AUTHOR=Zhang Meng , Li Ruiping , Zhang Shan , Xu Xin , Liao Lixin , Yang Yan , Guo Yuzhen 

TITLE=Analysis of prognostic factors of metastatic endometrial cancer based on surveillance, epidemiology, and end results database

JOURNAL=Frontiers in Surgery

VOLUME=Volume 9 - 2022

YEAR=2023

URL=https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2022.1001791

DOI=10.3389/fsurg.2022.1001791

ISSN=2296-875X

ABSTRACT=To investigate the prognostic risk factors for survival of patients with metastatic endometrial cancer and to construct and validate a reliable predictive model. Methods Patients with first diagnosis of metastatic EC from 2010 to 2015 in the US Surveillance, Epidemiology, and End Results (SEER) database were retrospectively analyzed and Cox proportional fractional risk models were applied to assess the impact of clinical variables on patients' overall survival (OS) and tumor-specific survival ( The predictive model was constructed and its ability was assessed using the consistency index (C-index), the Receiver Operating Characteristic Curve ROC, and calibration curves. Kaplan-Meier comparisons of survival outcomes between risk subgroups were also plotted. Results A total of 3878 patients were included in this study. Univariate analysis showed that age, race, marital status, type of pathology, Grade classification, T-stage, N-stage, surgery, radiotherapy, chemotherapy, bone metastases, brain metastases, liver metastases and lung metastases were significantly associated with OS and CSS in patients with metastatic EC (P-value<0.05). Multifactorial analysis showed that age, race, type of pathology, Grade classification, T-stage, N-stage, surgery, radiotherapy, chemotherapy, brain metastases, liver metastases and lung metastases were independent risk factors for OS and CSS (P-value<0.05). We combined the multifactorial results to construct nomograms. The C-indexes of the OS and CSS nomogram graphs were 0.749 (95% CI 0.739～0.760) and 0.746 (95% CI 0.736～0.756), 0.730 (95% CI 0.714～0.746) and 0.728 (95% CI 0.712～0.744) for the training and validation cohorts, respectively. The ROC curves and calibration curves show that our model has better predictive accuracy and clinical utility. The differences in Kaplan-Meier curves between risk subgroups were statistically significant (P-value<0.05). Conclusions This study was based on the SEER database to establish a nomogram to predict the survival time of patients with metastatic endometrial cancer, which was helpful for clinical evaluation of the prognosis of patients.