BACKGROUND AND PURPOSE: Cancers show heterogeneity at various levels, from genome to radiological imaging. This study aimed to explore the interplay between genomic, transcriptomic, and radiophenotypic data in pediatric low-grade glioma (pLGG), the most common group of brain tumors in children. MATERIALS AND METHODS: We analyzed data from 201 pLGG patients in the Children's Brain Tumor Network (CBTN), using principal component analysis and K-Means clustering on 881 radiomic features, along with clinical variables (age, sex, tumor location), to identify imaging clusters and examine their association with 2021 WHO pLGG classifications. To determine the transcriptome pathways linked to imaging clusters, we employed a supervised machine learning model with elastic net logistic regression based on the pathways identified through gene set enrichment and gene co-expression network analyses. RESULTS: Three imaging clusters with distinct radiomic characteristics were identified. CONCLUSIONS: Our radiogenomics study indicates that the intrinsic molecular characteristics of tumors correlate with distinct imaging subgroups in pLGG, paving the way for future multi-modal investigations that may enhance understanding of disease progression and targetability. ABBREVIATIONS: WHO = World Health Organization
CBTN = Children's Brain Tumor Network
pLGG = pediatric Low-Grade Glioma
EFS = Event-Free Survival
PC = Principal Component
CNS = Central Nervous System.