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

Abstract

Abstract Objectives:To develop and validate a machine learning model based on MR 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 VASARI (Visually AcceSAble Rembrandt Images) feature set. Multivariate Cox proportional hazards regression analysis was performed to select the independent prognostic factors. Results: Eosinophil, blood urea nitrogen, serum creatinine, and definition of the non-enhancing margin (F13) were significantly correlated with the prognosis of DFS. And blood urea nitrogen, D-dimer, tumor location (F1), T1/FLAIR ratio (F3), and T1/FLAIR ratio (F10) were independent predictors of OS. Based on these factors, survival models with the clinical variables, MR-VASARI features, and with both the clinical and MR-VASARI features were constructed for DFS and OS respectively. The c-indices of the three survival models for OS were 0.732, 0.729, and 0.768, respectively. For DFS, the c-indices were respectively 0.694, 0.576, and 0.714. Conclusion:Predictive modelling combining both clinical and MR-VASARI features is robust and may assist in the assessment of prognosis in patients with IEE.