Nomograms for primary mucinous ovarian cancer: A SEER population-based study

Abstract

BackgroundTo develop predictive nomograms of overall survival (OS) and cancer-specific survival (CSS) in patients with primary mucinous ovarian cancer (PMOC). MethodsPatients diagnosed with PMOC from 2010 to 2015 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database, and randomly divided into a training cohort and a validation cohort. Univariate and multivariate Cox regression analyses were conducted to identify the independent risk factors. Nomograms were constructed and then verified by calibration plots, the concordance index (C-index), and the area under the receiver operating characteristic curve (AUC). ResultsA total of 991 patients with PMOC were enrolled and randomly divided into a training cohort (n=695) and a validation cohort (n=296) at a ratio of 7:3. Multivariate Cox regression analyses demonstrated that independent risk factors for OS included age, laterality, and American Joint Committee on Cancer (AJCC) stage. Independent risk factors for CSS included age, laterality, grade, and AJCC stage. Predictive nomograms for OS and CSS were developed with respective independent risk variables. In the training cohort, the C-index of the CSS and OS nomograms were 0.88 [95% confidence interval (CI): 0.84–0.92] and 0.85 (95% CI: 0.81–0.89), respectively. In the validation cohort, the C-index of the predictive CSS and OS nomograms were 0.86 (95% CI: 0.80–0.92) and 0.80 (95% CI: 0.74–0.87), respectively. The AUCs were higher in both cohorts. Furthermore, the calibration curves in both cohorts showed good consistency between the predicted results and the actual results. ConclusionThe nomograms demonstrated good predictability for the survival of patients with PMOC, and could serve as an applicable tool to help clinicians improve treatment plans.