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International Journal of Computer Assisted Radiology and Surgery
Published in: International Journal of Computer Assisted Radiology and Surgery 7/2015

01-07-2015 | Original Article

Resting state fMRI feature-based cerebral glioma grading by support vector machine

Authors: Jiangfen Wu, Zhiyu Qian, Ling Tao, Jianhua Yin, Shangwen Ding, Yameng Zhang, Zhou Yu

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 7/2015

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Abstract

Purpose 

Tumor grading plays an essential role in the optimal selection of solid tumor treatment. Noninvasive methods are needed for clinical grading of tumors. This study aimed to extract parameters of resting state blood oxygenation level-dependent functional magnetic resonance imaging (RS-fMRI) in the region of glioma and use the extracted features for tumor grading.

Methods 

Tumor segmentation was performed with both conventional MRI and RS-fMRI. Four typical parameters, signal intensity difference ratio, signal intensity correlation (SIC), fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo), were defined to analyze tumor regions. Mann–Whitney \(U\) test was employed to identify statistical difference of these four parameters between low-grade glioma (LGG) and high-grade glioma (HGG), respectively. Support vector machine (SVM) was employed to assess the diagnostic contributions of these parameters.

Results 

Compared with LGG, HGG had more complex anatomical morphology and BOLD-fMRI features in the tumor region. SIC \((p<0.001)\), fALFF (\(p=0.02\)) and ReHo (\(p=0.17\)) were selected as features for classification according to the test \(p\) value. The accuracy, sensitivity and specificity of SVM classification were better than 80, where SIC had the best classification accuracy (89).

Conclusion 

Parameters of RS-fMRI are effective to classify the tumor grade in glioma cases. The results indicate that this technique has clinical potential to serve as a complementary diagnostic tool.
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Metadata
Title
Resting state fMRI feature-based cerebral glioma grading by support vector machine
Authors
Jiangfen Wu
Zhiyu Qian
Ling Tao
Jianhua Yin
Shangwen Ding
Yameng Zhang
Zhou Yu
Publication date
01-07-2015
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Computer Assisted Radiology and Surgery / Issue 7/2015
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-014-1111-z

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