Abstract 866: Radiomic prediction of survival in recurrent high-grade glioma patients treated with CAR T-cell therapy

Introduction: High-grade glioma (HGG) is the most common subtype of primary brain tumors with high recurrence rate and poor survival. The emergence of targeted molecular and cellular therapies (e.g., pembrolizumab, Chimeric Antigen Receptor [CAR] T-cell therapy) are potentially promising in improving overall survival. Due to increased intratumor heterogeneity and inhomogeneous treatment response, there is an unmet need of imaging biomarkers predictive of treatment response and survival. Radiomics using machine learning methods have shown promise in predicting treatment response in various solid tumors, including HGG. In this study, we compare the survival prediction performance using machine learning models with different radiomic features individually derived from T1- and T2-weighted MR images in patients suffering from HGG treated with CAR-T cell therapy. Methods: In this IRB-approved phase 1 clinical trial, 61 patients (39 males, median age = 49) suffering from recurrent HGG underwent surgical resection and CAR T-cell therapy1. All patients underwent baseline MRI scans prior to both surgical resection and CAR T-cell administration in the resection cavity. For patients with a complete set of T1- and T2-weighted MRIs (n = 50), we generated segmentations in a semi-automated manner, labeling with each tumorous voxel as either contrast-enhanced tumor (ET), non-enhancing tumor (NET) and edema. From each tumor label, we extracted shape-based, texture-based and image-filtered radiomic features2. We utilized gradient-boosted tree models (lightGBM) to classify whether survival is above or below group median (188 days) by using two nested loops of 10-fold cross validations each. For the inner validation loop, we determined the optimal model from hyper-parameters including regularization. For the outer validation loop, we tested this model on the hold-out data and the predictions were used as radiomic risk scores. Results: For each of the ET, NET, and edema tumor ROIs, we extracted 1313 radiomic features for predictive modeling. The outer validation loop Area Under the Receiver Operating Characteristic Curve (AUC) for ET, NET and edema were 0.55, 0.70, and 0.46, respectively, suggesting that radiomic features calculated from NET voxels are the most predictive of survival compared to features from ET and edema voxels. We also stratified the patients into two distinct prognostic sub-groups (25 patients each group) using the NET radiomic risk scores obtained from the