Development and validation of a prognostic model based on SEER data for patients with esophageal carcinoma after esophagectomy

Abstract

To develop a nomogram to predict the long-term survival of patients with esophageal cancer following esophagectomy. We collected the data of 7215 patients with esophageal carcinoma from the Surveillance, Epidemiology, and End Results (SEER) database during the period from 2004 and 2016. Of these patients, 5052 were allocated to the training cohort and the remaining 2163 patients to the internal validation cohort using bootstrap resampling, with another 435 patients treated in the Department of Cardiothoracic Surgery of Jinling Hospital between 2014 and 2016 serving as the external validation cohort. In the overall cohort, the 1-, 3-, and 5-year cancer-specific mortality rates were 14.6%, 35.7% and 41.6%, respectively. Age (≥80 years vs < 50 years, P < 0.001), gender (male vs female, P < 0.001), tumor site (lower vs middle segment, P=0.013), histology (EAC vs ESCC, P=0.012), tumor grade (poorly vs well differentiated, P < 0.001), TNM stage (Ⅳ vs Ⅰ, P < 0.001), tumor size (> 50 mm vs 0-20 mm, P < 0.001), chemotherapy (yes vs no, P < 0.001), and LNR (> 0.25 vs 0, P < 0.001) were identified as independent risk factors affecting long-term survival of the patients. The nomograms established based on the model for predicting the survival probability of the patients at 1, 3 and 5 years after operation showed a C-index of 0.726 (95% CI: 0.714-0.738) for predicting the overall survival (OS) and of 0.735 (95% CI: 0.727-0.743) for cancer-specific survival (CSS) in the training cohort. In the internal validation cohort, the C-index of the nomograms was 0.752 (95% CI: 0.738-0.76) for OS and 0.804 (95% CI: 0.790-0.817) for CSS, as compared with 0.749 (95% CI: 0.736-0.767) and 0.788 (95%CI: 0.751-0.808), respectively, in the external validation cohort. The nomograms also showed a higher sensitivity than the TNM staging system for predicting long-term prognosis. This prognostic model has a high prediction efficiency and can help to identify the high-risk patients with esophageal carcinoma after surgery and serve as a supplement for the current TNM staging system.