3D deep learning for multi-modal imaging-guided survival time prediction of brain tumor patients

Dong Nie, Han Zhang, Ehsan Adeli, Luyan Liu, Dinggang Shen

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

190 Citations (Scopus)


High-grade glioma is the most aggressive and severe brain tumor that leads to death of almost 50% patients in 1–2 years. Thus,accurate prognosis for glioma patients would provide essential guidelines for their treatment planning. Conventional survival prediction generally utilizes clinical information and limited handcrafted features from magnetic resonance images (MRI),which is often time consuming,laborious and subjective. In this paper,we propose using deep learning frameworks to automatically extract features from multi-modal preoperative brain images (i.e.,T1 MRI,fMRI and DTI) of high-grade glioma patients. Specifically,we adopt 3D convolutional neural networks (CNNs) and also propose a new network architecture for using multi-channel data and learning supervised features. Along with the pivotal clinical features,we finally train a support vector machine to predict if the patient has a long or short overall survival (OS) time. Experimental results demonstrate that our methods can achieve an accuracy as high as 89.9% We also find that the learned features from fMRI and DTI play more important roles in accurately predicting the OS time,which provides valuable insights into functional neuro-oncological applications.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
EditorsGozde Unal, Sebastian Ourselin, Leo Joskowicz, Mert R. Sabuncu, William Wells
PublisherSpringer Verlag
Number of pages9
ISBN (Print)9783319467221
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)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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