To predict ATRX mutation status in patients with lower-grade gliomas using radiomic analysis. Cancer Genome Atlas (TCGA) patients with lower-grade gliomas were randomly allocated into training (n = 63) and validation (n = 32) sets. An independent external-validation set (n = 91) was built based on the Chinese Genome Atlas (CGGA) database. After feature extraction, an ATRX-related signature was constructed. Subsequently, the radiomic signature was combined with a support vector machine to predict ATRX mutation status in training, validation and external-validation sets. Predictive performance was assessed by receiver operating characteristic curve analysis. Correlations between the selected features were also evaluated. Nine radiomic features were screened as an ATRX-associated radiomic signature of lower-grade gliomas based on the LASSO regression model. All nine radiomic features were texture-associated (e.g. sum average and variance). The predictive efficiencies measured by the area under the curve were 94.0 %, 92.5 % and 72.5 % in the training, validation and external-validation sets, respectively. The overall correlations between the nine radiomic features were low in both TCGA and CGGA databases. Using radiomic analysis, we achieved efficient prediction of ATRX genotype in lower-grade gliomas, and our model was effective in two independent databases. ATRX in lower-grade gliomas could be predicted using radiomic analysis. The LASSO regression algorithm and SVM performed well in radiomic analysis. Nine radiomic features were screened as an ATRX-predictive radiomic signature. The machine-learning model for ATRX-prediction was validated by an independent database.