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In our recent research, we have effectively demonstrated the feasibility of classifying magnetic resonance images (MRI) of glial tumors into four histological types utilizing standardized volume of interest (VOI), radiomics and machine learning. This research aims to determine the reproducibility of our approach when the locations of VOI are changed. We were able to demonstrate high reproducibility of ML results when the same feature selection methodology was employed across different VOIs. However, the reproducibility of radiomic features and their sets among various VOIs was not ensured for the sample size (n = 85) studied. The limited reproducibility of radiomic features should be taken into account when evaluating radiomics studies in glial tumors. [ABSTRACT FROM AUTHOR]