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

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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.

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Acknowledgments

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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Author notes

  1. Luyan Liu and Han Zhang contributed equally to this work.

Authors and Affiliations

  1. Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China

    Luyan Liu & Qian Wang

  2. Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Luyan Liu, Han Zhang, Xiaobo Chen, Islem Rekik & Dinggang Shen

  3. Glioma Surgery Division, Neurosurgery Department of Huashan Hospital, Fudan University, Shanghai, 200040, China

    Jinsong Wu, Zhengda Yu & Junfeng Lu

  4. Shanghai Key Lab of Medical Image Computing and Computer-Assisted Intervention, Shanghai, 200040, China

    Jinsong Wu & Junfeng Lu

  5. Neurosurgery Department of Huashan Hospital, 12 Wulumuqi Zhong Road, Shanghai, 200040, China

    Jinsong Wu & Junfeng Lu

  6. BASIRA Lab, CVIP Group, School of Science and Engineering, Computing, University of Dundee, Dundee, UK

    Islem Rekik

  7. Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea

    Dinggang Shen

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  1. Luyan Liu
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  2. Han Zhang
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  7. Qian Wang
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Correspondence to Qian Wang, Junfeng Lu or Dinggang Shen.

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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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