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

Novak, Jan, Zarinabad, Niloufar, Rose, Heather, Arvanitis, Theodoros, MacPherson, Lesley, Pinkey, Benjamin, Oates, Adam, Hales, Patrick, Grundy, Richard, Auer, Dorothee, Gutierrez, Daniel Rodriguez, Jaspan, Tim, Avula, Shivaram, Abernethy, Laurence, Kaur, Ramneek, Hargrave, Darren, Mitra, Dipayan, Bailey, Simon, Davies, Nigel, Clark, Christopher and Peet, Andrew (2021) Classification of paediatric brain tumours by diffusion weighted imaging and machine learning. Scientific reports, 11 (1). p. 2987. ISSN 2045-2322. This article is available to all UHB staff and students via ASK Discovery tool http://tinyurl.com/z795c8c by using their UHB Athens login IDs

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Abstract

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 mm s 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.

Item Type: Article
Additional Information: This article is available to all UHB staff and students via ASK Discovery tool http://tinyurl.com/z795c8c by using their UHB Athens login IDs
Subjects: QA Mathematics. Computing
QZ Pathology. Oncology
WLM Psychology
WN Medical imaging. Radiology
WS Paediatrics. Child health
Divisions: Womens and Childrens > Paediatrics
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Depositing User: Jamie Edgar
Date Deposited: 18 Feb 2021 12:03
Last Modified: 18 Feb 2021 12:03
URI: http://www.repository.uhblibrary.co.uk/id/eprint/3990

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