Whole brain morphologic features improve the predictive accuracy of IDH status and VEGF expression levels in gliomas

Glioma is a systemic disease that can induce micro and macro alternations of whole brain. Isocitrate dehydrogenase and vascular endothelial growth factor are proven prognostic markers and antiangiogenic therapy targets in glioma. The aim of this study was to determine the ability of whole brain morphologic features and radiomics to predict isocitrate dehydrogenase status and vascular endothelial growth factor expression levels. This study recruited 80 glioma patients with isocitrate dehydrogenase wildtype and high vascular endothelial growth factor expression levels, and 102 patients with isocitrate dehydrogenase mutation and low vascular endothelial growth factor expression levels. Virtual brain grafting, combined with Freesurfer, was used to compute morphologic features including cortical thickness, LGI, and subcortical volume in glioma patient. Radiomics features were extracted from multiregional tumor. Pycaret was used to construct the machine learning pipeline. Among the radiomics models, the whole tumor model achieved the best performance (accuracy 0.80, Area Under the Curve 0.86), while, after incorporating whole brain morphologic features, the model had a superior predictive performance (accuracy 0.82, Area Under the Curve 0.88). The features contributed most in predicting model including the right caudate volume, left middle temporal cortical thickness, first-order statistics, shape, and gray-level cooccurrence matrix. Pycaret, based on morphologic features, combined with radiomics, yielded highest accuracy in predicting isocitrate dehydrogenase mutation and vascular endothelial growth factor levels, indicating that morphologic abnormalities induced by glioma were associated with tumor biology.