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Updated: Oct 7, 2025
Making MR Imaging Child's Play - Pediatric Neuroimaging Protocol, Guidelines and Procedure
Published on: July 30, 2009
Diagnostic accuracy of qualitative MRI in 550 paediatric brain tumours: evaluating current practice in the
Luke Dixon1, Gurpreet Kaur Jandu2, Jai Sidpra3,4
1Department of Neuroradiology, Imperial University Healthcare NHS Foundation Trust, London, UK.
Insights
Conventional magnetic resonance imaging (MRI) accuracy for classifying pediatric brain tumors varies significantly by tumor type and location. Computational methods are needed to improve diagnostic accuracy in challenging cases.
Area of Science:
- Neuroradiology
- Pediatric Oncology
- Neuro-oncology
Background:
- Conventional magnetic resonance imaging (MRI) plays a crucial role in the preoperative assessment of pediatric brain tumors.
- Accurate classification of these tumors is essential for guiding treatment strategies and predicting outcomes.
Purpose of the Study:
- To investigate the diagnostic accuracy of qualitative reporting of conventional MRI in classifying pediatric brain tumors.
- To evaluate the concordance between MRI findings and histopathological diagnoses.
Main Methods:
- Retrospective review of preoperative MRI reports for 550 children with intracranial lesions.
- Assessment of concordance using the WHO 2016 classification of CNS tumors.
- Calculation of diagnostic accuracy, sensitivity, specificity, and predictive values.
Main Results:
- Diagnostic accuracy varied significantly by tumor type and grade.
- Highest sensitivities were observed for ependymomas and sellar/pituitary/pineal tumors (80.65-100%).
- Lower sensitivities were noted for meningiomas and germ cell tumors (0-56.25%), with most accurate predictions in the posterior fossa and least accurate in lobar regions.
Conclusions:
- Conventional MRI shows variable diagnostic accuracy for pediatric brain tumors, dependent on tumor type and location.
- Development of computational methods is recommended to enhance accuracy in specific tumor types and anatomical regions.
- This study represents the largest series on MRI predictive accuracy for pediatric brain tumors.
Background:
To investigate the accuracy of qualitative reporting of conventional magnetic resonance imaging (MRI) in the classification of paediatric brain tumours.
Methods:
Preoperative MRI reports of 608 children prior to resection or biopsy of an intracranial lesion were retrospectively reviewed. A total of 550 children had complete radiological and histopathological notes, thereby reaching our inclusion criteria. Concordance between MRI report and final histopathological diagnosis was assessed using an established lexicon derived from the WHO 2016 classification of CNS tumours. Levels of agreement based on cellular origin, tumour type, and tumour grade were evaluated. Diagnostic accuracy, sensitivity, specificity, confidence intervals, and positive and negative predictive values were calculated.
Results:
Diagnostic accuracy differed significantly between tumour types and tumour grades. Sensitivities were highest for ependymomas and sellar, pituitary, pineal, and cranial and/or paraspinal nerve tumours (range 80.65-100%). Sensitivity was slightly lower for astrocytic gliomas, oligodendrogliomas, and choroid plexus, neuronal, mixed neuronal-glial, embryonal, and histiocytic tumours (range 63.33-79.59%). Low sensitivities were noted for meningiomas and mesenchymal non-meningothelial, melanocytic, and germ cell tumours (range 0-56.25%). The most correct tumour type predictions were made in the posterior fossa whilst the most incorrect predictions were made in the lobar regions, pineal/tectal plate area, and the supratentorial ventricles.
Conclusions:
This is the largest published series investigating the predictive accuracy of MRI in paediatric brain tumours. We show that diagnostic accuracy varies greatly by tumour type and location. Looking forward, we should develop and leverage computational methods to improve accuracy in the tumour types and anatomical locations where qualitative diagnostic accuracy is lower.
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