Abstract: Objective To explore the value of texture analysis based on conventional magnetic resonance imaging (MRI) in predicting 1p/19q co-deletion in low-grade glioma.Methods A total of 106 patients with low-grade glioma were retrospectively included. According to the co-deletion of 1p/19q, they were divided into two groups. The regions of interest (ROI) were outlined in MRI images, and the parameters of ROI were extracted. The results were analyzed by the receiver operating characteristic curve (ROC) and multivariate logistics regression.Results According to ROC analysis, the sensitivity of T₂WI skewness parameter was 92.9%, the specificity was 69.4%, and the AUC was 0.857. The sensitivity of T₂WI coefficient of variation was 65.7%, the specificity was 69.4%, and the AUC was 0.702. Multivariate logistics regression analysis indicated that T₂WI (OR = 1.004, 95% CI: 1.001-1.006, P=0.001) and CE-T₁WI (OR=0.393, 95% CI: 0.206-0.748, P=0.004) skewness parameters were independent predictors of 1p/19q co-deletion in low-grade glioma.Conclusions Texture analysis based on conventional MRI can effectively predict the co-deletion of 1p/19q in low-grade glioma.