Epidemiologic characteristics and a prognostic nomogram for patients with vulvar cancer: results from the Surveillance, Epidemiology, and End Results (SEER) program in the United States, 1975 to 2016
- Author:
Shiyuan WEI
1
;
Lu LI
;
Tingting YI
;
Licong SU
;
Qi GAO
;
Liangzhi WU
;
Zhenbo OUYANG
Author Information
- Publication Type:Original Article
- From:Journal of Gynecologic Oncology 2023;34(6):e81-
- CountryRepublic of Korea
- Language:English
-
Abstract:
Objective:To elucidate clinical characteristics and build a prognostic nomogram for patients with vulvar cancer.
Methods:The study population was drawn from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly assigned to training and validation sets. Cox proportional hazards model and competing risk model were used to identify the prognostic parameters of overall survival (OS) and cancer-specific survival (CSS) to construct a nomogram. The nomogram was assessed by concordance index (C-index), area under the curve (AUC), calibration plot, and decision curve analysis (DCA).
Results:A total of 20,716 patients were included in epidemiological analysis, of whom 7,025 patients were selected in survival analysis, including 4,215 and 2,810 in training and validation sets, respectively. The multivariate Cox model showed that the predictors for OS were age, marital status, histopathology, differentiation and tumor node metastasis (TNM) stages, whether to undergo surgery and chemotherapy. However, the predictors for CSS were age, race, differentiation and TNM stages, whether to undergo surgery and radiation. The C-index for OS and CSS in the training set were 0.76 and 0.80. The AUC in the training set for 1-, 3- and 5-year OS and CSS were 0.84, 0.81, 0.80 and 0.88, 0.85, 0.83, respectively, which was similar in the validation set. The calibration curves showed good agreement between prediction and actual observations. DCA revealed that the nomogram had a better discrimination than TNM stages.
Conclusions:The nomogram showed accurate prognostic prediction in OS and CSS for vulvar cancer, which could provide guidance to clinical practice.
