Abstract
BACKGROUND: Inflammation-related biomarkers, such as systemic inflammation score (SIS) and neutrophil-lymphocyte ratio (NLR), are associated with colorectal cancer prognosis. However, the combined role of SIS, NLR, and clinicopathological factors in stage II/III colorectal cancer remains unclear. This study developed a nomogram to predict long-term prognosis for these patients. METHODS: This retrospective study included 1540 patients (training set) from the First Affiliated Hospital of Kunming Medical University and 152 patients (testing set) from The Honghe Third People's Hospital. Cox regression identified independent prognostic factors, and machine learning established predictive models. Model performance was evaluated by the C-index, area under the curve (AUC), and decision curve analysis (DCA). RESULTS: In the training set, a total of 1540 patients with stage II/III colorectal cancer were included. More than 70 years old (HR = 1.830, p = 0.000); SIS = 2 (HR = 1.693, p = 0.002); Preoperative CEA more than 5 ng/mL (HR = 1.614, p = 0.000); and Moderately differentiated (HR = 1.438, p = 0.011); or Low/undifferentiated (HR = 2.126, p = 0.000); The pN1 (HR = 2.040, p = 0.000) and pN2 (HR = 3.297, p = 0.000) stages were considered independent prognostic risk factors of stage II/III colorectal cancer. Negative perineural invasion (HR = 0.733, p = 0.014) and NLR less than 4 (HR = 0.696, p = 0.022) were considered independent prognostic protective factors of stage II/III colorectal cancer. A nomogram was established based on SIS, NLR, and the clinicopathological results for predicting and validating the overall survival in the training and testing sets. The C-index of the training set was 0.746, and the C-index of the testing set was 0.708, indicating the high prediction efficiency of the nomogram. CONCLUSIONS: A nomogram combining SIS, NLR, and clinicopathological factors provides an effective, cost-efficient tool for predicting the prognosis of stage II/III colorectal cancer. Future studies will validate its long-term predictive performance in larger, multicenter cohorts.