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Brain Imaging and Behavior

, Volume 13, Issue 5, pp 1333–1351 | Cite as

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 WangEmail author
  • Junfeng LuEmail author
  • Dinggang ShenEmail author
Original Research

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.

Keywords

Survival Prognosis Glioma Functional connectivity Brain network Connectomics Machine learning 

Notes

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).

Compliance with ethical standards

Disclosure

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

Ethical approval

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.

Informed consent

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

Supplementary material

11682_2018_9949_MOESM1_ESM.docx (4.1 mb)
ESM 1 (DOCX 4163 kb)

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Med-X Research Institute, School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA
  3. 3.Glioma Surgery Division, Neurosurgery Department of Huashan HospitalFudan UniversityShanghaiChina
  4. 4.Shanghai Key Lab of Medical Image Computing and Computer-Assisted InterventionShanghaiChina
  5. 5.Neurosurgery Department of Huashan HospitalShanghaiChina
  6. 6.BASIRA Lab, CVIP Group, School of Science and Engineering, ComputingUniversity of DundeeDundeeUK
  7. 7.Department of Brain and Cognitive EngineeringKorea UniversitySeoulRepublic of Korea

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