Nomograms for predicting overall survival and cancer-specific survival of endometrioid ovarian carcinoma: A retrospective cohort study from the SEER database.

OBJECTIVE: Endometrioid ovarian cancer (EnOC) accounts for approximately 10%-15% of epithelial ovarian cancer cases. There are no effective tools for predicting the prognosis of EnOC in clinical work. The aim of this study was to construct and validate a nomogram to predict overall survival and cancer-specific survival (CSS) in patients with EnOC. METHODS: Data regarding patients diagnosed with primary EnOC between 2004 and 2019 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. LASSO Cox regression and Cox regression analyses were performed to screen for prognostic factors, which were used to construct nomograms. In addition, we performed subgroup analyses of the prognostic value of chemotherapy and lymph node surgery. RESULTS: In total, 3957 patients with primary EnOC were included in the analysis: 2770 in a training cohort and 1187 in a validation cohort. Age, stage, grade, lymph node surgery, and race were significantly and independently correlated with overall survival and CSS. Nomograms were constructed to predict 3- and 5-year overall survival and CSS. Nomograms have good predictive ability and clinical practicability. Subgroup analysis showed that lymph node surgery improved the prognosis of patients with EnOC (P < 0.05) except for patients with grade III-IV and Stage I disease (overall survival P = 0.272, CSS P = 0.624). Chemotherapy did not improve survival time in most patients (P > 0.05) except for patients with grade I-II and Stage II-IV disease (overall survival P = 0.008, CSS P = 0.046). CONCLUSION: We constructed predictive nomograms and a risk classification system to evaluate overall survival and CSS in EnOC patients. For most patients with EnOC, chemotherapy did not improve the prognosis. In contrast to chemotherapy, lymph node surgery improved prognosis in most patients with EnOC.