Glioblastoma (GBM) is a highly aggressive brain tumor with poor prognosis, motivating the development of more accurate survival prediction models that can integrate complementary clinical and molecular information. However, existing multimodal survival frameworks often rely on simplified genomic summaries, underuse sequence-context information from mutations, or discard global spatial structure in whole-slide histopathology. In this study, we present MUSA , a multimodal survival framework that integrates three data sources: whole-slide H&E histopathology, tabular clinical and gene expression variables, and language-model-derived mutation features. For histopathology, we use a class-map-based feature extraction pipeline that summarizes slide-level composition, spatial adjacency, and fragmentation patterns. For the molecular branch, we represent missense mutations using protein-sequence-context-based features derived from a language model and evaluate both frozen and survival-supervised representations. These modality-specific features are then fused within a survival prediction framework and evaluated under nested cross-validation. In mutation-only experiments, survival-supervised language-model features outperformed one-hot and VEP-derived baselines. In multimodal benchmark comparisons, MUSA outperformed matched reference models, and in ablation analyses the full trimodal model achieved the best overall median concordance. Together, these results show that mutation-sequence representations and spatial histopathology features provide complementary prognostic information for GBM survival modeling.
