Min Zhu1, Bin Cao2, Xiao Li1, Peng Li1, Zixian Wen3, Jiafu Ji3, Li Min1, Shutian Zhang1. 1. Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Diseases, Beijing Digestive Disease Center, Beijing Key Laboratory for Precancerous Lesion of Digestive Diseases, Beijing, China. 2. Department of Endocrinology, Beijing Key Laboratory of Diabetes Research and Care, Center for Endocrine Metabolism and Immune Diseases, Lu He Hospital, Capital Medical University, Beijing, China. 3. Department of Gastrointestinal Surgery, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, China.
Abstract
BACKGROUND AND AIM: No predictive model for lymph node metastasis (LNM) of superficial esophagogastric junction (EGJ) cancer exists. This study aimed to evaluate incidence, identify risk factors, and develop a predictive nomogram for LNM in patients with superficial EGJ cancers. METHODS: Data were extracted from the Surveillance, Epidemiology, and End Results database for model development and internal validation. Another data set was obtained from two hospitals for external validation. A nomogram was developed based on independent risk factors that resulted from a multivariate logistic regression analysis. Internal and external validations were performed to assess the performance of nomogram model by receiver operating characteristic and calibration plot. RESULTS: Prevalence of LNM was 11.41% for intramucosal cancer and increased to 26.50% for submucosal cancer. On the multivariate analysis, large tumor size (odds ratio [OR] = 1.42; P < 0.001), moderately and poorly/un-differentiated pathological type (OR = 5.62 and 7.67; P = 0.024 and 0.008, respectively), and submucosal invasion (OR = 2.73; P = 0.004) were independent risk factors of LNM. The nomogram incorporating these three predictors demonstrated good discrimination (area under the estimated receiver operating characteristic curve [AUC]: 0.74; 95% confidence interval [95%CI]: 0.68, 0.80) and calibration (mean absolute error was 0.012). Moreover, the discrimination in the internal and external validation sets was good (AUC: 0.73 [95%CI: 0.66, 0.81] and 0.74 [95%CI: 0.60, 0.89], respectively). Nomogram provided better clinical usefulness as assessed by a decision curve analysis. CONCLUSIONS: Prevalence of LNM in superficial EGJ cancer was high. The first risk-predictive nomogram model for LNM of superficial EGJ cancer may help clinicians to decide optimal treatment option preoperatively.
BACKGROUND AND AIM: No predictive model for lymph node metastasis (LNM) of superficial esophagogastric junction (EGJ) cancer exists. This study aimed to evaluate incidence, identify risk factors, and develop a predictive nomogram for LNM in patients with superficial EGJ cancers. METHODS: Data were extracted from the Surveillance, Epidemiology, and End Results database for model development and internal validation. Another data set was obtained from two hospitals for external validation. A nomogram was developed based on independent risk factors that resulted from a multivariate logistic regression analysis. Internal and external validations were performed to assess the performance of nomogram model by receiver operating characteristic and calibration plot. RESULTS: Prevalence of LNM was 11.41% for intramucosal cancer and increased to 26.50% for submucosal cancer. On the multivariate analysis, large tumor size (odds ratio [OR] = 1.42; P < 0.001), moderately and poorly/un-differentiated pathological type (OR = 5.62 and 7.67; P = 0.024 and 0.008, respectively), and submucosal invasion (OR = 2.73; P = 0.004) were independent risk factors of LNM. The nomogram incorporating these three predictors demonstrated good discrimination (area under the estimated receiver operating characteristic curve [AUC]: 0.74; 95% confidence interval [95%CI]: 0.68, 0.80) and calibration (mean absolute error was 0.012). Moreover, the discrimination in the internal and external validation sets was good (AUC: 0.73 [95%CI: 0.66, 0.81] and 0.74 [95%CI: 0.60, 0.89], respectively). Nomogram provided better clinical usefulness as assessed by a decision curve analysis. CONCLUSIONS: Prevalence of LNM in superficial EGJ cancer was high. The first risk-predictive nomogram model for LNM of superficial EGJ cancer may help clinicians to decide optimal treatment option preoperatively.