This paradigm exemplifies the biological interpretability of machine-learning based radiomics compared to non-feature based methods and illustrates how it can be used to re-evaluate categorical classifications and improve characterisation and understanding of the pathological continuum in glioma grades, in particular with emerging novel, validated molecular biomarkers, such as CDKN2A/B deletion in IDH-mutant astrocytomas, which have been suggested to form a new clinical risk group [3]. The gene discussed is CDKN2A; the disease is central nervous system cancer.