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Med Image Comput Comput Assist Interv. 2016 Oct;9901:212-220. doi: 10.1007/978-3-319-46723-8_25. Epub 2016 Oct 2.

3D Deep Learning for Multi-modal Imaging-Guided Survival Time Prediction of Brain Tumor Patients.

Author information

1
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA; Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, USA.
2
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA.

Abstract

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.

PMID:
28149967
PMCID:
PMC5278791
DOI:
10.1007/978-3-319-46723-8_25
[Indexed for MEDLINE]
Free PMC Article

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