A Generalizable Multimodal Model for Treatment‐Stratified Risk and Survival Assessment under Real‐World Constraints: A Multi‐Center Study of Colorectal Cancer

Integrating histopathology, genomic, and clinical phenotypes holds promise for improving prognostic patient stratification, including within treatment-defined subgroups, in colorectal cancer (CRC). However, ensuring the availability of all modalities in routine clinical practice remains challenging. Comprehensive molecular profiling is often constrained by cost and turnaround time, leaving histopathology as the most consistently available modality across institutions. In addition, variability in staining protocols, scanning devices, patient demographics, and outcome distributions across centers undermines the generalizability of models trained under controlled conditions with complete inputs. To address these challenges, we developed the Domain-Adaptive Incomplete Multimodal Stratification framework (DAIMS). DAIMS leverages paired histopathology, genomic, and clinical data during training to learn generalizable disease representations and uses histology-derived surrogate latent representations when auxiliary modalities are unavailable, enabling flexible histology-only inference. Trained on TCGA samples and validated on two independent European and East Asian cohorts comprising 842 patients, DAIMS consistently outperformed state-of-the-art methods. Adapted DAIMS improved the C-index by 5.6%-13.9% on F1CRC and 4.1%-9.0% on SURGEN compared with baseline methods. DAIMS improved within-cohort prognostic discrimination and localized prognostically relevant morphological patterns that remained stable across institutions. Our evaluation showed that DAIMS achieved generalizable survival stratification across disease stages, molecular subgroups, and treatment-defined patient groups.