This study aimed to establish and validate a radiomics signature-based nomogram with robust radiomics features from contrast enhanced MRI images. The radiomics features were selected using LASSO regression. A prediction model was constructed with multivariate logistic regression analysis. A nomogram combined radiomics signature and clinical factors were established, showing good performance for predicting the TERT mutation status. The clinical value of radiomics nomogram was further assessed by the prognosis analysis. In conclusion, the radiomics signature-based nomogram is a promising method for preoperatively predicting TERT promoter mutation status and has the potential to assess prognosis noninvasively in glioblastoma patients.