Glioblastoma and brain solitary metastasis from lung cancer have similar peritumoral edema on T2-weighted imaging (T2WI). However, indistinguishable signs between these two tumors embarrass the radiologists and lead to high misdiagnosis rate. To address such issue, radiomics biomarkers were analyzed to detail the tumors’ histologic and morphologic characteristics. Results indicated that radiomics biomarkers including histogram of oriented gradient, shape and grey level co-occurrence matrix, which charaterize the lesion’s shape and signal showed good performance in differentiating these two tumors. Furthermore, using those radiomics biomarkers, a gradient-boosting machine learning model was established and showed good performance (Area under the curve=0.88).