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• Original Article •
Application of radiomics in the grading of brain tumor
YANG Zhi-wei  GUO Tian  XIE Hai-bin  YANG Guang 

DOI:10.12015/issn.1674-8034.2018.06.008.


[Abstract] Objective: Using radiomics and conventional magnetic resonance images to grade brain tumor.Materials and Methods: This paper uses the open dataset: BRATS2017. Kinds of features, including shape features, intensity features and texture features, were extracted from region of interest and a subset of features were selected by hybrid feature selection methods to distinguish the high grade glioma (HGG) from the low grade glioma (LGG). Three classification algorithms including support vector machine (SVM), decision tree, and K nearest neighborhood were compared. The wrapper method using genetic algorithm (GA) was compared with filter method.Results: SVM was found to exhibit the best accuracy: 91.33% and AUC: 0.90 when using the feature selection method of filter. The accuracy and AUC raise to 93.33% and 0.94 when further using the method of genetic algorithm.Conclusions: We could use radiomics methods and conventional magnetic resonance images to automatically grade brain tumor by selecting suitable features.
[Keywords] Magnetic resonance imaging；Radiomics；Glioma；Feature selection；Genetic algorithm；Neoplasm grading

YANG Zhi-wei Shanghai Key Laboratory of Magnetic Resonance, Department of Physics, East China Normal University, Shanghai 200062, China

GUO Tian Shanghai Key Laboratory of Magnetic Resonance, Department of Physics, East China Normal University, Shanghai 200062, China

XIE Hai-bin* Shanghai Key Laboratory of Magnetic Resonance, Department of Physics, East China Normal University, Shanghai 200062, China

YANG Guang* Shanghai Key Laboratory of Magnetic Resonance, Department of Physics, East China Normal University, Shanghai 200062, China

*Correspondence to: Xie HB, E-mail: hbxie@phy.ecnu.edu.cn; Yang G, E-mail: gyang@phy.ecnu.edu.cn

Conflicts of interest   None.

ACKNOWLEDGMENTS  This work was part of Key Program of National Natural Science Foundation of China No. 61731009
Received  2018-04-09
Accepted  2018-04-28
DOI: 10.12015/issn.1674-8034.2018.06.008
DOI:10.12015/issn.1674-8034.2018.06.008.

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