Semisupervised adaptive learning models for IDH1 mutation status prediction

Fengning Liang, Yaru Cao, Teng Zhao, Qian Xu and Hong Zhu

PLOS ONE, 2025, vol. 20, issue 5, 1-16

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 emerged as a significant research challenge 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. Firstly, our model uses a rough set algorithm to remove the redundant medical image features extracted by radiomics, while adding pseudo-labels for non-labeled data via statistical. T-tests to mitigate the common 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 U-Net and CRNN to construct UCNet, a semisupervised classification model for classifying 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.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0321404

DOI: 10.1371/journal.pone.0321404

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