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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Citations (Scopus)

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 %).

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
PublisherSpringer Verlag
Pages26-34
Number of pages9
Volume9901 LNCS
ISBN (Print)9783319467221
DOIs
Publication statusPublished - 2016
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9901 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Liu, L., Zhang, H., Rekik, I., Chen, X., Wang, Q., & Shen, D. (2016). Outcome prediction for patient with high-grade gliomas from brain functional and structural networks. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings (Vol. 9901 LNCS, pp. 26-34). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9901 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46723-8_4