Original Article
A nomogram for predicting lymph node metastasis in surgically resected T1 esophageal squamous cell carcinoma
Abstract
Background: Endoscopic therapies for T1 esophageal carcinoma have been increasingly used around the world. However, the procedures are limited by without lymph nodes harvested. The risk of lymph node metastasis (LNM) should been established. Our objective was to construct a nomogram model for predict risks of LNM in patients with pT1 esophageal squamous cell carcinoma (ESCC).
Methods: We reviewed the records of 221 patients with pT1 ESCC who underwent surgical resection and radical lymphadenectomy. Clinicopathological variables were analyzed univariate and multivariate logistic regression analysis. A nomogram for predicting risk of LNM was constructed and validated using bootstrap resampling.
Results: Of the 221 patients, 53 patients had been examined as LNM. Following multivariate analysis, poor differentiation (P=0.0006), lymphovascular invasion (P<0.0001) and SM3 (tumor invades the lower third of the submucosal layer) (P=0.0192) cancer were significantly independent risk factors for LNM and were entered into the nomogram. The nomogram showed a robust discrimination, with an area under the receiver operating characteristic curve (AUC) of 0.8667. The calibration curves for the probability of LNM showed optimal agreement between the probability as predicted by the nomogram and the actual probability.
Conclusions: We established a nomogram that can provide individual predicting for LNM in T1 ESCC, and this model has the potential clinical utility in making therapeutic procedures.
Methods: We reviewed the records of 221 patients with pT1 ESCC who underwent surgical resection and radical lymphadenectomy. Clinicopathological variables were analyzed univariate and multivariate logistic regression analysis. A nomogram for predicting risk of LNM was constructed and validated using bootstrap resampling.
Results: Of the 221 patients, 53 patients had been examined as LNM. Following multivariate analysis, poor differentiation (P=0.0006), lymphovascular invasion (P<0.0001) and SM3 (tumor invades the lower third of the submucosal layer) (P=0.0192) cancer were significantly independent risk factors for LNM and were entered into the nomogram. The nomogram showed a robust discrimination, with an area under the receiver operating characteristic curve (AUC) of 0.8667. The calibration curves for the probability of LNM showed optimal agreement between the probability as predicted by the nomogram and the actual probability.
Conclusions: We established a nomogram that can provide individual predicting for LNM in T1 ESCC, and this model has the potential clinical utility in making therapeutic procedures.
