IIEEJ Transactions on Image Electronics and Visual Computing
Online ISSN : 2188-1901
Print ISSN : 2188-1898
ISSN-L : 2188-191X
Contributed Paper
Computerized Classification Method for 1p/19q Codeletion in Low Grade Gliomas from Brain MRI Images Using Three Dimensional Radiomics Features
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2022 Volume 10 Issue 1 Pages 120-126


The purpose of this study was to develop a computerized classification method for 1p/19q codeletion in low grade gliomas (LGGs) from brain MRI (magnetic resonance imaging) images using three dimensional (3D) radiomics features. Our database consisted of brain T2 weighted MRI images (102 LGGs with 1p/19q codeletion and 57 LGGs without it) obtained from 159 patients. In the proposed method, 107 3D radiomics features were extracted from LGG region in T2 weighted MRI images. The feature selection was performed with a least absolute shrinkage and selection operator to reduce redundancy among the extracted 3D radiomics features. A support vector machine (SVM) with the selected 3D radiomics features evaluated the likelihood of 1p/19q codeletion in LGG. A three-fold cross validation method was employed to train and test the proposed method. The classification accuracy, the sensitivity, the specificity, and the area under the receiver operating characteristic curve with the proposed method were 80.5%, 83.3%, 75.4%, and 0.836, respectively, showing an improvement when compared with SVM using 2D radiomics features (74.2%, 78.4%, 66.7%, and 0.783; p = 0.03). The proposed method with 3D radiomics features achieved high classification accuracy for 1p/19q codeletion in LGG from brain MRI images and would be useful for determining the patient managements.

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© 2022 The Institute of Image Electronics Engineers of Japan
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