Integration of Systemic Inflammation Response Index (SIRI) and clinicopathological factors enhances survival prediction in colorectal cancer: A retrospective cohort study

Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide. The current tumor-node-metastasis (TNM) staging systems exhibit limited prognostic accuracy because they do not account for host inflammatory responses. This study aimed to develop and validate a novel prognostic nomogram integrating clinicopathological features with systemic inflammatory biomarkers to improve survival prediction in patients with CRC after radical resection. In this retrospective cohort study, clinical data from 324 CRC patients undergoing surgery (January 2010-March 2020) were analyzed. Preoperative hematological indices (including neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, fibrinogen-to-albumin ratio, derived neutrophil-to-lymphocyte ratio, mean corpuscular volume/lymphocytes, Systemic Inflammation Response Index, Systemic Immune-Inflammation Index, Prognostic Nutritional Index, Cumulative Inflammatory Index, Prognostic Immune and Nutritional Index, hemoglobin, albumin, lymphocyte, and platelet) and clinicopathological variables were assessed. Variable selection was conducted using univariate Cox analysis, least absolute shrinkage and selection operator regression (Lambda.1se), and multivariate Cox analysis. The final model was constructed as a nomogram and validated for 1-, 3-, and 5-year overall survival predictions using receiver operating characteristic analysis, calibration curves, and decision curve analysis. Multivariate analysis identified 4 independent prognostic factors: N stage (N1: hazard ratio [HR] = 2.72, 95% confidence interval [CI]: 1.51-4.89, P < .001; N2: HR = 5.26, 95% CI: 2.60-10.67, P < .001), vascular invasion (HR = 6.02, 95% CI: 3.79-9.58, P < .001), perineural invasion (HR = 2.02, 95% CI: 1.29-3.16, P = .002), and SIRI ≥ 2.39 (HR = 2.03, 95% CI: 1.33-3.09, P < .001). The nomogram demonstrated significantly superior prognostic accuracy compared with conventional TNM staging, with excellent calibration in both the training and validation cohorts. Decision curve analysis confirmed the significant net clinical benefits of the nomogram. Risk stratification revealed significantly divergent survival rates between the high- and low-risk groups (P < .001). This inflammatory-clinicopathological nomogram improves prognostic accuracy over TNM staging, enabling personalized risk assessment. SIRI integration highlights systemic inflammation's critical role.