MDB-42. RADIOMIC TEXTURAL AND MORPHOLOGICAL-BASED FEATURES TO RISK-STRATIFY PEDIATRIC MEDULLOBLASTOMA PATIENTS

Abstract

Abstract BACKGROUND One key determinate in the treatment pathway for pediatric medulloblastoma (MB), the most frequent malignant brain tumor in children, is accurate risk-stratification. MB tumors are classified as standard- or high-risk based on current approaches (Chang’s/molecular stratification). However, there is still a need for additional attributes towards reliable risk-stratification. This work presents a radiomic-based approach that combines textural- and morphological-based attributes to risk-stratify MB patients. Our rationale is that coupling texture and morphology attributes from the intra-tumoral regions that quantify the heterogeneity and disorderly nature of aggressive tumors can transcend the current clinical approaches in accurately stratifying tumors into high- and standard-risk. METHODS T1-weighted MRI scans of 119 MB patients (2–18 years) were collected from Cincinnati Children’s Hospital Medical Center (Site1-n=42), Children’s Hospital Los Angeles (Site2-n=47), and Children’s Hospital of Philadelphia (Site3-n=30), used interchangeably for training and testing. Following preprocessing and performing annotations for the enhancing lesion and edema sub-compartments, 232 textural and morphological features were extracted from each sub-compartment. Namely, 214 textural (gradient, Haralick, intensity, Gabor, Laws, entropy) as well as 34 morphological (4 surface-based (e.g., curvedness and sharpness), 18 global (e.g., roundness, elongation, compactness, flatness)) features were extracted. Features were then fed into Elastic-Net regression models for survival analysis. RESULTS Our analysis revealed that texture and morphology features, combined, yielded Concordance index (CI) and p-value of 0.52, 0.0001, respectively, for the enhancing lesion, using Sites1,3 for training, and CI=0.61, p=0.0065 using Site3 for testing. Additionally, for edema, CI and p-value of 0.514, 0.0001 were obtained when employing Sites2,3 for training and CI=0.61, p=0.00096 using Site1 for testing. Results were not significant when using Chang’s or molecular stratification alone for survival analysis. CONCLUSIONS Our study shows that radiomic-based morphological and textural features show promise towards reliable MB risk-stratification.