Nomogram for Predicting Survival Post-Immune Therapy in Cholangiocarcinoma Based on Inflammatory Biomarkers.

Abstract

Background: Immune therapy, especially involving PD-1/PD-L1 inhibitors, has shown promise as a therapeutic option for cholangiocarcinoma. However, limited studies have evaluated survival outcomes in cholangiocarcinoma patients treated with immune therapy. This study aims to develop a predictive model to evaluate the survival benefits of immune therapy in patients with cholangiocarcinoma.

Methods: This retrospective analysis included 120 cholangiocarcinoma patients from Shulan (Hangzhou) Hospital. Univariate and multivariate Cox regression analyses were conducted to identify factors associated with survival following immune therapy. A predictive model was constructed and validated using calibration curves (CC), decision curve analysis (DCA), concordance index (C-index), and receiver operating characteristic (ROC) curves.

Results: Cox regression analysis identified several factors as potential predictors of survival post-immune therapy in cholangiocarcinoma: treatment cycle (<6 vs ≥ 6 months, 95% CI: 0.119-0.586, P = 0.001), neutrophil-to-lymphocyte ratio (NLR <3.08 vs ≥ 3.08, 95% CI: 1.864-9.624, P = 0.001), carcinoembryonic antigen (CEA <4.13 vs ≥ 4.13, 95% CI: 1.175-5.321, P = 0.017), and presence of bone metastasis (95% CI: 1.306-6.848, P = 0.010). The nomogram model achieved good predictive accuracy with a C-index of 0.811. CC indicated strong concordance between the predicted and observed outcomes. Multi-timepoint ROC curves at 1, 2, and 3 years validated the model's performance (1-year AUC: 0.906, 2-year AUC: 0.832, 3-year AUC: 0.822). The multi-timepoint DCA curves also demonstrated a higher net benefit compared to extreme curves.

Conclusion: The nomogram model, incorporating key risk factors for cholangiocarcinoma patients post-immune therapy, demonstrates robust predictive accuracy for survival outcomes, offering the potential for improved clinical decision-making.