Artificial intelligence (AI) enabled radiomics has evolved immensely\nespecially in the field of oncology. Radiomics provide assistancein diagnosis\nof cancer, planning of treatment strategy, and predictionof survival. Radiomics\nin neuro-oncology has progressed significantly inthe recent past. Deep learning\nhas outperformed conventional machinelearning methods in most image-based\napplications. Convolutional neu-ral networks (CNNs) have seen some popularity\nin radiomics, since theydo not require hand-crafted features and can\nautomatically extract fea-tures during the learning process. In this regard, it\nis observed that CNNbased radiomics could provide state-of-the-art results in\nneuro-oncology,similar to the recent success of such methods in a wide spectrum\nofmedical image analysis applications. Herein we present a review of the most\nrecent best practices and establish the future trends for AI enabled radiomics\nin neuro-oncology.\n