Can structural MRI radiomics predict DIPG histone H3 mutation and patient overall survival at diagnosis time?

Radiomics was proposed to identify tumor phenotypes noninvasively from quantitative imaging features. The present study aimed at investigating if radiomic features measured at diagnosis time from structural MRI can predict histone H3 mutations and overall survival of patients with diffuse intrinsic pontine glioma. To this end, 316 radiomic features from multimodal diagnostic MRI of 38 patients were extracted, and three clinical parameters were added. Two approaches for computing radiomic features were proposed: A global estimation from a spherical region of interest defined inside the tumor and a local estimation where features are computed inside the previously defined region from fixed size spherical patches and the mean of these features is considered. A feature selection pipeline was then developed. Three machine learning models for H3 mutation classification and three regression models for overall survival prediction were used. Leave-one-out F1-weighted scores for SVM model combining imaging and clinical features reached 0.83, showing a good prediction of H3 mutation using structural MRI. Results on overall survival prediction are not conclusive and suggest the need of a larger number of patients.