Abstract
BACKGROUND: Ovarian endometrioid carcinoma (OEC) is the second most commonly occurring ovarian epithelial malignancy, but the associated prognostic factors remain obscure. This study aimed to analyze independent prognostic factors for patients with OEC and to develop and validate a nomogram to predict the overall survival (OS) of these patients. METHODS: Clinical information of patients with OEC (2000-2019) was obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox analyses were used to identify independent prognostic factors, and nomogram models were constructed using independent prognostic factors. Receiver operating characteristic (ROC) curve, calibration plots, and decision curve analysis (DCA) were used to verify the accuracy and validity of the nomogram. Kaplan-Meier curves were used to compare the differences in OS and cancer-specific survival (CSS) among subgroups. RESULTS: A total of 4628 patients with OEC were included, being divided into training (n = 3238) and validation (n = 1390) sets (7:3 ratio). On multivariate Cox analysis, AJCC stage, age, tumor size, differentiation, chemotherapy, and lymph node resection were significant predictors of survival outcomes (P < 0.05). Resection of 1-3 lymph nodes in early-stage OEC patients did not significantly prolong OS (P > 0.05), but resection of ≥ 4 lymph nodes in early-stage improved OS and CSS (P < 0.05). The OS of early-stage patients was not related to whether or not they received chemotherapy (P > 0.05). Lymph node resection and chemotherapy significantly improved the prognosis of patients with advanced OEC (P < 0.05). The c-index of nomogram prediction model was 0.782. ROC with good discrimination, calibration plots with high consistency, and DCA with large net benefit rate result in large clinical value. CONCLUSION: AJCC stage, differentiation, tumor size, age, chemotherapy, and lymph node dissection were prognostic factors of OEC. The constructed nomogram prediction model can effectively predict the prognosis of OEC patients and improve the accuracy of clinical decision-making.