A predictive model for advanced esophageal cancer involving the lower third of the esophagus.

Background: Esophageal cancer (EC) is one of the most common malignant tumors worldwide, which has severely threatened human health. This study aims to evaluate the prognostic factors and predictors of survival in patients diagnosed with advanced lower third esophageal carcinoma (aLEC). Based on the Surveillance, Epidemiology, and End Results (SEER) database, we developed a model (nomogram) to provide accurate and individualized survival prediction for the patients who have lost the opportunity to undergo radical surgery.

Methods: Using SEER database, the basic information and Medicare data of patients diagnosed with aLEC from 2010 to 2015 were collected. The patients were randomly divided into the training and validation set according to an 8:2 ratio. Univariate and multivariate Cox analyses were used to investigate variables significantly correlated with disease-specific survival (DSS). A nomogram was constructed to predict the prognosis of EC patients. We used the area under the curve (AUC) of the receiver operating characteristic (ROC) curve for the evaluation of performance. Furthermore, calibration curves were used to evaluate the accuracy of the model. The clinical utility was also assessed via decision curve analysis (DCA). Differences in clinicopathological characteristics between T1NanyM1 and T2-4NanyM1 stages were evaluated using the Chi-squared test. Cox regression analysis was performed and Kaplan-Meier curves were plotted to evaluate the impact of T-stage, chemotherapy, and radiotherapy on the survival time of EC patients.

Results: Results of multivariate regression analysis demonstrated that histology type, T stage, and chemotherapy were independent prognostic factors for predicting survival time in patients with aLEC. Notably, the constructed nomogram suggested that patients with stage T2 or T3 had a higher survival rate at 6 months, 1 year, and 2 years compared with those with stage T1. DCAs showed that the predictive nomogram was clinically useful. There were fewer patients with stage T1NanyM1 receiving chemotherapy (P=0.004) or radiotherapy (P<0.001) than patients with stage T2-4NanyM1. Moreover, patients with stage T1NanyM1 who underwent chemotherapy had a better prognosis than those who did not [hazard ratio (HR) 3.15, 95% confidence interval (CI): 2.58-3.83; P<0.001]. For patients with stage T1NanyM1, radiotherapy did not improve outcomes (HR 0.98, 95% CI: 0.82-1.17; P=0.80).

Conclusions: A prognostic nomogram integrating three clinicopathological factors was constructed to predict survival in aLEC patients. Chemotherapy improves outcomes of patients with stage T1NanyM1 aLEC.