Epidemiology and Survival of Patients with Spinal Ependymomas: A Large Retrospective Cohort Study

Background Spinal ependymomas, the most prevalent primary intramedullary spinal tumors, exhibit a bimodal age distribution and variable histology (WHO grades I-III). Existing studies are limited by small cohorts and outdated data. This large-scale study aimed to characterize epidemiology, identify survival predictors, and develop a clinically applicable prognostic tool. Methods Using SEER data (2000–2020), 3,179 patients with histologically confirmed spinal ependymomas (cord, conus, or cauda equina) were analyzed. Variables included demographics, tumor characteristics (size, WHO grade), and treatments. Overall survival (OS) was assessed via Kaplan-Meier and Cox regression. Independent prognostic factors were integrated into a nomogram (validated in training:validation cohorts, 7:3 ratio) predicting 3-, 5-, and 10-year OS. Model performance was evaluated using ROC, calibration, and decision curve analysis. A risk stratification system and web-based dynamic tool were developed. Results Annual incidence was 0.31–0.44 per 100,000, with 40.21% of patients aged > 50 years. Multivariate analysis identified seven independent predictors of overall survival (OS): male sex (HR = 1.50, p = 0.002), age > 61 years (HR = 7.07, p < 0.001), WHO grade III (HR = 3.65, p < 0.001), "Other" race (Asian, Native American, etc.; reference: Black; HR = 0.47, p = 0.046), radiotherapy (HR = 1.48, p = 0.011), surgery (HR = 0.64, p = 0.021), and chemotherapy (HR = 5.62, p < 0.001; univariate HR = 10.03). The nomogram demonstrated strong discrimination (AUC: 3-year = 0.80–0.81, 5-year = 0.76–0.82, 10-year = 0.76–0.87) and calibration. Risk stratification (low/medium/high) based on nomogram scores (cutoffs: 59, 132) showed significant survival differences (p < 0.05). Conclusions This study establishes key demographic, clinical, and therapeutic determinants of OS in spinal ependymomas. The validated nomogram and web-based tool enable individualized survival prediction, risk stratification, and tailored treatment planning, addressing critical gaps in prognostication for this rare tumor.