Abstract
BACKGROUND: The aim of this study was to explore the influence of preoperative tumor volume, length, maximum diameter and the number of postoperative pathologically lymph node metastasis (LNM) regions on survival prognosis of esophageal squamous cell carcinoma (ESCC) patients. METHODS: A total of 296 patients with ESCC treated by standard curative esophagectomy were retrospectively analyzed. These patients were grouped for further analysis according to the optimal threshold of preoperative tumor volume, length, maximum diameter and the number of postoperative pathologically LNM regions. Kaplan-Meier method was used to calculate survival rate and survival comparison was performed by Log rank test. The Cox proportional hazards model was used to carry out univariate and multivariate analyses. Nomogram model was established by integrating statistically significant clinicopathological parameters, and the predictive value was further verified by calibration curve, concordance index (C-index) and decision curve. RESULTS: The univariate and multivariate Cox regression analysis all showed that differentiation grade, TNM stage, adjuvant therapy, preoperative tumor volume and the number of post operative pathologically LNM regions were independent prognostic factors for PFS and OS (all P<0.05). The C-indexes of PFS and OS by nomograms were predicted to be 0.747 (95% CI: 0.717-0.777) and 0.732 (95% CI: 0.697-0.767), respectively, which were significantly higher than the 7th AJCC TNM stage, the C-indexes 0.612 (95% CI: 0.574-0.650) and 0.633 (95% CI: 0.595-0.671), separately. In addition, the calibration curves of nomogram models were highly consistent with actual observation for the five-year PFS and OS rate, and the decision curve analysis also showed that nomogram models had higher clinical application potentials than TNM staging model in predicting survival prognosis of thoracic ESCC after surgery. CONCLUSION: The nomograms incorporated preoperative tumor volume and the number of postoperative pathologically LNM areas are of great value in predicting survival prognosis of patients with thoracic ESCC.