Abstract
BACKGROUND: Malignant ependymomas are rare cancerous tumors that are associated with increased morbidity and mortality in the affected patients. Lately, there has been a lot of controversy about the correct way to manage and predict the survival outcome of these patients. We aim in this retrospective cohort study to develop novel nomograms that can better predict the overall survival (OS) and cancer-specific survival (CSS) of these patients.
METHODS: This is a retrospective cohort study that was conducted through the Surveillance, Epidemiology, and End Results databases (SEER) between 1998 and 2016. Patients were excluded if they had an unknown diagnosis, unknown cause of death or those with survival duration less than a month. We used penalized regression models with the highest time-dependent area under the ROC curve (AUC) and most stable calibra-tions to construct the nomograms. By searching the SEER database and applying the eligibility criteria, we identified 3391 patients for the final analysis.
RESULTS: Nine penalized regression models were developed of which two models including adaptive elastic-net was selected for both OS and CSS. The model incorporated age, sex, year of diagnosis, site, race, radiation, chemotherapy, surgery, and type for the construction of no-mograms. We aimed in this population-based cohort study to develop novel prediction tools that can help physicians estimate the survival of malignant ependymoma patients and provide better care.
CONCLUSIONS: Our nomograms appear to have high accuracy and applicability, which we hope that can predict the survival and improve the treatment and prognosis of these patients.