Radiomics based multi-variate models and state-of-art convolutional neural networks (CNNs) have demonstrated their usefulness for predicting IDH genotype in gliomas from MRI images. However, adaptability and clinical explanability of these models on unseen multi-center datasets has not been investigated. Our work trains radiomics and CNN based classifiers on a large dataset (TCIA) and tests multiple local datasets. Results demonstrate higher adaptability of radiomics than standard CNNs, except for transfer learned CNNs. Better interpretability was obtained from feature ranking (in case of radiomics) and high resolution class activation maps (in case of CNNs).