Semisupervised adaptive learning models for IDH1 mutation status prediction

Abstract The mutation status of isocitrate dehydrogenase1 (IDH1) in glioma is critical information for the diagnosis, treatment, and prognosis. Accurately determining such information from MRI data has become an important research problem in recent years. Existing techniques for this problem often suffer from various limitations, such as the data waste and instability issues. To address such issues, we present a semisupervised adaptive deep learning model based on radiomics and rough sets for predicting the mutation status of IDH1 from MRI data. Our model first uses a rough set algorithm to remove the redundant medical image features extracted by radiomics, and adds pseudo-labels to those non-label data with the help of statistical T-test. This helps us address the commonly encountered issue of insufficient datasets in medical imaging analysis. Then, it applies a Sand Cat Swarm Optimization (SCSO) algorithm to optimize the weight of pseudo-label data. Finally, our model adopts UNet and CRNN to construct a semisupervised classification model UCNet to classify the IDH1 mutation status. To validate our models, we use a preoperative MRI dataset with 316 glioma patients to evaluate the performance. Our study suggests that the prediction accuracy of glioma IDH1 mutation status reaches 95.63%. Our experimental results suggest that the study can effectively improve the utilization of glioma imaging data and the accuracy of intelligent diagnosis of glioma IDH1 mutation status.