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We introduce a transparent, end\u2010to\u2010end workflow that fuses available whole\u2010slide histology and RNA\u2010seq to discover clinically meaningful glioblastoma subgroups using an unsupervised learning model after feature extraction. Haematoxylin\u2013eosin slides are tiled, tissue\u2010screened and stain\u2010normalised; tiles are embedded with a pretrained ResNet\u201050 to yield 2048\u2010dimensional features, averaged per patient and compressed to 30\u2010D by an autoencoder. In parallel, RNA\u2010seq (~48\u2009k genes) undergoes low\u2010variance filtering and normalisation, then a second autoencoder produces a 30\u2010D transcriptomic embedding. The two 30\u2010D representations are concatenated into a 60\u2010D fused vector, robustly scaled and refined with PCA (\u224898% variance retained). Across\n                    <jats:italic>K<\/jats:italic>\n                    \u2010means, Gaussian mixture models and Agglomerative clustering (\n                    <jats:italic>k<\/jats:italic>\n                    \u2009=\u20092\u201320), Agglomerative\n                    <jats:italic>k<\/jats:italic>\n                    \u2009=\u20092 was decisively best (mean silhouette \u22480.53), yielding clusters of 150 and 8 patients (survival subset 147 and 8). Survival separation was substantial (median 454 vs. 138\u2009days; log\u2010rank\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20090.0096). In Cox models, the poorer\u2010prognosis cluster showed increased risk (HR\u2009\u2248\u20092.70), which remained significant after age adjustment (HR\u2009=\u20092.15, 95% CI 1.04\u20134.46; age per year HR\u2009=\u20091.02, 95% CI 1.01\u20131.04). Attribution and consensus analyses yielded compact, interpretable gene sets (22 shared; 8 per cluster), including markers associated with NOTCH\/\u03b3\u2010secretase and oxidative phosphorylation. These findings nominate biologically plausible hypotheses for future validation rather than immediate treatment\u2010selection rules. Overall, this study demonstrates that auditable late fusion of histology and transcriptomics, built from routine data, can identify survival\u2010associated glioblastoma subgroups and provides a hypothesis\u2010generating framework for prospective, harmonised, multi\u2010centre validation.\n                  <\/jats:p>","DOI":"10.1002\/cam4.72182","type":"journal-article","created":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T05:46:56Z","timestamp":1786427216000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning\u2010Based Multimodal Fusion of Whole\u2010Slide Images and\n                    <scp>RNA<\/scp>\n                    Sequencing Identifies Survival\u2010Relevant Glioblastoma Clusters"],"prefix":"10.1002","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1906-9900","authenticated-orcid":false,"given":"Amin","family":"Zadeh Shirazi","sequence":"first","affiliation":[{"name":"Centre for Cancer Biology Adelaide University  Adelaide South Australia Australia"},{"name":"Department of Computer Engineering, Faculty of Engineering Khayyam University  Mashhad Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0494-2404","authenticated-orcid":false,"given":"Guillermo A.","family":"Gomez","sequence":"additional","affiliation":[{"name":"Centre for Cancer Biology Adelaide University  Adelaide South Australia 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