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Overall survival time prediction for high-grade glioma patients based on large-scale brain functional networks


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.

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This work is partially supported by National Natural Science Foundation of China (NSFC) Grants (61473190, 61401271, 81471733, 81201156, and 81401395), the National Key Technology R&D Program of China (2014BAI04B05), and NIH grant (EB022880).

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Correspondence to Qian Wang or Junfeng Lu or Dinggang Shen.

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The authors report no conflict of interest concerning the materials or methods used in this study or the findings specified in this paper.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the Huashan Institutional Review Board and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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Liu, L., Zhang, H., Wu, J. et al. Overall survival time prediction for high-grade glioma patients based on large-scale brain functional networks. Brain Imaging and Behavior 13, 1333–1351 (2019). https://doi.org/10.1007/s11682-018-9949-2

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