Risk factors for distant metastasis and prognosis in stage T1 esophageal cancer: A population-based study

Abstract

PurposeStage T1 esophageal cancer (EC) with distant metastasis (DM) is rare and poorly understood. In this study, we aimed to construct and validate a novel nomogram for predicting the probability of DM in T1 EC patients.MethodsA total of 1,663 eligible T1 EC patients were enrolled from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. The patients were randomly divided into training and validation cohorts. Univariate and multivariate logistic analyses in the training cohort were used to identify risk factors related to DM, and then these risk factors were applied to construct the nomogram. Receiver operating characteristic (ROC) curves, the area under the curve (AUC), calibration plots, the Hosmer-Lemeshow (HL) test, and decision curve analysis (DCA) were used to evaluate the nomogram.ResultsAmong the 1,663 patients identified, 143 (8.6%) had DM. Five risk factors (tumor location, lymph node status, tumor length, T1 subtype, and grade) were significant predictors of DM. The AUC values were 0.828 and 0.851 in the training cohort and validation cohort, respectively, revealing good discrimination. The calibration plots in the training cohort and validation cohort both showed good consistency. DCA showed that the nomogram was clinically effective. In addition, the nomogram has a good risk stratification ability to identify patients with different risks according to the nomogram score. In terms of survival analysis, univariate and multivariate Cox analyses showed that age, race, tumor length, grade, lymph node status, M stage and treatment were significant prognostic factors for overall survival (OS). For cancer-specific survival (CSS), the independent prognostic factors were age, tumor length, histology, grade, lymph node status, M stage and treatment.ConclusionThe nomogram could effectively predict the probability of DM in T1 EC patients. It can aid clinicians in detecting high-risk patients and making individual clinical decisions.