Abstract
BACKGROUND: Ovarian cancer is one of the top seven causes of cancer deaths. Incidence of ovarian cancer varies by ethnicity, where Asian women demonstrate lower incidence rates than non-Hispanic Blacks and Whites. Survival prediction models for ovarian cancer have been developed for Caucasians and Black populations using national databases; however, whether these models work for Asians is unclear. Therefore, a retrospective cohort study was conducted to develop survival prediction models for patients with epithelial ovarian cancer from a Taiwan Cancer Registry (TCR) who underwent de-bulking and chemotherapy, with the aim to identify variables that can predict prognosis accurately. Patients diagnosed with OC from TCR were included. METHOD: Two prognostic models (M1 and M2) were developed: M1 utilized clinical variables only, M2 additionally included cancer-specific variables with the aim to improve the accuracy. All methods were repeated independently for patients with only serous ovarian cancer. All findings for model M1 were validated among Black, White, and Asian populations from Surveillance, Epidemiology, and End Results (SEER) database and 10-fold internal cross-validations. Due to absence of cancer-specific site variables in SEER, model M2 was only internally validated. Cox-proportional hazards regression analysis was performed and a stepwise strategy with Akaike-information criterion was used to select appropriate variables as predictors to develop both M1 and M2. RESULTS: The c-index values of both models were >0.7 in both TCR and SEER populations for epithelial ovarian cancer. Calibration analysis demonstrated good prediction performance with the proportional difference between predicted and observed survival to be <5%. The performance was similar for the subset of patients with serous epithelial ovarian cancer. Notably, no significant racial differences were observed. CONCLUSION: The prognostic models proposed in this study can potentially be used for identifying patients, especially from Taiwan, at higher risk of ovarian cancer mortality early on, leading to improved prognosis, through shared decision-making between physicians and patients.