Radiomics in pediatric brain tumors: from images to insights

Radiomics has emerged as a promising non-invasive imaging approach in pediatric neuro-oncology, offering the ability to extract high-dimensional quantitative features from routine MRI to support diagnosis, risk stratification, molecular characterization, and outcome prediction. Pediatric brain tumors, which differ significantly from adult tumors in biology and imaging appearance, present unique diagnostic and prognostic challenges. By integrating radiomics with machine learning algorithms, studies have demonstrated strong performance in classifying tumor types such as medulloblastoma, ependymoma, and gliomas, and predicting molecular subgroups and mutations such as H3K27M and BRAF. Recent studies combining radiomics with machine learning algorithms — including support vector machines, random forests, and deep learning CNNs — have demonstrated promising performance, with AUCs ranging from 0.75 to 0.98 for tumor classification and 0.77 to 0.88 for molecular subgroup prediction, across cohorts from 50 to over 450 patients, with internal cross-validation and external validation in some cases. In resource-limited settings or regions with limited radiologist manpower, radiomics-based tools could help augment diagnostic accuracy and consistency, serving as decision support to prioritize patients for further evaluation or biopsy. Emerging applications such as radio-immunomics and radio-pathomics may further enhance understanding of tumor biology but remain investigational. Despite its potential, clinical translation faces notable barriers, including limited pediatric-specific datasets, variable imaging protocols, and the lack of standardized, reproducible workflows. Multi-institutional collaboration, harmonized pipelines, and prospective validation are essential next steps. Radiomics should be viewed as a supplementary tool that complements existing clinical and pathological frameworks, supporting more informed and equitable care in pediatric brain tumor management.