Nomogram for Predicting Survival Post-Immune Therapy in Cholangiocarcinoma Based on Inflammatory Biomarkers - Jianan Jin MMed; Haibo Mou MD; Yibin Zhou MMed; Shiqi Zhang MMed (Cancer Control)
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.