Identification of Glioma Phenotypic Subtypes From Multimodal MRI Data Using Hierarchical Multi‐Kernel Learning

Background Gliomas are the most common primary brain tumors, exhibiting significant phenotypic variability even within the same grade. Identifying glioma subtypes through non‐invasive methods could improve patient management. Methods In this study, we applied hierarchical multi‐kernel learning to identify glioma phenotypic subtypes using MRI data (T1CE and T2FLAIR) from the First Hospital of Shanxi Medical University (FHSXMU) and Shanxi Provincial People's Hospital (SPPH) ( n = 246). We further validated our findings using an independent dataset of similar tumor characteristics from The Cancer Genome Atlas/The Cancer Imaging Archive (TCGA/TCIA) ( n = 144). Additionally, we analyzed pathway activity across glioma subtypes from the TCGA/TCIA dataset and employed five machine learning models, namely kernel partial least squares with the genetic algorithm (GA‐KPLS), random forest, the least absolute shrinkage and selection operator, K‐Nearest Neighbor, and Naïve Bayes, to predict isocitrate dehydrogenase (IDH) genotype from the FHSXMU/SPPH dataset. Results We identified 2 glioma phenotypic subtypes, high‐risk and low‐risk groups. These groups showed significant differences in overall survival ( p < 0.05) and were associated with distinct signaling pathways. The JAK–STAT and TGF‐β pathways were activated in the high‐risk group, while the Hypoxia and p53 pathways were activated in the low‐risk group. Among the machine learning models, the GA‐KPLS model demonstrated the highest predictive performance for the IDH genotype, achieving an area under the curve of 0.819. Conclusion Our study provides a non‐invasive method to identify glioma phenotypic subtypes, reveal distinct signaling pathways, and define therapeutically homogeneous patient subgroups that could guide targeted therapy.