Constructing a prognostic survival model for patients with stage II-IV epithelial ovarian cancer: a study based on the SEER database and external validation in China

This study utilized the Surveillance, Epidemiology, and End Results (SEER) database to identify clinicopathological prognostic factors and develop a prognostic nomogram for predicting 1-, 3-, and 5-year overall survival (OS) in patients with stage II–IV epithelial ovarian cancer (EOC), aiming to enhance survival prediction accuracy and inform personalized therapeutic strategies. Clinical data of patients diagnosed between 2004 and 2020 were extracted and randomly divided into training, tuning, and internal validation cohorts at a 7:2:1 ratio. Univariate and multivariate Cox regression analyses identified age, tumor differentiation grade, AJCC stage, tumor size, number of positive lymph nodes, number of lymph nodes examined, surgery, chemotherapy, sequence of systemic therapy and surgery, and time from diagnosis to treatment as independent prognostic factors for OS (all P < 0.05). A prognostic nomogram was subsequently developed and externally validated using an independent cohort of 115 EOC patients from Chengde Medical University (2014–2020). The model exhibited acceptable discriminative performance, with concordance indexes (C-index) of 0.693, 0.683, and 0.700 for the training, internal validation, and external validation cohorts, respectively. Calibration curves, receiver operating characteristic (ROC) curves, and area under the curve (AUC) values all exceeded 0.7, indicating reliable prediction of 1-, 3-, and 5-year survival rates. Kaplan–Meier curves revealed significant survival differences across risk groups, and decision curve analysis (DCA) confirmed the clinical utility of the nomogram. In conclusion, this externally validated nomogram can assist in predicting OS in patients with stage II–IV EOC, demonstrating moderate yet clinically useful performance with a C-index of 0.700. It addresses the need for personalized prognostic assessment and aids in tailoring therapeutic strategies, thereby potentially improving patient outcomes.