Abstract
PURPOSE: Ovarian clear cell carcinoma (OCCC) is a distinct and highly malignant subtype of ovarian cancer with high individual heterogeneity in survival that requires specific prognostic predictive tools. Thus, this study aimed to construct and validate nomograms for predicting individual survival in OCCC patients. METHODS: In total, 91 patients with OCCC who were diagnosed and treated at Renji Hospital between 2010 and 2020 were extracted as the training cohort, then 86 patients from the First Affiliated Hospital of USTC were used as the external validation cohort. Prognostic factors that affect survival were identified using least absolute shrinkage and selection operator regression. Nomograms of progression-free survival (PFS) and overall survival (OS) were then established with the Cox regression model and the performance was subsequently evaluated using the concordance index (C-index), calibration plots, decision curve analysis (DCA), and risk subgroup classification. RESULTS: Advanced tumor, ascites of >400 mL, lymph node-positive, CA199 of >142.3 IU/mL, and fibrinogen of >5.36 g/L were identified as risk factors for OS while advanced tumor, ascites of >400 mL, lymph node-positive, and fibrinogen of >5.36 g/L were risk factors for PFS. The C-indexes for the OS and PFS nomograms were 0.899 and 0.731 in the training cohort and 0.804 and 0.787 in the validation cohort, respectively. The calibration plots showed that nomograms could provide better consistency in predicting patient survival than the FIGO staging system. DCA also demonstrated that nomograms were more clinically beneficial than the FIGO staging system. Additionally, patients could be classified into two risk groups based on scores using nomograms, with significant survival differences. CONCLUSIONS: We developed nomograms that could more objectively and reliably predict the individual survival of patients with OCCC compared with the FIGO staging system. These tools might assist in clinical decision-making and management of patients with OCCC to improve their survival outcomes.