A multitask classification framework based on vision transformer for predicting molecular expressions of glioma

Abstract

PurposeThe purpose of this study is to develop a Vision Transformer model with multitask classification framework that is appropriate for predicting four molecular expressions of glioma simultaneously based on MR imaging. Materials and methodsA total of 188 glioma (grades II–IV) patients with an immunohistochemical diagnosis of IDH, MGMT, Ki67 and P53 expression were enrolled in our study. A Vision Transformer (ViT) model, including three independent networks based on T2WI, T1CWI and T2 + T1CWI (T2-net, T1C-net and TU-net), was developed for the prediction of four glioma molecular expressions simultaneously. To evaluate the model performance, the accuracy rate, recall, precision, F1-score, and area under the receiver operating characteristic curve (AUC) were calculated. ResultsThe proposed ViT model achieved high accuracy in predicting IDH, MGMT, Ki67 and P53 expression in gliomas. Among the three networks using the ViT model, TU-net achieved the best results with the highest values of accuracy (range, 0.937–0.969), precision (range, 0.949–0.972), recall (range, 0.873–0.991), F1-score (range, 0.910–0.981) and AUC (range, 0.976–0.984). Comparisons were also made between our ViT model and convolutional neural network (CNN)-based models, and the proposed ViT model outperformed the existing CNN-based models. ConclusionVision Transformer is a reliable approach for the prediction of glioma molecular biomarkers and can be a viable alternative to CNNs.