Nomograms for predicting risk of locoregional recurrence and distant metastases for esophageal cancer patients after radical esophagectomy

• Main Authors: Wen-Yi Zhang, Xing-Xing Chen, Wen-Hao Chen, Hui Zhang, Chang-Lin Zou
• Format: Article
• Language: English
• Published: BMC 2018-09-01
• Series: BMC Cancer
• Subjects: Nomogram; Esophageal cancer; Risk prediction; Locoregional recurrence; Distant metastases
• Online Access: http://link.springer.com/article/10.1186/s12885-018-4796-5

Abstract Background The aim of this study was to develop nomograms for predicting the risk of locoregional recurrence or distant metastasis in esophageal cancer patients who were treated with esophagectomy and regional lymphadenectomy. Methods The clinicopathologic data of 408 esophageal cancer patients after esophagectomy and regional lymphadenectomy were analyzed in this study. Univariate and multivariate COX regression analyses were used to test the association between the clinicopathologic data and the risk of locoregional recurrence or distant metastasis. The nomograms were built from the COX regression model. Results Univariate analyses revealed that tumor length, tumor width, T-staging and perineural invasion(PNI) were significantly associated with locoregional recurrence, and that tumor length, tumor width, differentiation, T-staging, N-staging, lymph vascular space invasion(LVSI), PNI and adjuvant chemotherapy were significantly associated with distant metastasis. Multivariate analyses revealed that tumor length, tumor width and T-staging were predictors of risk of locoregional recurrence, and that differentiation, N-staging, LVSI and PNI were predictors of risk of distant metastasis. Two nomograms were constructed for a visual explanation of these two COX regression models. The bias-corrected curve showed no significant departure from the ideal curve in these two nomograms. Conclusions Two nomograms were developed and validated to predict the risk of locoregional recurrence and distant metastasis in esophageal cancer patients after radical esophagectomy. The calculation outcome will help oncologists to choose adjuvant treatment regimens.