Preoperative MR - based model for predicting prognosis in patients with intracranial extraventricular ependymoma.

Objectives: To develop and validate a prediction model based on brain MRI features to predict disease-free survival (DFS) and overall survival (OS) for patients with intracranial extraventricular ependymoma (IEE). Methods: The study included 114 patients with pathology-proven IEE, of whom 80 were randomly assigned to a training group and 34 to a validation group. Preoperative brain MRI images were assessed with the Visually AcceSAble Rembrandt Images (VASARI) feature set. Clinical variables were assessed including age, gender, KPS, pathological grade of the tumor and blood test data such as eosinophil, blood urea nitrogen and serum creatinine. Multivariate Cox proportional hazards regression analysis was performed to select the independent prognostic factors for DFS and OS. Three prediction models were built with clinical variables, MRI-VASARI features, and combined clinical and MRI-VASARI data, respectively. The predictive power of survival models was assessed using c-index and calibration curve. Results: Clinical variables such as eosinophil, blood urea nitrogen and serum creatinine, and MRI-VASARI feature for definition of the non-enhancing margin (F13) were significantly correlated with the prognosis of DFS. Blood urea nitrogen, D-dimer, tumor location (F1), eloquent brain (F3), and T1/FLAIR ratio (F10) were independent predictors of OS. Based on these factors, prediction models were constructed. The concordance indices of the three survival models for OS were 0.732, 0.729, and 0.768, respectively. For DFS, the concordance indices were respectively 0.694, 0.576, and 0.714. Conclusion: Predictive modelling combining both clinical and MRI-VASARI features is robust and may assist in the assessment of prognosis in patients with IEE.