Turkish Journal of Electrical Engineering and Computer Sciences




As per 2016 updates by World Health Organization (WHO) on cancer disease, gliomas are categorized and further treated based on genomic mutations. The imaging modalities support a complimentary but immediate noninvasive diagnosis of cancer based on genetic mutations. Our aim is to train a deep convolutional neural network for isocitrate dehydrogenase (IDH) genotyping of glioma by auto-extracting the most discriminative features from magnetic resonance imaging (MRI) volumes. MR imaging data of total 217 patients were obtained from The Cancer Imaging Archives (TCIA) of high and low-grade gliomas. A 3-pathway convolutional neural network was trained for IDH classification. The multipath neural network, consisting of one shallow and two deep neural network paths, is used to auto-extract the significant imaging features for successful IDH discrimination into IDH mutant and wild type. An accuracy of 93.67% and cross-entropy loss of 0.052 is achieved for IDH classification. The results of 3-pathway convolutional neural network (CNN) are better than the results achieved from individual paths of 3-pathway model. The results have demonstrated the multipath convolutional neural networks as state-of-the-art method with simple design to predict IDH genotypes in glioma with auto-extraction of radiogenomic features.


Glioma, magnetic resonance imaging, radiogenomic analysis, multipath convolutional neural network, isocitrate dehydrogenase

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