Classification of paediatric brain tumours by diffusion weighted imaging and machine learning

Jan Novak, Niloufar Zarinabad, Heather Rose, Theodoros Arvanitis, Lesley MacPherson, Benjamin Pinkey, Adam Oates, Patrick Hales, Richard Grundy, Dorothee Auer, Daniel Rodriguez Gutierrez, Tim Jaspan, Shivaram Avula, Laurence Abernethy, Ramneek Kaur, Darren Hargrave, Dipayan Mitra, Simon Bailey, Nigel Davies, Christopher ClarkAndrew Peet

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To determine if apparent diffusion coefficients (ADC) can discriminate between posterior fossa brain tumours on a multicentre basis. A total of 124 paediatric patients with posterior fossa tumours (including 55 Medulloblastomas, 36 Pilocytic Astrocytomas and 26 Ependymomas) were scanned using diffusion weighted imaging across 12 different hospitals using a total of 18 different scanners. Apparent diffusion coefficient maps were produced and histogram data was extracted from tumour regions of interest. Total histograms and histogram metrics (mean, variance, skew, kurtosis and 10th, 20th and 50th quantiles) were used as data input for classifiers with accuracy determined by tenfold cross validation. Mean ADC values from the tumour regions of interest differed between tumour types, (ANOVA P < 0.001). A cut off value for mean ADC between Ependymomas and Medulloblastomas was found to be of 0.984 × 10-3 mm2 s-1 with sensitivity 80.8% and specificity 80.0%. Overall classification for the ADC histogram metrics were 85% using Naïve Bayes and 84% for Random Forest classifiers. The most commonly occurring posterior fossa paediatric brain tumours can be classified using Apparent Diffusion Coefficient histogram values to a high accuracy on a multicentre basis.

Original languageEnglish
Article number2987
JournalScientific Reports
Issue number1
Publication statusPublished - 4 Feb 2021

ASJC Scopus subject areas

  • General


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