Outcome Prediction for Patient with High-Grade Gliomas from Brain Functional and Structural Networks

  • Luyan Liu
  • Han Zhang
  • Islem Rekik
  • Xiaobo Chen
  • Qian Wang
  • Dinggang Shen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9901)

Abstract

High-grade glioma (HGG) is a lethal cancer, which is characterized by very poor prognosis. To help optimize treatment strategy, accurate preoperative prediction of HGG patient’s outcome (i.e., survival time) is of great clinical value. However, there are huge individual variability of HGG, which produces a large variation in survival time, thus making prognostic prediction more challenging. Previous brain imaging-based outcome prediction studies relied only on the imaging intensity inside or slightly around the tumor, while ignoring any information that is located far away from the lesion (i.e., the “normal appearing” brain tissue). Notably, in addition to altering MR image intensity, we hypothesize that the HGG growth and its mass effect also change both structural (can be modeled by diffusion tensor imaging (DTI)) and functional brain connectivities (estimated by functional magnetic resonance imaging (rs-fMRI)). Therefore, integrating connectomics information in outcome prediction could improve prediction accuracy. To this end, we unprecedentedly devise a machine learning-based HGG prediction framework that can effectively extract valuable features from complex human brain connectome using network analysis tools, followed by a novel multi-stage feature selection strategy to single out good features while reducing feature redundancy. Ultimately, we use support vector machine (SVM) to classify HGG outcome as either bad (survival time ≤650 days) or good (survival time >650 days). Our method achieved 75 % prediction accuracy. We also found that functional and structural networks provide complementary information for the outcome prediction, thus leading to increased prediction accuracy compared with the baseline method, which only uses the basic clinical information (63.2 %).

Keywords

Support Vector Machine Diffusion Tensor Imaging Functional Connectivity Diffusion Tensor Imaging Data Functional Brain Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This work is supported by National Natural Science Foundation of China (NSFC) Grants (Nos. 61473190, 61401271, 81471733).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Luyan Liu
    • 1
  • Han Zhang
    • 2
  • Islem Rekik
    • 2
  • Xiaobo Chen
    • 2
  • Qian Wang
    • 1
  • Dinggang Shen
    • 2
  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

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