Muscular injury as an independent risk factor for esophageal stenosis after endoscopic submucosal dissection of esophageal squamous cell cancer

Prediction of post-ESD esophageal stricture by a nomogram and risk factor analysis of ineffective oral steroids prophylaxis

BACKGROUND AND AIMS

Several risk models for esophageal stricture after endoscopic submucosal dissection have been developed. However, some of them did not include the use of steroids in the risk analysis. Glucocorticoid sensitivity mediated by glucocorticoid receptor expression has not been discussed in this condition.

METHODS

Clinical and endoscopic characteristics were included in the logistic regression model to establish a nomogram for stenosis prediction. The score for each risk factor was estimated. Risk factors of ineffective oral steroid prophylaxis were analyzed and glucocorticoid receptor expressions were detected by immunohistochemistry.

RESULTS

Three hundred fourteen patients of endoscopic submucosal dissection for esophageal superficial neoplasms were included to develop the nomogram. The circumferential range(≤ 3/4, 3/4-1 or the whole circumference), longitudinal diameter reached 4 cm (yes or not) and lesion location (the cervical and upper thoracic part, the middle thoracic part or the lower thoracic part) consisted of the nomogram. Patients have a high risk of esophageal stricture if they have a total point greater than 36. In the simplified risk score model, the corresponding cutoff score was 1. 92 patients with oral steroid prophylaxis were separately analyzed and the circumferential mucosal defect involving 7/8 or more was an independent risk factor of ineffective prevention (OR 12.2, 95%CI 5.27-28.11). The expression of glucocorticoid receptor β was higher in the stricture group (p = 0.042 for AOD; p = 0.016 for the scoring system).

CONCLUSIONS

We established a nomogram for esophageal stricture prediction. Depending on the characteristics of lesions, it is possible to estimate the risk of stricture under routine post-ESD treatments (no steroids or oral steroids). Alternative treatments should be considered if the risk is extremely high, especially for patients with mucosal defects involving 7/8 or more of circumference in which oral steroid treatment tends to be ineffective. The higher glucocorticoid receptor β may indicate potential glucocorticoid resistance.

Enhancing Patient Outcomes: A Novel Nomogram Prediction Model Based on Systemic Immune-Inflammation Index for Esophageal Stricture After Endoscopic Submucosal Dissection

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.