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Prediction Model for Overall Survival in Patients with Epithelial Ovarian Cancer Undergoing Surgery and Systemic
Hongtang Shi1, Shaowei Zhan1, Yujiao An2
1Department of Gynecology, Binzhou Medical University Hospital, Binzhou, Shandong, 256600, People's Republic of China.
Purpose:
Epithelial ovarian cancer (EOC) is a common gynecological malignancy, and accurate survival prediction after treatment remains challenging. Our aim was to construct a nomogram that effectively evaluates the overall survival (OS) of patients with EOC after surgery and systemic therapy.
Patients And Methods:
Patients with EOC who underwent surgery and systemic therapy and were enrolled in the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2021 were divided into training and test sets in a 7:3 ratio for internal validation. Univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis were used to screen independent risk factors and construct a nomogram to predict OS. Model discrimination was assessed using the time-dependent area under the receiver operating characteristic curve (AUC). Calibration curves were plotted to evaluate agreement between predicted and observed survival, and clinical utility was assessed using decision curve analysis (DCA).
Results:
A total of 17,285 EOC patients who underwent surgery and systemic therapy were included, with a median follow-up of 69 months. During follow-up, 4799 patients had died and 7282 remained alive. The final model included thirteen variables: age, race, marital status, histological classification, tumor size, CA125, T/N stage, cancer summary stage, FIGO stage, time from diagnosis to treatment, treatment sequence, and the extent of regional lymph node dissection. The 5-year AUC of the prediction model was 0.77 (95% CI: 0.76-0.78) in the training cohort and 0.77 (95% CI: 0.75-0.79) in the test cohort. The Brier scores ranged from 0.051 to 0.193 in training and test sets. These results indicate that the model possesses acceptable discriminatory capability and stability.
Conclusion:
We developed a nomogram based on clinical variables to provide individualized estimation of OS in EOC patients who underwent surgery and systemic therapy.
