Deep Learning-Based Multimodal Fusion of Whole-Slide Images and RNA Sequencing Identifies Survival-Relevant Glioblastoma Clusters.

Glioblastoma is profoundly heterogeneous, and single-modality analyses often miss prognostically relevant structure. We introduce a transparent, end-to-end workflow that fuses available whole-slide histology and RNA-seq to discover clinically meaningful glioblastoma subgroups using an unsupervised learning model after feature extraction. Haematoxylin-eosin slides are tiled, tissue-screened and stain-normalised; tiles are embedded with a pretrained ResNet-50 to yield 2048-dimensional features, averaged per patient and compressed to 30-D by an autoencoder. In parallel, RNA-seq (~48 k genes) undergoes low-variance filtering and normalisation, then a second autoencoder produces a 30-D transcriptomic embedding. The two 30-D representations are concatenated into a 60-D fused vector, robustly scaled and refined with PCA (≈98% variance retained). Across K-means, Gaussian mixture models and Agglomerative clustering (k = 2-20), Agglomerative k = 2 was decisively best (mean silhouette ≈0.53), yielding clusters of 150 and 8 patients (survival subset 147 and 8). Survival separation was substantial (median 454 vs. 138 days; log-rank p = 0.0096). In Cox models, the poorer-prognosis cluster showed increased risk (HR ≈ 2.70), which remained significant after age adjustment (HR = 2.15, 95% CI 1.04-4.46; age per year HR = 1.02, 95% CI 1.01-1.04). Attribution and consensus analyses yielded compact, interpretable gene sets (22 shared; 8 per cluster), including markers associated with NOTCH/γ-secretase and oxidative phosphorylation. These findings nominate biologically plausible hypotheses for future validation rather than immediate treatment-selection rules. Overall, this study demonstrates that auditable late fusion of histology and transcriptomics, built from routine data, can identify survival-associated glioblastoma subgroups and provides a hypothesis-generating framework for prospective, harmonised, multi-centre validation.