ArticleQuantitative imaging in medicine and surgery2022
Diagnostic accuracy of qualitative MRI in 550 paediatric brain tumours: evaluating current practice in the computational era.
Article in Quantitative imaging in medicine and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 16 citations in OpenAlex.
- The public health burden of nervous system tumors in Anambra State, Nigeria necessitates strategic resource allocation.Scientific reports · 2026Article
- DiffBTS: A Lightweight Diffusion Model for 3D Multimodal Brain Tumor Segmentation.Sensors (Basel, Switzerland) · 2025Article
- Tumour volume as a predictor of postoperative speech impairment in children undergoing resection of posterior fossa tumours: a prospective, multicentre study.Acta neurochirurgica · 2025Article
- Pineal region tumors: prognostic stratification based on magnetic resonance imaging features-a retrospective cohort study.Quantitative imaging in medicine and surgery · 2025Article
- Deep-learning model for diagnostic clue: detecting the dural tail sign for meningiomas on contrast-enhanced T1 weighted images.Quantitative imaging in medicine and surgery · 2023Article
- Pediatric brain tumor classification using deep learning on MR images with age fusion.Neuro-oncology advancesArticle
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4 authors at 4 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundTo investigate the accuracy of qualitative reporting of conventional magnetic resonance imaging (MRI) in the classification of paediatric brain tumours.
methodsPreoperative 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.
resultsDiagnostic 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.
conclusionsThis 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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