Development and validation of a nomogram for predicting recurrence in epithelial ovarian cancer after primary cytoreductive surgery: A single-center retrospective study

Abstract

Objective: To explore the risk factors for recurrence of epithelial ovarian cancer (EOC) after primary cytoreductive surgery, and to develop and validate a risk prediction model based on the identified factors.

Methodology: Clinical data of 213 patients with epithelial ovarian cancer (EOC) who underwent primary cytoreductive surgery (CRS) in Huzhou Maternity & child Health Carel Hospital from January 2020 to July 2025 were retrospectively collected. Patients were randomly divided into a training (n=128) and a validation cohort (n=85) at a ratio of 6:4. The Least Absolute Shrinkage and Selection Operator (LASSO) method and multivariate logistic regression were used for feature analysis of the training cohort, which was further converted into a nomogram risk model for postoperative recurrence. The discrimination and calibration abilities of the nomogram were validated in both cohorts using the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) and the Hosmer-Lemeshow (H-L) test.

Results: The EOC recurrence rate was 40.8% (87/213). The analysis identified six independent risk factors for predicting recurrence, including International Federation of Gynecology and Obstetrics (FIGO) stage, differentiation grade, residual lesions, positive ascites cytology, carbohydrate antigen 125 (CA125) positive, and positive Risk of Ovarian Malignancy Algorithm (ROMA) index. The nomogram model demonstrated sufficient predictive accuracy, with AUC values of 0.861 (95% CI: 0.795-0.928) and 0.801 (95% CI: 0.697-0.904) in the training and validation cohorts, respectively. The results of the H-L test showed good fitness.

Conclusions: The nomogram model developed in this study exhibits good predictive performance and applicability, and can be used to screen for the risk of recurrence in this population.