Magnetic resonance imaging (MRI) radiomics in paediatric neuro-oncology: A systematic review of clinical applications, feature interpretation, and biological insights in the characterisation and management of childhood brain tumours

Background: Childhood brain tumours, even though rare, present with significant diagnostic and treatment challenges. Radiomics involves feature extraction that is data-driven from standard imaging modalities, such as magnetic resonance imaging (MRI). In paediatric brain tumour imaging, MRI is often preferred because it is non-invasive and avoids exposure to radiation, making it safer for children. Radiomic features reveal additional information about tumour morphology and heterogeneity. By integrating biological meaning into imaging data, this approach enhances our understanding of tumour biology, thus supporting improved classification, treatment planning and management.

Purpose: This review focuses on MRI-based radiomics for the diagnosis and prognosis of childhood brain tumours. It assesses various approaches used in image pre-processing, tumour segmentation, feature extraction, and predictive model development to understand their accuracy and outcome, while it aims to understand the biological meaning and interpretation of radiomic features.

Methods: A systematic review was conducted, including 559 MRI-based radiomics studies in PubMed and Engineering Village Compendex databases, following preferred reporting items for systematic reviews and meta-analyses guidelines (PROSPERO registration: CRD42024503524). Data extraction focused on age, sample size, tumour type, pre-processing techniques, segmentation methods, feature extraction, and performance metrics.

Results: Nineteen studies were included, primarily addressing ependymoma (EP) and medulloblastoma (MB). Common pre-processing methods included intensity normalisation (n = 11) and bias correction (n = 4). GLCM (n = 12) and GLRLM (n = 7) were frequently used features, with LASSO (n = 8) and PCA (n = 2) as leading selection methods. SVM was the most used classification algorithm (n = 9), with an AUC range of 0.858–0.977. We have included the biological meaning and clinical significance of radiomic features to further understand why these insights are important.

Conclusion: While challenges such as limited datasets and varied imaging protocols exist, we recommend identifying and integrating the most informative radiomic features to enhance diagnostic and prognostic accuracy in childhood brain tumours, ultimately improving patient outcomes.