A Nomogram Based on SEER Database for Predicting Prognosis in Patients with Mucinous Ovarian Cancer: A Real-World Study

PURPOSE: Mucinous ovarian cancer (MOC) is a rare histological type of EOC. In order to guide the clinical diagnosis and management of MOC patients, we constructed and verified a nomogram for the estimation of overall survival in patients with MOC. PATIENTS AND METHODS: We collected 494 patients with MOC diagnosed from 2010 to 2015 in SEER database, and the following main inclusion criteria were used: (1) patients whose MOC was confirmed by pathology; (2) patients without a history of primary other cancer. Subsequently, we performed randomized grouping (6:4) and Cox hazard regression analysis in the training group. Subsequently, the nomogram was established. A variety of indicators were used to validate the prognosis value of nomogram, including the C-index, area under the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA). Moreover, Kaplan–Meier analysis was used to compare the survival results among different risk subgroups. RESULTS: Cox hazard regression analysis revealed that age, grade, FIGO stage and log odds of positive lymph nodes stage were independent risk factors for patients with MOC. In the training group, the C-index of the nomogram was 0.827 (95% CI: 0.791–0.863) and the areas under the curve (AUC) predicting the 1-, 3- and 5-year survival rate were 0.853 (95% CI: 0.791–0.915), 0.886 (95% CI: 0.852–0.920) and 0.815 (95% CI: 0.766–0.864), respectively. The calibration curve revealed that the nomogram of the 1-, 3- and 5-year survival rate was consistent with the actual fact. Patients with high risk had a poorer prognosis than those with low risk (P < 0.001). DCA revealed that the nomogram had the best clinical value than other classical prognostic markers. Similarly, nomogram had excellent prognostic ability in the testing group. CONCLUSION: The nomogram was constructed to predict overall survival in patients with MOC, which had the significance for clinical evaluation.