Dixon, Luke; Jandu, Gurpreet Kaur; Sidpra, Jai; Mankad, Kshitij; (2022) Diagnostic accuracy of qualitative MRI in 550 paediatric brain tumours: evaluating current practice in the computational era. Quantitative Imaging in Medicine and Surgery , 12 (1) pp. 131-143. 10.21037/qims-20-1388.

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Abstract

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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