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We separately constructed an MRI habitat prediction model, a WSI Transformer\u2010based deep learning pathomics (PDL) model, and a Combined model. A dynamic nomogram web page for predicting high\u2010risk molecular subtypes was developed based on the Combined model. Patients were stratified into risk groups according to the output scores of the Combined model, and Kaplan\u2013Meier survival analysis and the Log\u2010rank test were employed to evaluate survival differences between the groups. Additionally, differential expression and GO\/KEGG enrichment analyses were further performed in the test set with available RNA\u2010seq data to explore transcriptional features and biological processes associated with model\u2010based risk stratification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The Combined model demonstrated the highest AUC (Training set: 0.888, Test set: 0.836) compared to the Habitat model (Training set: 0.832, Test set: 0.798) and the PDL model (Training set: 0.852, Test set: 0.821). The high\u2010risk and low\u2010risk groups, stratified based on the cutoff value derived from the Combined model output scores, exhibited significant survival differences. Exploratory transcriptomic analysis showed that differentially expressed genes between the high\u2010 and low\u2010risk groups were mainly enriched in biological processes and pathways related to the extracellular matrix and cell\u2013matrix interactions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The cross\u2010scale Combined model not only enabled identification of high\u2010risk molecular subtypes and risk stratification but also showed associations with biologically relevant transcriptional features.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cns.71171","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T14:27:46Z","timestamp":1790605666000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrating Multimodal\n                    <scp>MRI<\/scp>\n                    Habitat and Transformer\u2010Based Pathomics to Predict High\u2010Risk Molecular Subtypes and Explore Biological Mechanisms in Adult Diffuse Gliomas"],"prefix":"10.1002","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-8272-5725","authenticated-orcid":false,"given":"Wenju","family":"Niu","sequence":"first","affiliation":[{"name":"Department of Radiology First Hospital of Shanxi Medical University  Taiyuan Shanxi Province China"},{"name":"College of Medical Imaging Shanxi Medical University  Taiyuan Shanxi Province China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Duan","sequence":"additional","affiliation":[{"name":"Department of Radiology First Hospital of Shanxi Medical University  Taiyuan Shanxi Province China"},{"name":"College of Medical Imaging Shanxi Medical University  Taiyuan Shanxi Province 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