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

Authors
  • Zhang, Wen-Yi
  • Chen, Xing-Xing
  • Chen, Wen-Hao
  • Zhang, Hui
  • Zou, Chang-Lin
Type
Published Article
Journal
BMC Cancer
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Sep 10, 2018
Volume
18
Issue
1
Identifiers
DOI: 10.1186/s12885-018-4796-5
Source
Springer Nature
Keywords
License
Green

Abstract

BackgroundThe 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.MethodsThe 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.ResultsUnivariate 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.ConclusionsTwo 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.

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