[1]XU Huachang,XU Qian,ZHAO Yulin,et al.Prediction of glioma IDH1 mutation status based on AEViT and prior knowledge[J].CAAI Transactions on Intelligent Systems,2024,19(4):952-960.[doi:10.11992/tis.202209055]
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Prediction of glioma IDH1 mutation status based on AEViT and prior knowledge

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 4 Page number: 952-960 Column: 学术论文—智能系统 Public date: 2024-07-05
Title:
Prediction of glioma IDH1 mutation status based on AEViT and prior knowledge
Author(s):
XU Huachang1;  XU Qian2;  ZHAO Yulin1;  LIANG Fengning1;  XU Kai2;  ZHU Hong1
1. School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou 221000, China;
2. Medical Imaging Department, The Affiliated Hospital of Xuzhou Medical University, Xuzhou 221000, China
Keywords:
glioma; IDH1; K-Means clustering algorithm; pseudo-label; Auto-Encoder; vision Transformer; fruit fly optimization algorithm; priori knowledge
CLC:
TP18
DOI:
10.11992/tis.202209055
Abstract:
Aiming at the problems of insufficient data and low accuracy in predicting the mutation status of brain glioma isocitrate dehydrogenase1 (IDH1), a prediction method for IDH1 mutation status of brain glioma is proposed based on auto-encoder vision Transformer (AEViT) and priori knowledge. Firstly, an improved K-Means clustering algorithm was used to label the pseudo-labels for MRI data of glioma without IDH1 status labels, and vision Transformer (ViT) network was used to modify the pseudo labels to obtain the final glioma IDH1 mutation status. In order to avoid inaccurate pseudo-label data that affect accuracy of the model, the fruit fly optimization algorithm was used to assign the optimal weight to the pseudo-label data. Secondly, a classification model AEViT based on Auto-Encoder and Vision Transformer was proposed, and Auto-Encoder was used to extract MRI features of glioma, and then the features were input into ViT to classify the IDH1 mutation status of glioma. Finally, the prior knowledge based on glioma location information was added to the model, which achieved a prediction accuracy of 99.01%. The experimental results show that this method is superior to other existing models, and can realize glioma data augmentation and non-invasive and accurate preoperative prediction of glioma IDH1 mutation status, thereby assisting the diagnosis and treatment process.
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