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