Adaptive Survival Prediction via Multimodal Case-Level Evidence Reliability

Multimodal survival models combine complementary evidence from computed tomography (CT), radiomics, and clinical records, but the trustworthiness of that evidence varies across patients. Standard fusion rules infer modality weights from content alone and can therefore conflate semantic relevance with data quality, missingness, and prediction-derived certainty. We introduce Case-Adaptive Evidence Reliability (CAER), a modular framework that separates these two roles. Each modality produces an independently supervised discrete-time survival distribution. A reliability channel—which does not access the evaluated patient’s outcome at inference—combines modality availability, an explicit observable-quality proxy, and distributional certainty. Reliability then acts as a bounded residual to the content gate; exact masking assigns zero weight to unavailable evidence and preserves a patient-level audit trail. On the locked split of the 422-patient NSCLC-Radiomics cohort, CAER obtains the best values among six statistical, tree-based, and neural survival baselines for Harrell and Uno concordance, 1/2/3-year time-dependent AUC, and integrated Brier score. Controlled ablations favor the complete relevance–reliability decomposition over content-only fusion and simpler reliability controls. The results support explicit case-level reliability as a useful inductive bias for multimodal survival prediction.