Background: 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. 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. BRAF V600E mutations were primarily found in imaging cluster 3, while KIAA1549::BRAF fusion occurred in subtype 1. The model's predictive accuracy (AUC) was 0.77 for subtype 1, 0.78 for subtype 2, and 0.70 for subtype 3. Each imaging cluster exhibited unique molecular mechanisms: subtype 1 was linked to oxidative phosphorylation, PDGFRB, and interleukin signaling, whereas subtype 3 was associated with histone acetylation and DNA methylation pathways, related to BRAF V600E pLGGs. 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.

The authors have declared no competing interest.

Funding: National Institute of Health Grant Fundings 75N91019D00024, Supplement 3U2CHL156291-03S2, and 75N91019D00024.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

In this study, we analyzed retrospectively-collected data from the Children's Brain Tumor Network (CBTN) repository (cbtn.org), which includes specimens and longitudinal clinical and imaging data, facilitating the sharing of de-identified samples for research. The study complied with HIPAA guidelines and received IRB approval from the Children's Hospital of Philadelphia (CHOP) through the CBTN protocol, with informed consent obtained for patient enrollment.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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The processed data and the codes utilized for the analysis of genomic, transcriptomic, and radiomic data in conjunction with patient clinical characteristics is accessible at https://github.com/d3b-center/pLGG_imaging_clustering_genomic. All image processing tools employed in this study are publicly available and free to use, including CaPTk (https://www.cbica.upenn.edu/captk) and the in-house automated tumor segmentation model (https://github.com/d3b-center/peds-brain-auto-seg-public).

https://github.com/d3b-center/peds-brain-auto-seg-public