Development and Validation of Nomograms to Predict Overall Survival Outcomes in Serous Ovarian Cancer Patients with Satisfactory Cytoreductive Surgery and Chemotherapy

OBJECTIVE: Nomograms are statistics-based predictive tools that integrate predictive factors. Herein, a nomogram was developed and validated to predict the overall survival (OS) in serous ovarian cancer (SOC). METHODS: Primary SOC patients with satisfactory cytoreductive surgery, chemotherapy, and OS ≥1 month were included in this study. A total of 6957 patients from the Surveillance, Epidemiology, and End Results (SEER) database comprised the training group and 1244 patients comprised the external validation group. The nomogram was structured on Cox models and evaluated in both the training and validation groups using consistency index, area under the receiver operating characteristics curve, calibration plots, and risk subgroup classification. Kaplan–Meier curves were plotted to compare the survival outcomes between subgroups. A decision-curve analysis was used to test the clinical value of the nomogram. RESULTS: Independent factors, including age, tumor grade, and Federation of Gynecology and Obstetrics (FIGO) stage, identified by multivariate analysis in the training cohort, were selected for the nomogram. The consistency indexes for OS were 0.689 in the training cohort and 0.639 in the validation cohort. The 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 that our nomogram was superior to the American Joint Committee on Cancer (AJCC) staging system. CONCLUSION: A nomogram was constructed to predict the long-term OS in SOC and verified in Asians. The accurate predictions facilitated personalized treatments and follow-up strategies.