Overall Survival Time Prediction for High-grade Glioma Patients based on Large-scale Brain Functional Networks

Luyan Liu, Han Zhang, Jinsong Wu, Zhengda Yu, Xiaobo Chen, Islem Rekik, Qian Wang (Lead / Corresponding author), Junfeng Lu (Lead / Corresponding author), Dinggang Shen (Lead / Corresponding author)

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
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Abstract

High-grade glioma (HGG) is a lethal cancer with poor outcome. Accurate preoperative overall survival (OS) time prediction for HGG patients is crucial for treatment planning. Traditional presurgical and noninvasive OS prediction studies have used radiomics features at the local lesion area based on the magnetic resonance images (MRI). However, the highly complex lesion MRI appearance may have large individual variability, which could impede accurate individualized OS prediction. In this paper, we propose a novel concept, namely brain connectomics-based OS prediction. It is based on presurgical resting-state functional MRI (rs-fMRI) and the non-local, large-scale brain functional networks where the global and systemic prognostic features rather than the local lesion appearance are used to predict OS. We propose that the connectomics features could capture tumor-induced network-level alterations that are associated with prognosis. We construct both low-order (by means of sparse representation with regional rs-fMRI signals) and high-order functional connectivity (FC) networks (characterizing more complex multi-regional relationship by synchronized dynamics FC time courses). Then, we conduct a graph-theoretic analysis on both networks for a jointly, machine-learning-based individualized OS prediction. Based on a preliminary dataset (N = 34 with bad OS, mean OS, ~400 days; N = 34 with good OS, mean OS, ~1030 days), we achieve a promising OS prediction accuracy (86.8%) on separating the individuals with bad OS from those with good OS. However, if using only conventionally derived descriptive features (e.g., age and tumor characteristics), the accuracy is low (63.2%). Our study highlights the importance of the rs-fMRI and brain functional connectomics for treatment planning.
Original languageEnglish
Pages (from-to)1333-1351
Number of pages19
JournalBrain Imaging and Behavior
Volume13
Issue number5
Early online date28 Aug 2018
DOIs
Publication statusPublished - Oct 2019

Keywords

  • Brain network
  • Connectomics
  • Functional connectivity
  • Glioma
  • Machine learning
  • Prognosis
  • Survival

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