IMG-13. PREDICTION OF PEDIATRIC MEDULLOBLASTOMA SUBGROUPS USING CLINICO-RADIOMIC ANALYSIS

Abstract BACKGROUND Pediatric medulloblastoma is an aggressive brain tumor and tailored treatment has the potential to lead to better patient outcomes. The WHO 2021 classification of medulloblastoma involves an integrated diagnosis that incorporates genetically defined characteristics, including molecular subgroups (WNT-activated, SHH-activated TP53 wildtype, SHH-activated TP53-mutant, and non-WNT/non-SHH). Clinically acquired radiology (MRI) imaging characteristics could provide a non-invasive, pre-treatment biomarker for such subgroups faster than standard methylation-based turn-around times, and for use in countries where methylation is not available. METHODS Herein, we utilize a multi-institutional dataset of multi-parametric, clinical MRIs of pediatric medulloblastoma patients from the Children’s Brain Tumor Network (median age = 8.9 years) to assess the predictive value of patient-level radiomic and clinical factors. 89 subjects with treatment-naïve T1w/T1w contrast-enhanced/T2w/FLAIR images and molecular subgroups (derived from methylation profiling or RNA sequencing) were included (SHH=18, WNT=10, non-SHH/WNT=61). Radiomic features were extracted from a radiologist-defined volumetric (3D) segmentation for each subject separately (including solid tumor, cystic, and peritumoral edema regions). Clinical variables included sex, age at diagnosis, and metastatic disease. 20 top performing features were selected based on an ANOVA between features/classes and subsequently used to train and evaluate three separate classification models based on radiomic, clinical, or clinico-radiomic features (Linear SVM; leave-one-subject-out cross-validation). RESULTS The combined clinico-radiomic model had the top performance (AUC=0.83) followed by radiomic (0.78) and clinical (0.5) for predicting SHH/WNT vs. non-SHH/WNT groups. Predictive radiomic features included intensity-based statistics (T1w, T2w, T1w-CE), texture (T2w), and morphological shape characteristics. CONCLUSIONS Our findings show potential for early prediction of molecular subgroups using baseline imaging that could lend to faster decision-making in patient treatment planning. Future work aims to evaluate the inclusion of histological characteristics for a full integrated diagnostic approach as well as assessment of prognostic value of the combined multi-omic features.