Our study demonstrated that a combination of shape- and texture-based radiomic features played a pivotal role in distinguishing IDH1-mutant from wild-type gliomas, reinforcing the growing clinical relevance of radiomics-driven machine learning models. The gene discussed is IDH1; the disease is central nervous system cancer.