Abstract

Abstract BACKGROUND Medulloblastoma (MB) is the most frequent malignant brain tumor in children with an inadequate 5-year survival rate of 70-75%. A key determinate in MB treatment (consisting of radiation, chemotherapy) is the accurate risk-stratification of patients into low- and high-risk. The current stratification approaches (Chang’s classification, molecular categorization) are sub-optimal in predicting individual patient outcomes. Reliable risk-assessment approaches can significantly impact treatment management. Our group previously developed a new radiomics feature COLLAGE that measures the orientation of local intensity gradients as an estimate of lesion heterogeneity. We hypothesize that the disorder (i.e., heterogeneity) in orientation of local per-voxel intensity measured by COLLAGE, will tease-out differences in patients with low - versus high-risk MB tumors. METHODS T1-weighted MRI scans of 49 pediatric MB patients were retrospectively collected from Children’s Hospital Los Angeles and Cincinnati Children’s Hospital Medical Center. Registration of the scans was performed via age-specific pediatric atlases, followed by skull stripping and bias correction. Ground truth annotations for tumor sub-compartments; enhancing tumor, edema, and non-enhancing tumor, were generated by two experienced radiologists. Then, 52 Collage features were extracted from each of these sub-compartments, followed by conducting a Lasso model and a cox-proportional hazards model to identify the top features for risk-stratification. A continuous risk score was created for every patient and a risk threshold was obtained from the fitted cox model to stratify patients into low- and high-risk. RESULTS Our risk-score consisted of 13 top COLLAGE features from edema and 14 from non-enhancing tumor sub-compartments, respectively, and demonstrated significant differences between low- and high-risk patients (p = .017 for edema, p =0.0027 for non-enhancing tumor). CONCLUSION Our preliminary analysis suggests that gradient-entropy radiomic risk score maybe able to predict overall survival in pediatric MB tumors. In future, we plan to include additional patients to perform independent multi-site analysis.

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