Abstract
BACKGROUND: Immunotherapy offers potential benefits for recurrent or metastatic cervical cancer (R/M CC), yet personalized predictive tools are essential for optimizing treatment. This study aims to develop a nomogram integrating nutritional-inflammatory biomarkers and clinical features to predict survival in R/M CC patients undergoing immune checkpoint inhibitor (ICI) therapy. PATIENTS AND METHODS: We retrospectively analyzed 98 R/M CC patients treated with ICIs. Overall survival (OS) was the primary endpoint. Demographic characteristics and peripheral blood biomarkers before and after ICI treatment were collected. Univariate analysis screened potential variables, followed by LASSO regression to select key biomarkers and compute the Risk-Score. Prediction models combining clinical features and the Risk-Score were evaluated using ROC curves and decision curve analysis (DCA). The optimal nomogram model was developed and validated with ROC curves, calibration plots, and DCA. RESULTS: Three models were established: (i) a clinical model based on age and squamous cell carcinoma antigen (SCC-Ag), (ii) a Risk-Score model, and (iii) a combined model integrating age, SCC-Ag, and Risk-Score. The combined model showed superior predictive performance. A nomogram incorporating age, stage, SCC-Ag, and Risk-Score predicted 6-month, 1-year, and 2-year survival probability, with respective AUC values of 0.892, 0.868, and 0.846. Calibration curves and DCA affirmed high predictive accuracy and clinical utility. CONCLUSION: The nomogram integrating nutritional-inflammatory biomarkers and clinical parameters may serve as a promising tool for predicting survival in R/M CC patients undergoing ICI therapyand may help guide individualized treatment strategies following further validation.