Enhancing Patient Outcomes: A Novel Nomogram Prediction Model Based on Systemic Immune‐Inflammation Index for Esophageal Stricture After Endoscopic Submucosal Dissection - Chen Wang; Mengqiu Tang; Dawei Chen; Yang Zhou; Gaofeng Liang; Ruiwei Shen; Tian Chen (Cancer Medicine)
ABSTRACT Background Endoscopic submucosal dissection (ESD) is a widely utilized treatment for early esophageal cancer. However, the rising incidence of postoperative esophageal stricture poses a significant challenge, adversely affecting patients' quality of life and treatment outcomes. Developing precise predictive models is urgently required to enhance treatment outcomes. Materials and Methods This study retrospectively analyzed clinical data from 124 patients with early esophageal cancer who underwent ESD at Ningbo Medical Center Lihuili Hospital. Patients were followed up to assess esophageal stricture incidence. Binary logistic regression analysis was used to identify factors associated with post‐ESD esophageal stricture. A novel nomogram prediction model based on Systemic Immune‐inflammation Index (SII) was constructed and evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results ROC curve analysis showed that the optimal value of SII for predicting esophageal stricture was 312.67. Both univariate and multivariate analyses identified lesion infiltration depth (< M2 vs. ≥ M2, p = 0.002), lesion longitudinal length (< 4 cm vs. ≥ 4 cm, p = 0.008), circumferential resection range (< 0.5, 0.5–0.75, ≥ 0.75, p = 0.014), and SII (< 312.67 vs. ≥ 312.67, p = 0.040) as independent risk factors for post‐ESD esophageal stricture. A novel nomogram prediction model incorporating these four risk factors was developed. Validation using ROC curve analysis demonstrated satisfactory model performance, while calibration curves indicated good agreement between model‐predicted risk and observed outcomes. Conclusion We successfully constructed a novel nomogram prediction model based on SII, which can accurately and intuitively predict the occurrence of esophageal stricture after ESD, providing guidance for clinicians and improving treatment outcomes.