Semisupervised adaptive learning models for IDH1 mutation status prediction

• Published in: PloS one Vol. 20; no. 5; p. e0321404
• Main Authors: Liang, Fengning, Cao, Yaru, Zhao, Teng, Xu, Qian, Zhu, Hong
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 05.05.2025
• Subjects: Accuracy; Adaptive learning; Algorithms; Analysis; Biology and Life Sciences; Brain cancer; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - genetics; Care and treatment; Classification; Computer and Information Sciences; Datasets; Deep Learning; Diagnosis; Enzymes; Feature extraction; Glioma; Glioma - diagnostic imaging; Glioma - genetics; Gliomas; Health aspects; Humans; Isocitrate dehydrogenase; Isocitrate Dehydrogenase - genetics; Labels; Machine learning; Magnetic Resonance Imaging; Medical imaging; Medical prognosis; Medicine and Health Sciences; Mutation; Neural networks; Optimization; Patients; Physical Sciences; Prognosis; Radiomics; Research and Analysis Methods; Statistical analysis; Tumors; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0321404

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.