Cancer medicine · 2026
Identification of Glioma Phenotypic Subtypes From Multimodal MRI Data Using Hierarchical Multi-Kernel Learning.
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Yan Junyu, Hao Min, Wang Tong, Yang Qi, Jia Congcong, Niu Wenju, Tan Yan, Zhang Hui, Cao Hongyan, Yang Guoqiang
Аннотация
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.
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.