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Identifying glioma subtypes through non\u2010invasive methods could improve patient management.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      In this study, we applied hierarchical multi\u2010kernel 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                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009246). 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                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009144). 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\u2010KPLS), random forest, the least absolute shrinkage and selection operator, K\u2010Nearest Neighbor, and Na\u00efve Bayes, to predict isocitrate dehydrogenase (IDH) genotype from the FHSXMU\/SPPH dataset.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      We identified 2 glioma phenotypic subtypes, high\u2010risk and low\u2010risk groups. These groups showed significant differences in overall survival (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.05) and were associated with distinct signaling pathways. The JAK\u2013STAT and TGF\u2010\u03b2 pathways were activated in the high\u2010risk group, while the Hypoxia and p53 pathways were activated in the low\u2010risk group. Among the machine learning models, the GA\u2010KPLS model demonstrated the highest predictive performance for the IDH genotype, achieving an area under the curve of 0.819.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our study provides a non\u2010invasive method to identify glioma phenotypic subtypes, reveal distinct signaling pathways, and define therapeutically homogeneous patient subgroups that could guide targeted therapy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cam4.71572","type":"journal-article","created":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T01:02:27Z","timestamp":1769994147000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Identification of Glioma Phenotypic Subtypes From Multimodal\n                    <scp>MRI<\/scp>\n                    Data Using Hierarchical Multi\u2010Kernel Learning"],"prefix":"10.1002","volume":"15","author":[{"given":"Junyu","family":"Yan","sequence":"first","affiliation":[{"name":"Department of Radiology First Hospital of Shanxi Medical University  Taiyuan China"},{"name":"Academy of Medical Sciences Shanxi Medical University  Taiyuan China"},{"name":"Department of Health Statistics, Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, School of Public Health Shanxi Medical University  Taiyuan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Hao","sequence":"additional","affiliation":[{"name":"Department of Radiology First Hospital of Shanxi Medical University  Taiyuan China"},{"name":"College of Medical Imaging Shanxi Medical University  Taiyuan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tong","family":"Wang","sequence":"additional","affiliation":[{"name":"Academy of Medical Sciences Shanxi Medical University  Taiyuan China"},{"name":"Department of Health Statistics, Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, School of Public Health Shanxi Medical University  Taiyuan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Health Statistics, Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, School of Public Health Shanxi Medical University  Taiyuan China"},{"name":"MOE Key Laboratory of Coal Environmental Pathogenicity and Prevention Shanxi Medical University  Taiyuan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Congcong","family":"Jia","sequence":"additional","affiliation":[{"name":"Department of Health Statistics, Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, School of Public Health Shanxi Medical University  Taiyuan China"},{"name":"MOE Key Laboratory of Coal Environmental Pathogenicity and Prevention Shanxi Medical University  Taiyuan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenju","family":"Niu","sequence":"additional","affiliation":[{"name":"Department of Radiology First Hospital of Shanxi Medical University  Taiyuan China"},{"name":"College of Medical Imaging Shanxi Medical University  Taiyuan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Tan","sequence":"additional","affiliation":[{"name":"Department of Radiology First Hospital of Shanxi Medical University  Taiyuan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Radiology First Hospital of Shanxi Medical University  Taiyuan China"},{"name":"College of Medical Imaging Shanxi Medical University  Taiyuan China"},{"name":"Shanxi Key Laboratory of Intelligent Imaging and Nanomedicine First Hospital of Shanxi Medical University  Taiyuan 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