Original Article
Development and validation of a nomogram for survival benefit of lymphadenectomy in resected gallbladder cancer
Abstract
Background: Due to absence of large, prospective, randomized, clinical trial data, the potential survival benefit of lymphadenectomy with different number of regional lymph nodes (LNs) remains controversial. We aim to create a predicting model to help estimate individualized potential survival benefit of lymphadenectomy with more regional LNs for patients with resected gallbladder cancer (GBC).
Methods: Patients with resected GBC were selected from the Surveillance, Epidemiology, and End Results database who were diagnosed between 2004 and 2014. Covariates included age, race, sex, grade, histological stage, tumor sizes and receipt of non-primary surgery. Two types of multivariate survival regression models were constructed and compared. The best model performance was tested by the external validation data from our hospital.
Results: A total of 1,669 patients met the inclusion criteria for this study. The lognormal survival model showed the best performance and was tested by the external validation data, including 193 patients with resected GBC from our hospital. Nomograms, which based on the accelerated failure time parametric survival model, were built to estimate individualized survival. C-index, was up to 0.754 and 0.710 in internal validation for more and less regional LNs removed, respectively. Both of internal and external calibration curves showed good agreement between predicted and observed outcomes in the 1-, 3-, and 5-year overall survival (OS).
Conclusions: A predicting model can be used as a decision model to predict which patients may obtain benefit from lymphadenectomy with more regional LNs.
Methods: Patients with resected GBC were selected from the Surveillance, Epidemiology, and End Results database who were diagnosed between 2004 and 2014. Covariates included age, race, sex, grade, histological stage, tumor sizes and receipt of non-primary surgery. Two types of multivariate survival regression models were constructed and compared. The best model performance was tested by the external validation data from our hospital.
Results: A total of 1,669 patients met the inclusion criteria for this study. The lognormal survival model showed the best performance and was tested by the external validation data, including 193 patients with resected GBC from our hospital. Nomograms, which based on the accelerated failure time parametric survival model, were built to estimate individualized survival. C-index, was up to 0.754 and 0.710 in internal validation for more and less regional LNs removed, respectively. Both of internal and external calibration curves showed good agreement between predicted and observed outcomes in the 1-, 3-, and 5-year overall survival (OS).
Conclusions: A predicting model can be used as a decision model to predict which patients may obtain benefit from lymphadenectomy with more regional LNs.
