Clinical information and multi-sequence MRI Transformer model predicts isocitrate dehydrogenase mutation status in glioma
10.3969/j.issn.1002-1671.2025.02.002
- VernacularTitle:临床信息与多序列MRI Transformer模型预测胶质瘤异柠檬酸脱氢酶突变状态
- Author:
Yong WEI
1
;
Yuena LIU
;
Fengmei ZHOU
;
Changhua LIANG
Author Information
1. 新乡医学院第一附属医院放射科,河南 卫辉 453100
- Publication Type:Journal Article
- Keywords:
glioma;
isocitrate dehydrogenase;
magnetic resonance imaging;
Transformer model
- From:
Journal of Practical Radiology
2025;41(2):186-189
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the value of the Transformer model based on multi-sequence MRI to predict isocitrate dehydrogenase(IDH)mutation status in patients with glioma.Methods The multi-sequence MRI data of 500 glioma patients(103 mutation-type and 397 wild-type)were analyzed retrospectively from the publicly available dataset Cancer Imaging Archive.The prediction model was constructed through the Transformer deep learning algorithm.Area under the curve(AUC)of the receiver operating characteristic(ROC)curve was used to evaluate the predictive performance,and the five-fold crossover was used for validation of the predictive model.Results The clinical,multi-sequence MRI,and combined clinical+multi-sequence MRI models based on the Transformer deep learning algorithm could be used to predict the IDH mutation status of patients with glioma,and the combined clinical+multi-sequence MRI model had the highest diagnostic efficacy compared with the former two,with an AUC of 0.904[95％confidence interval(CI)0.875-0.928],and the sensitivity and specificity of 86.41％and 86.40％,respectively.DeLong's test showed that the difference in AUC between the combined clinical+multi-sequence MRI model and the clinical model was statistically significant(Z=3.327,P＜0.001).Conclusion The Transformer model based on multi-sequence MRI can effectively identify patients with IDH mutation-type glioma and IDH wild-type glioma.
