ArticleCancer medicine2024
Enhancing Patient Outcomes: A Novel Nomogram Prediction Model Based on Systemic Immune-Inflammation Index for Esophageal Stricture After Endoscopic Submucosal Dissection.
Article in Cancer medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Risk Prediction Models of Esophageal Strictures After Endoscopic Submucosal Dissection: A Systematic Review and Meta-Analysis.Digestive diseases and sciences · 2025Pooled it
- Advances in the treatment of postoperative anastomotic strictures in esophageal cancer a comparative analysis of the efficacy of endoscopic versus interventional dilatation.Langenbeck's archives of surgery · 2026Review
- Correlation between sarcopenia and esophageal stenosis following endoscopic submucosal dissection and construction of a postoperative stenosis risk model.World journal of gastroenterology · 2026Article
- Concurrent pathological scar: independent risk factor for esophageal stricture after endoscopic submucosal dissection.Surgical endoscopy · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
backgroundEndoscopic 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
methodsThis 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).
resultsROC 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.
conclusionWe 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.