To address these limitations, we propose a 2.5D multi-task hybrid convolutional neural network (CNN) approach for classifying both IDH mutation and 1p/19q codeletion status of high- and low-grade gliomas (grades 2–4) from routine MR sequences (ie, pre-operative postcontrast T1-weighted (T1c), T2-weighted (T2), and T2-weighted Fluid-attenuated inversion recovery (FLAIR). The gene discussed is IDH1; the disease is glioma.