Serology-Based Model for Personalized Epithelial Ovarian Cancer Risk Evaluation.

This study aimed to establish a prognosis-prediction model based on serological indicators in patients with epithelial ovarian cancer (EOC). Patients initially diagnosed as ovarian cancer and surgically treated in Fudan University Shanghai Cancer Center from 2014 to 2018 were consecutively enrolled. Serological indicators preoperatively were collected. A risk model score (RMS) was constructed based on the levels of serological indicators determined by receiver operating characteristic curves. We correlated this RMS with EOC patients' overall survival (OS). Finally, 635 patients were identified. Pearson's χ 2 results showed that RMS was significantly related to clinical parameters. Kaplan-Meier analysis demonstrated that an RMS less than 3 correlated with a longer OS ( p < 0.0001). Specifically, significant differences were perceived in the survival curves of different subgroups. Multivariate Cox analysis revealed that age ( p = 0.015), FIGO stage ( p = 0.006), ascites ( p = 0.015) and RMS ( p = 0.005) were independent risk factors for OS. Moreover, RMS combined with age, FIGO and ascites could better evaluate for patients' prognosis in DCA analyses. Our novel RMS-guided classification preoperatively identified the prognostic subgroups of patients with EOC and showed higher accuracy than the conventional method, meaning that it could be a useful and economical tool for tailored monitoring and/or therapy.