl MRI data, we used 3D scans and 2D slices that we selected manually to show the tumor region. We find that the model with 2D MRI slices and genomic data combined gives the highest accuracies over individual sources but by a modest margin. We see considerable variation in accuracies across the 10 folds and that our model achieves 100% accuracy on the training data but lags behind in test accuracy. With dropout our training accuracy falls considerably. This shows that predicting glioma survival time is a challenging task but it is unclear if this is also a symptom of insufficient data. A clear direction here is to augment our data that we plan to explore with generative models. Overall we present a novel multi-modal network that incorporates SNP, gene expression, and MRI image data for glioma survival time prediction.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
EditorsTaesung Park, Young-Rae Cho, Xiaohua Tony Hu, Illhoi Yoo, Hyun Goo Woo, Jianxin Wang, Julio Facelli, Seungyoon Nam, Mingon Kang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2558-2562
Number of pages5
ISBN (Electronic)9781728162157
DOIs
StatePublished - Dec 16 2020
Event2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 - Virtual, Seoul, Korea, Republic of
Duration: Dec 16 2020Dec 19 2020

Publication series

NameProceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

Conference

Conference2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
CountryKorea, Republic of
CityVirtual, Seoul
Period12/16/2012/19/20

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Information Systems and Management
  • Medicine (miscellaneous)
  • Health Informatics

Keywords

  • CNN
  • MRI
  • SNPs
  • TCGA GBM
  • mRNA expression

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