Liang Fengning, Cao Yaru, Zhao Teng, Xu Qian, Zhu Hong
School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, China.
Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
PLoS One. 2025 May 5;20(5):e0321404. doi: 10.1371/journal.pone.0321404. eCollection 2025.
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.
胶质瘤中异柠檬酸脱氢酶1（IDH1）的突变状态是诊断、治疗和预后的关键信息。近年来，从MRI数据中准确确定此类信息已成为一项重大研究挑战。解决这个问题的现有技术往往存在各种局限性，例如数据浪费和不稳定问题。为了解决这些问题，我们提出了一种基于放射组学和粗糙集的半监督自适应深度学习模型，用于从MRI数据预测IDH1的突变状态。首先，我们的模型使用粗糙集算法去除放射组学提取的冗余医学图像特征，同时通过统计t检验为未标记数据添加伪标签，以缓解医学影像分析中常见的数据集不足问题。然后，应用沙猫群优化（SCSO）算法优化伪标签数据的权重。最后，我们的模型采用U-Net和CRNN构建UCNet，这是一个用于分类IDH1突变状态的半监督分类模型。为了验证我们的模型，我们使用了一个包含316例胶质瘤患者的术前MRI数据集来评估性能。我们的研究表明，胶质瘤IDH1突变状态的预测准确率达到95.63%。我们的实验结果表明，该研究可以有效提高胶质瘤影像数据的利用率和胶质瘤IDH1突变状态智能诊断的准确性。
