Abstract
PURPOSE: This study aimed to evaluate the nutritional, immune, and inflammatory status of esophageal squamous cell carcinoma (ESCC) patients before immunotherapy using blood-based biomarkers, and to develop and validate competing-risk nomogram models to predict overall survival (OS) and progression-free survival (PFS). PATIENTS AND METHODS: This retrospective study enrolled 300 ESCC patients who received immunotherapy at the First Affiliated Hospital of Zhengzhou University between January 2021 and December 2023, and randomly assigned them to a primary cohort (210 cases) and a validation cohort (90 cases) at a 7:3 ratio. We screened prognostic factors using least absolute shrinkage and selection operator (LASSO) regression coupled with univariate and multivariate Cox regression analyses to construct a nomogram prediction model. Model validation was performed using the following approaches: (1) Calibration curves were employed to evaluate the agreement between predicted and observed outcomes; (2) Discrimination was assessed through the index of concordance (C-index), time-dependent receiver operating characteristic (ROC) curves, and time-dependent area under the curve (AUC); (3) Clinical utility and predictive accuracy were further assessed using net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA). Furthermore, the performance of the proposed nomogram was systematically compared with the conventional tumor-node-metastasis (TNM) staging system and another published prediction model. RESULTS: Through comprehensive multivariate OS analysis conducted in the primary cohort, we established an innovative prognostic nomogram combining systemic immunoinflammatory index (SII) and prognostic nutritional index (PNI). Compared with TNM staging, our model demonstrated strong accuracy, discriminative ability, and clinical utility in the primary and validation cohorts. CONCLUSION: This research confirms the prognostic value of SII and PNI in ESCC patients undergoing anti-PD-1 immunotherapy. Given their significant correlation with clinical outcomes, these biomarkers show potential for pretreatment risk assessment in immunotherapy candidates.