Abstract
BACKGROUND: Despite high-grade serous ovarian carcinoma (HGSOC) being the most common epithelial ovarian cancer, it is a heterogenous group of tumors with several histological subtypes. The goal of our study was to develop specifical models to predict the survival of actively treated, HGSOC. METHODS: This retrospective cohort study included patients with HGSOC who had undergone surgery and chemotherapy between the years of 2010 and 2016 using the Surveillance, Epidemiology, and End Results (SEER) database. A total of 3,788 cases were randomly divided into a training (n=2,591) and test set (n=1,197). Cox-LASSO algorithm and cross validation (based on lambda.1se) were used to identify survival factors in the training set. Nomograms were created and internally validated. We used Harrell's C-statistic to assess discrimination. The performance of each nomogram was evaluated using calibration plots. The clinical benefit of our models was evaluated using a decision curve analysis. RESULTS: The significant prognostic factors were marital status, age, lymph node (LN) dissection, tumor size, residual disease, and the International Federation of Obstetrics and Gynecology (FIGO) stage, which were utilized to develop the nomogram for accurately predicting 3- and 5-year overall survival (OS). Among the above factors, except for marital status, the others were included in the model for cancer-specific survival (CSS). The C-indices for OS and CSS achieved 0.679 [95% confidence interval (CI): 0.660 to 0.699] and 0.678 (95% CI: 0.658 to 0.698), respectively, in the training set and 0.662 (95% CI: 0.633 to 0.690) and 0.680 (95% CI: 0.653 to 0.707), respectively, in the test set. The good consistency was illustrated using calibration plots. In comparison with models including only FIGO or the AJCC staging system, C-index in our study were increased by 4.5-7.0% for the development test and by 6.7-7.9% for the validation test. In addition, the nomograms had a bigger range of threshold probabilities in the decision curve analysis (DCA) curves. The high-risk subgroup had significantly less favorable survival than the low-risk subgroup. CONCLUSIONS: The present study indicated that the low-cost nomograms could be used as a potential prognostic tool specially for predicting survival in patients with HGSOC. Given the relatively small C-index, we still need to build a more accurate model to predict survival of HGSOC.