Objective Exploring the value of magnetic resonance contrast-enhanced T1-weighted(CE-T1 WI)-based ra-diomics model for predicting O6-methylguanine-DNA methyltransferase(MGMT)promoter methylation status in patients with gliomas.Methods MRI data from 281 glioma patients(108 methylation and 101 unmethylation)were retrospectively analyzed from the publicly available dataset The Cancer Imaging Archive(TCIA).The least absolute shrinkage and selection operator(LASSO)regression analysis and the max-relevance and min-redundancy correlation method were used for feature selection.We established a prediction model based on the multi-layer perceptron algorithm.The area under the subject operat-ing characteristic curve(AUC)was used to evaluate the predictive performance.Results The clinical model based on the multi-layer perceptron algorithm,the MRI model,and the combined clinical+MRI model could be used to predict the MGMT promoter methylation of patients with glioma,and the combined clinical+MRI model had the highest diagnostic efficacy com-pared with the former two,with an AUC of 0.909(95％CI:0.864-0.944),and the sensitivity and specificity of 86.11％and 82.50％,respectively in the training set.The validation set AUC was 0.831(95％CI:0.708-0.917),with sensitivity and specificity of 80.56％and 80.95％,respectively Delong's test showed that the difference in AUC between the combined clinical+MRI model and the clinical model was statistically significant(training set:Z=4.718,P＜0.05,validation set:Z=2.677,P＜0.05).Conclusion The multi-layer perceptron model based on MRI can effectively identify patients with MG-MT promoter methylation and MGMT promoter unmethylation gliomas.
GliomasO6-methylguanine-DNA methyltransferaseMagnetic resonance imagingMulti-layer perceptron model
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