陆军军医大学学报 (Mar 2024)
Construction of survival prediction model for metastatic advanced endometrial cancer based on the SEER database
Abstract
Objective To investigate the association between clinical features and survival in distant metastatic endometrial carcinoma (EC) patients, and to construct a prognostic risk model to guide the clinical practice. Methods The clinical data of EC patients with distant metastases were retrospectively collected from the SEER database between 2010 and 2017. Univariate and multivariate Cox regression analyses were applied to determine the independent prognostic factors of metastatic EC patients, and then a nomogram model was constructed based on these predictive factors. The predictive performance of the model was evaluated and internally validated using C-index, receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA). In addition, the model was externally validated using the data collected from the EC patients with distant metastases admitted to the First Affiliated Hospital of Chongqing Medical University between October 2013 and December 2019. Results A total of 1 807 EC patients with distant metastases who met the inclusion and exclusion criteria in the SEER database were enrolled in this study. Multivariate Cox analysis revealed that tumor size, grade, and histological type were independent risk factors for distant metastatic EC; and race, surgery, and chemotherapy were independent protective factors for metastatic distant EC. In the training cohort, the C-index of the constructed nomogram model was 0.711 (95%CI: 0.695~0.727); in the internal validation cohort, the C-index of the model was 0.744 (95%CI: 0.721~0.768); ROC curve analysis indicated that the model had good prediction and discrimination; calibration curve showed that there was a high consistency between the predicted survival probability and the actual survival probability; meanwhile, the results of DCA curves revealed that the model had great potential for clinical utility. Furthermore, based on the 21 EC patients with distant metastases included as an external validation dataset, the results showed that the model had good predictive ability. Conclusion A survival prediction model for distant metastatic EC is established, and it can assist clinicians in evaluating the prognosis of patients and performing personalized clinical decisions for this specific population.
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