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

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 usi...

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Autores principales:	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 Clark, Andrew Peet
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ceb6c559134f49fa9d1b2048f5539650
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spelling	oai:doaj.org-article:ceb6c559134f49fa9d1b2048f55396502021-12-02T10:44:21ZClassification of paediatric brain tumours by diffusion weighted imaging and machine learning10.1038/s41598-021-82214-32045-2322https://doaj.org/article/ceb6c559134f49fa9d1b2048f55396502021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-82214-3https://doaj.org/toc/2045-2322Abstract 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.Jan NovakNiloufar ZarinabadHeather RoseTheodoros ArvanitisLesley MacPhersonBenjamin PinkeyAdam OatesPatrick HalesRichard GrundyDorothee AuerDaniel Rodriguez GutierrezTim JaspanShivaram AvulaLaurence AbernethyRamneek KaurDarren HargraveDipayan MitraSimon BaileyNigel DaviesChristopher ClarkAndrew PeetNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q 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 Clark Andrew Peet Classification of paediatric brain tumours by diffusion weighted imaging and machine learning
description	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 × 10−3 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.
format	article
author	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 Clark Andrew Peet
author_facet	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 Clark Andrew Peet
author_sort	Jan Novak
title	Classification of paediatric brain tumours by diffusion weighted imaging and machine learning
title_short	Classification of paediatric brain tumours by diffusion weighted imaging and machine learning
title_full	Classification of paediatric brain tumours by diffusion weighted imaging and machine learning
title_fullStr	Classification of paediatric brain tumours by diffusion weighted imaging and machine learning
title_full_unstemmed	Classification of paediatric brain tumours by diffusion weighted imaging and machine learning
title_sort	classification of paediatric brain tumours by diffusion weighted imaging and machine learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ceb6c559134f49fa9d1b2048f5539650
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Classification of paediatric brain tumours by diffusion weighted imaging and machine learning

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