ReviewDigital health
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.
Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Predictive Performance of Radiomics-Based Machine Learning for Colorectal Cancer Recurrence Risk: Systematic Review and Meta-Analysis.JMIR medical informatics · 2025Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 ( 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.