AUTHOR=Li Xingchen , Fan Yuan , Dong Yangyang , Cheng Yuan , Zhou Jingyi , Wang Zhiqi , Li Xiaoping , Wang Jianliu
TITLE=Development and Validation of Nomograms Predicting the Overall and the Cancer-Specific Survival in Endometrial Cancer Patients
JOURNAL=Frontiers in Medicine
VOLUME=7
YEAR=2020
URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2020.614629
DOI=10.3389/fmed.2020.614629
ISSN=2296-858X
ABSTRACT=Background: The present study was aimed at developing nomograms estimating the overall survival (OS) and cancer-specific survival (CSS) of endometrial cancer (EC)-affected patients.
Patients and Methods: We retrospectively collected 145,445 EC patients between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. Independent prognostic factors were identified via univariate and multivariate Cox analyses. These risk factors were used to establish nomograms to predict 3- and 5-year OS and CSS rates. Internal and external data were used for validation. The predictive accuracy and discriminative ability were measured by using concordance index (C-index) and risk group stratification.
Results: A total of 63,510 patients were collected and randomly assigned into the training cohort (n = 42,340) and the validation cohort (n = 21,170). Age at diagnosis, marital status, tumor size, histologic type, lymph node metastasis, tumor grade, and clinical stage were identified as independent prognostic factors for OS and CSS (p < 0.05 according to multivariate Cox analysis) and were further used to construct the nomograms. The area under the receiver operating characteristics (ROC) curve was greater than that of International Federation of Gynecology and Obstetrics (FIGO) staging system for predicting OS (0.83 vs. 0.73, p < 0.01) and CSS (0.87 vs. 0.79, p < 0.01) in the training cohort. The stratification into different risk groups ensured a significant distinction between survival curves within different FIGO staging categories.
Conclusion: We constructed and validated nomograms that accurately predicting OS and CSS in EC patients. The nomograms can be used for estimating OS and CSS of individual patients and establishing their risk stratification.