TDABNet: Three-directional attention block network for the determination of IDH status in low- and high-grade gliomas from MRI

Abstract

The isocitrate dehydrogenase (IDH) mutation in low- and high-grade gliomas have proven to be the critical molecular biomarker associated with better prognosis. Although the determination of the IDH status of these neoplasms prior to surgical intervention is considered beneficial for prognosis, this information is currently only available after surgical removal of the tissue. At present, most studies have proved the efficiency of deep learning technology in noninvasive diagnosing IDH status. However, there are still some shortages. Firstly, they only input the 2D slices of gliomas into the network, ignoring the significant amount of extra information of gliomas in the third dimension. Secondly, because glioma is a heterogeneous three-dimensional volume with complex imaging features, it is still a challenge for traditional CNN to learn the features that help predict IDH status from magnetic resonance imaging (MRI). To address these issues, we propose a Three-Directional Attention Block Network (TDABNet) based on a three-dimensional convolutional neural network (3D CNN), which can accurately determine the IDH status in gliomas from 3D MRI. The performance of TDABNet was validated in a dataset of 235 patients with low- and high-grade gliomas and the area under the operating characteristic curve (AUC) of IDH status prediction is 96. 44%. It is proved by experiment that TDABNet can accurately predict the IDH status of gliomas.