A Multidimensional Prognostic Model for Gallbladder Cancer Based on a Multicenter Cohort Integrating Clinicopathological Features, Systemic Inflammation, and Tumor Biomarkers

Background The anatomical TNM staging system inadequately reflects the profound survival heterogeneity in gallbladder cancer (GBC), thereby limiting accurate risk stratification and contributing to suboptimal clinical decision-making. To overcome this limitation, we developed a multidimensional and dynamic prognostic framework integrating clinicopathological features, systemic inflammatory markers, and tumor biomarkers, leveraging large-scale multicenter real-world data. Methods A total of 1,354 patients with GBC from 44 medical centers were retrospectively analyzed and randomly assigned to training (n = 947) and validation (n = 407) cohorts. Independent prognostic factors were identified using LASSO and multivariable Cox regression to construct three risk scores: clinicopathological (C-score), blood-based inflammatory (B-score), and tumor marker (T-score). An integrated prognostic model was subsequently developed and evaluated through machine-learning–based benchmarking. Model performance was assessed using time-dependent ROC analysis (6–48 months), calibration curves, and decision curve analysis (DCA). Gene Set Enrichment Analysis (GSEA) was performed to explore the biological relevance of the scoring systems. Results Age, R0 resection status, TNM stage, CA19-9 (log1p), neutrophil-to-lymphocyte ratio (NLR), and lymphocyte-to-CRP ratio (LCR) were identified as independent prognostic factors. The integrated model consistently outperformed individual C-, B-, and T-scores across all follow-up intervals, demonstrating strong discriminative ability with a 12-month AUC of 0.857 in both cohorts. Calibration and decision curve analyses confirmed good model reliability and clinical utility. GSEA revealed distinct molecular associations underlying the three scores, including ECM–receptor interaction, immune-inflammatory signaling (JAK–STAT/NF-κB), and metabolic stress pathways (HIF-1/p53). A web-based dynamic prediction platform was further developed to enable individualized survival estimation. Conclusion This multidimensional framework provides a biologically interpretable and dynamically adaptable tool for prognostic stratification in gallbladder cancer. Implemented through a web-based platform, the model facilitates individualized risk assessment and supports data-driven clinical decision-making.