Development and Validation of Nomograms to Predict Long-term Oncological Outcomes in Serous Ovarian Cancer

Abstract

Background: Nomograms are statistics-based predictive tools that integrate predictive factors. We developed and validated a nomogram to predict overall survival (OS) in serous ovarian cancer (SOC).Methods: In total, 6957 patients from the SEER database were included in the training group; the external validation group included 1244 SOC patients from two Chinese hospitals. The nomogram was structured on Cox regression analyses and was evaluated in both the training and validation groups using consistency index, area under the receiver operating characteristics curve (AUC), calibration plots, and risk subgroup classification. Kaplan–Meier curves were plotted to compare survival outcomes between subgroups. A decision-curve analysis was used to test the clinical value of the nomogram.Results: The independent factors identified by multivariate analysis in the training cohort and selected for the nomogram included age, tumor grade, and FIGO stage. The consistency indexes for OS were 0.689 (95% confidence interval: 0.677–0.701) in the training cohort and 0.639 (95% confidence interval: 0.601–0.670) in the validation cohort; the AUCs were 0.675 and 0.661 in the validation cohorts, respectively. Calibration curves showed good consistency between predicted and actual 3- and 5-year OS. Significant differences were observed in the survival curves of different risk subgroups. The decision-curve analysis indicated our nomogram was superior to the AJCC staging system.Conclusion: We constructed a nomogram to predict long-term OS in SOC and externally verified it in an Asian population. This nomogram showed more accurate survival predictions, which will help provide personalized treatments and follow-up strategies.