BACKGROUND: The purpose of this study was to develop a nomogram that can be used to predict lymph node metastasis (LNM) in patients with endometrial cancer (EC). METHODS: The clinical data of EC patients diagnosed between 2004 and 2015 were retrieved from the Surveillance, Epidemiology, and End Results Program (SEER) registry. The nomogram was constructed using independent risk factors chosen by a multivariate logistic regression analysis. Accuracy was validated for both groups using discrimination analysis and calibration curves. RESULTS: The final study group consisted of 63,836 women that met specific inclusion criteria. The factors that were identified in the multivariate analysis to be significant predictors of LNM were age, tumor size, histological type, myometrial invasion, cervical stromal invasion, and tumor grade in training group (N=42,558). These variables were included in the nomogram. Discriminations of the nomogram and Mayo criteria were 0.848 (95% CI: 0.843-0.853) and 0.806 (95% CI: 0.801-0.812), respectively. In the validation group (N=21,278), the AUC values were 0.847 (95% CI: 0.840-0.857) and 0.804 (95% CI: 0.796-0.813) for the nomogram and the Mayo criteria, respectively (P<0.01). Calibration plots showed that training and validation cohorts were well-calibrated. CONCLUSIONS: A nomogram was developed to predict LNM in EC patients based on a large population-based analysis. The nomogram showed good performance for predicting LNM in patients with EC. This convenient predictive tool may help clinicians to formulate suitable individualized treatment. 2021 Annals of Translational Medicine. All rights reserved.
BACKGROUND: The purpose of this study was to develop a nomogram that can be used to predict lymph node metastasis (LNM) in patients with endometrial cancer (EC). METHODS: The clinical data of EC patients diagnosed between 2004 and 2015 were retrieved from the Surveillance, Epidemiology, and End Results Program (SEER) registry. The nomogram was constructed using independent risk factors chosen by a multivariate logistic regression analysis. Accuracy was validated for both groups using discrimination analysis and calibration curves. RESULTS: The final study group consisted of 63,836 women that met specific inclusion criteria. The factors that were identified in the multivariate analysis to be significant predictors of LNM were age, tumor size, histological type, myometrial invasion, cervical stromal invasion, and tumor grade in training group (N=42,558). These variables were included in the nomogram. Discriminations of the nomogram and Mayo criteria were 0.848 (95% CI: 0.843-0.853) and 0.806 (95% CI: 0.801-0.812), respectively. In the validation group (N=21,278), the AUC values were 0.847 (95% CI: 0.840-0.857) and 0.804 (95% CI: 0.796-0.813) for the nomogram and the Mayo criteria, respectively (P<0.01). Calibration plots showed that training and validation cohorts were well-calibrated. CONCLUSIONS: A nomogram was developed to predict LNM in EC patients based on a large population-based analysis. The nomogram showed good performance for predicting LNM in patients with EC. This convenient predictive tool may help clinicians to formulate suitable individualized treatment. 2021 Annals of Translational Medicine. All rights reserved.
Authors: Toni P Kilts; Gretchen E Glaser; Carrie L Langstraat; Amanika Kumar; Amy L Weaver; Michaela E Mc Gree; Bobbie S Gostout; Karl C Podratz; Sean C Dowdy; William A Cliby; Andrea Mariani; Jamie N Bakkum-Gamez Journal: Gynecol Oncol Date: 2019-08-10 Impact factor: 5.482
Authors: Roberto Vargas; J Alejandro Rauh-Hain; Joel Clemmer; Rachel M Clark; Annekathryn Goodman; Whitfield B Growdon; John O Schorge; Marcela G Del Carmen; Neil S Horowitz; David M Boruta Journal: Gynecol Oncol Date: 2014-02-16 Impact factor: 5.482
Authors: Pluvio J Coronado; Agnieszka Rychlik; Maria A Martínez-Maestre; Laura Baquedano; María Fasero; Aida García-Arreza; Sara Morales; Daniel M Lubian; Ignacio Zapardiel Journal: J Gynecol Oncol Date: 2018-01 Impact factor: 4.401