A novel clinical nomogram for predicting cancer-specific survival in patients with non-serous epithelial ovarian cancer: A real-world analysis based on the Surveillance, Epidemiology, and End Results database and external validation in a tertiary center

Background: Currently, there is a lack of prognostic evaluation methods for non -serous epithelial ovarian cancer (EOC). Method: We collected patients with non -serous EOC diagnosed between 2010 and 2017 from the Surveillance, Epidemiology, and End Results (SEER) database into a training cohort (n = 2078) and an internal validation cohort (n = 891). Meanwhile, patients meeting the criteria were screened from the Fujian Provincial Maternal and Child Health Hospital from 2013 to 2022 as an external validation cohort (n = 56). Univariate and multivariable logistic regression were used to determine the independent prognostic factors of cancer -specific survival (CSS) to construct the nomogram. The nomogram was validated by the concordance index (C -index), receiver operating characteristics (ROC) curve and calibration curves. Result: Age, laterality, preoperative CA125 status, histologic type, tumor grade, AJCC stage, surgery lesion, number of lymph nodes examined, residual lesion size, and bone metastasis were identified as independent prognostic factors to construct the nomogram. The nomogram showed better predictive ability than FIGO stage through internal and external cohorts validation. The C -index of the nomogram in the training cohort, validation cohort, and external validation cohort were 0.831, 0.835 and 0.944 higher than those of the Federation International of Gynecology and Obstetric (FIGO) stage, P<0.05. The Area Under Curve (AUC) values results indicated great clinical usefulness of the nomogram. The calibration curve indicated good agreement between the nomogram prediction and actual survival. Conclusion: We developed a nomogram with high predictive accuracy to predict survival in patients with nonserous EOC.