biostudies-literatureUnknown195(10)Zhang BAccurate prognosis represents a critical component in oncology research, enabling personalized treatment planning and optimized health care resource use. Although existing prognostic models demonstrate promising performance on restricted data sets, they remain constrained by two limitations: modality-specific architectural designs and cancer type-specific training paradigms that hinder cross-domain generalization. To address these challenges, the Unified Multimodal Pan-Cancer Survival Network (UMPSNet) was introduced, which integrated histopathology images, genomic expression profiles, and four metadata categories through structured text templates. UMPSNet used the optimal transport-based attention for multimodal feature alignment and a guided mixture of expert mechanisms to address cancerThe American journal of pathology1869-1884https://www.ebi.ac.uk/biostudies/studies/S-EPMC12597545biostudies-literatureFrom Single-Cancer to Pan-Cancer Prognosis: A Multimodal Deep Learning Framework for Survival Analysis with Robust Generalization Capability.PMC12597545Ren XGuo LLi SMeng ZZhang BJian JZhao ZSu FfalseFrom Single-Cancer to Pan-Cancer Prognosis: A Multimodal Deep Learning Framework for Survival Analysis with Robust Generalization Capability.Accurate prognosis represents a critical component in oncology research, enabling personalized treatment planning and optimized health care resource use. Although existing prognostic models demonstrate promising performance on restricted data sets, they remain constrained by two limitations: modality-specific architectural designs and cancer type-specific training paradigms that hinder cross-domain generalization. To address these challenges, the Unified Multimodal Pan-Cancer Survival Network (UMPSNet) was introduced, which integrated histopathology images, genomic expression profiles, and four metadata categories through structured text templates. UMPSNet used the optimal transport-based attention for multimodal feature alignment and a guided mixture of expert mechanisms to address cancer2025-01-01T00:00:00Z2025 Oct2026-05-17T03:19:43.666Z2026-05-17T03:12:44.878ZS-EPMC125975454065154010.1016/j.ajpath.2025.06.006