Knowledge-enhanced pathology-genomics multimodal learning with uncertainty-guided fusion for cancer survival prediction

Multimodal cancer survival prediction draws on whole-slide pathology and transcriptomics, but it often treats predictive importance as evidence reliability, allowing poorly supported or conflicting observations to disproportionately affect patient-level estimates. This study presents Knowledge-enhanced Pathology–Pathway Survival (KP2Surv), a pathology–genomics framework that models patient-specific trust in evidence. Biological knowledge defines structured evidence units: spatial–phenotypic tissue groups form a pathology hypergraph, and curated molecular pathways form a pathway hypergraph. Separate token-importance and quality-risk estimates guide bidirectional pathology–pathway interaction. Pathology, pathway, and cross-modal branches predict continuous-time survival distributions, which are fused using predictive precision, learned reliability, and cross-branch disagreement. Across 4,263 patients in eight cohorts from The Cancer Genome Atlas (TCGA), KP2Surv achieved the highest numerical mean concordance index (C-index) among evaluated methods in every cohort; the unweighted mean was 0.7369 versus 0.7159 for the strongest comparator on average. Exact paired tests provided nominal evidence of improvement in four cohorts, while four comparisons were not statistically significant. Ablation and sensitivity analyses supported the selected evidence-trust components and default settings within the evaluated cohorts. Evaluation in two Clinical Proteomic Tumor Analysis Consortium (CPTAC) cohorts produced mean C-indices of 0.5987 for lung adenocarcinoma and 0.6143 for uterine corpus endometrial carcinoma, showing partial transportability but substantial attenuation under domain shift. Pathway-conditioned spatial attribution generated hypotheses linking molecular programs to localized tissue patterns. The findings support explicit modeling of evidence meaning and trustworthiness. Prospective validation and dedicated uncertainty evaluation remain necessary before clinical use.