Development and validation of a nomogram to predict cancer-specific survival of mucinous epithelial ovarian cancer after cytoreductive surgery

BackgroundMucinous epithelial ovarian cancer (mEOC) is a relatively uncommon subtype of ovarian cancer with special prognostic features, but there is insufficient research in this area. This study aimed to develop a nomogram for the cancer-specific survival (CSS) of mEOC based on Surveillance, Epidemiology, and End Results (SEER) database and externally validate it in National Union of Real World Gynecological Oncology Research and Patient Management (NUWA) platform from China.MethodsPatients screened from SEER database were allocated into training and internal validation cohort in a ratio of 7: 3, with those from NUWA platform as an external validation cohort. Significant factors selected by Cox proportional hazard regression were applied to establish a nomogram for 3-year and 5-year CSS. The performance of nomogram was assessed by concordance index, calibration curves and Kaplan-Meier (K-M) curves.ResultsThe training cohort (n = 572) and internal validation cohort (n = 246) were filtered out from SEER database. The external validation cohort contained 186 patients. Baseline age, tumor stage, histopathological grade, lymph node metastasis and residual disease after primary surgery were significant risk factors (p < 0.05) and were included to develop the nomogram. The C-index of nomogram in training, internal validation and external validation cohort were 0.869 (95% confidence interval [CI], 0.838-0.900), 0.839 (95% CI, 0.787-0.891) and 0.800 (95% CI, 0.738-0.862), respectively. The calibration curves of 3-year and 5-year CSS in each cohort showed favorable agreement between prediction and observation. K-M curves of different risk groups displayed great discrimination.ConclusionThe discrimination and goodness of fit of the nomogram indicated its satisfactory predictive value for the CSS of mEOC in SEER database and external validation in China, which implies its potential application in different populations.