Development and Validation of a Nomogram Based on CA125 Levels for Predicting Progression-free Survival in Patients with Ovarian Cancer

Background Ovarian cancer is the leading cause of death in patients with gynecological cancers, and it has very low survival. CA125 have been shown to be an important prognostic factor for PFS, Therefore, we aimed to develop and validate a nomogram incorporating CA125 levels after three cycles of chemotherapy for predicting progression-free survival (PFS) in patients with ovarian cancer.Methods The nomogram was developed in a primary cohort of 491 patients with stage II-IV ovarian cancer. Performance was assessed by concordance index (C-index), calibration curve, and decision curve analysis, and compared with the International Federation of Gynecology and Obstetrics (FIGO) staging system. The predictive value of CA125 levels after three cycles of chemotherapy was evaluated. The model was subjected to bootstrap internal validation. An independent cohort of 81 patients was used for external validation.Results CA125 levels after three cycles of chemotherapy were significantly associated with PFS. Five variables, including CA125 levels were selected to develop the nomogram. The nomogram demonstrated adequate discrimination, with a bootstrap-corrected C-index of 0.708, and good calibration. External validation of the nomogram achieved excellent discrimination (C-index, 0.724) and calibration. CA125 levels after three adjuvant chemotherapy cycles showed a marginally significant increment of discrimination to the nomogram in the primary cohort (C-index, 0.708 vs 0.668; P = 0.097). Superior discriminative ability was observed in the nomogram when compared with the FIGO staging system only in the primary cohort (C-index, 0.708 vs 0.578; P < 0.001). Decision curve analysis demonstrated that our nomogram was clinically useful.Conclusion We developed and validated a nomogram incorporating CA125 levels after three chemotherapy cycles for PFS prediction in ovarian cancer. This nomogram showed well-predictive performance and easy clinical application.