Brain areas related to mathematical abili- ties in children have been mainly assessed through their activation in fMRI, while volume-based analysis have been employed in sMRI to discover structural differ- ences. However, a recent technique in precision medicine allows to enhance the sMRI analysis by extracting a large number of features, also called radiomics, related to shape, intensity and texture from specific areas. In the present study, a structural neuroimaging analysis based on radiomics and machine learning models is pre- sented with the aim of identifying brain areas related to different mathematical tests. A total of 77 school- aged children from third to sixth grade were adminis- tered four mathematical tests: Math Fluency, Calcu- lation, Applied Problems and Concepts as well as a structural brain imaging scan. The results confirmed and extended the involvement of brain areas found in sMRI and fMRI literature such as the frontal, parietal, temporal and occipital cortex, as well as basal ganglia and limbic system areas. For these areas, texture fea- tures were the most informative while volume represented less than 15% of the shape information. These findings emphasize the potential of radiomics for a more in-depth analysis of medical images for the identifi- cation of brain areas related to mathematical perfor- mance. The code used to obtain these results can be found at github.com/vicmancr/MathBrainRadiomics.