Distinguishing between paediatric brain tumour types using multi-parametric magnetic resonance imaging and machine learning: a multi-site study

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Distinguishing between paediatric brain tumour types using multi-parametric magnetic resonance imaging and machine learning : a multi-site study. / Grist, James T.; Withey, Stephanie; Macpherson, Lesley; Oates, Adam; Powell, Stephen; Novak, Jan; Abernethy, Laurence; Pizer, Barry; Grundy, Richard; Bailey, Simon; Mitra, Dipayan; Arvanitis, Theodoros N.; Auer, Dorothee P.; Avula, Shivaram; Peet, Andrew C.

In: NeuroImage: Clinical, Vol. 25, 102172, 23.01.2020, p. 1-6.

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Grist, James T. ; Withey, Stephanie ; Macpherson, Lesley ; Oates, Adam ; Powell, Stephen ; Novak, Jan ; Abernethy, Laurence ; Pizer, Barry ; Grundy, Richard ; Bailey, Simon ; Mitra, Dipayan ; Arvanitis, Theodoros N. ; Auer, Dorothee P. ; Avula, Shivaram ; Peet, Andrew C. / Distinguishing between paediatric brain tumour types using multi-parametric magnetic resonance imaging and machine learning : a multi-site study. In: NeuroImage: Clinical. 2020 ; Vol. 25. pp. 1-6.

Bibtex

@article{f8d8239f802143489af30e740408ae27,
title = "Distinguishing between paediatric brain tumour types using multi-parametric magnetic resonance imaging and machine learning: a multi-site study",
abstract = "The imaging and subsequent accurate diagnosis of paediatric brain tumours presents a radiological challenge, with magnetic resonance imaging playing a key role in providing tumour specific imaging information. Diffusion weighted and perfusion imaging are commonly used to aid the non-invasive diagnosis of children's brain tumours, but are usually evaluated by expert qualitative review. Quantitative studies are mainly single centre and single modality. The aim of this work was to combine multi-centre diffusion and perfusion imaging, with machine learning, to develop machine learning based classifiers to discriminate between three common paediatric tumour types. The results show that diffusion and perfusion weighted imaging of both the tumour and whole brain provide significant features which differ between tumour types, and that combining these features gives the optimal machine learning classifier with >80% predictive precision. This work represents a step forward to aid in the non-invasive diagnosis of paediatric brain tumours, using advanced clinical imaging.",
keywords = "perfusion, diffusion, machine learning",
author = "Grist, {James T.} and Stephanie Withey and Lesley Macpherson and Adam Oates and Stephen Powell and Jan Novak and Laurence Abernethy and Barry Pizer and Richard Grundy and Simon Bailey and Dipayan Mitra and Arvanitis, {Theodoros N.} and Auer, {Dorothee P.} and Shivaram Avula and Peet, {Andrew C}",
year = "2020",
month = jan,
day = "23",
doi = "10.1016/j.nicl.2020.102172",
language = "English",
volume = "25",
pages = "1--6",
journal = "NeuroImage: Clinical",
issn = "2213-1582",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Distinguishing between paediatric brain tumour types using multi-parametric magnetic resonance imaging and machine learning

T2 - a multi-site study

AU - Grist, James T.

AU - Withey, Stephanie

AU - Macpherson, Lesley

AU - Oates, Adam

AU - Powell, Stephen

AU - Novak, Jan

AU - Abernethy, Laurence

AU - Pizer, Barry

AU - Grundy, Richard

AU - Bailey, Simon

AU - Mitra, Dipayan

AU - Arvanitis, Theodoros N.

AU - Auer, Dorothee P.

AU - Avula, Shivaram

AU - Peet, Andrew C

PY - 2020/1/23

Y1 - 2020/1/23

N2 - The imaging and subsequent accurate diagnosis of paediatric brain tumours presents a radiological challenge, with magnetic resonance imaging playing a key role in providing tumour specific imaging information. Diffusion weighted and perfusion imaging are commonly used to aid the non-invasive diagnosis of children's brain tumours, but are usually evaluated by expert qualitative review. Quantitative studies are mainly single centre and single modality. The aim of this work was to combine multi-centre diffusion and perfusion imaging, with machine learning, to develop machine learning based classifiers to discriminate between three common paediatric tumour types. The results show that diffusion and perfusion weighted imaging of both the tumour and whole brain provide significant features which differ between tumour types, and that combining these features gives the optimal machine learning classifier with >80% predictive precision. This work represents a step forward to aid in the non-invasive diagnosis of paediatric brain tumours, using advanced clinical imaging.

AB - The imaging and subsequent accurate diagnosis of paediatric brain tumours presents a radiological challenge, with magnetic resonance imaging playing a key role in providing tumour specific imaging information. Diffusion weighted and perfusion imaging are commonly used to aid the non-invasive diagnosis of children's brain tumours, but are usually evaluated by expert qualitative review. Quantitative studies are mainly single centre and single modality. The aim of this work was to combine multi-centre diffusion and perfusion imaging, with machine learning, to develop machine learning based classifiers to discriminate between three common paediatric tumour types. The results show that diffusion and perfusion weighted imaging of both the tumour and whole brain provide significant features which differ between tumour types, and that combining these features gives the optimal machine learning classifier with >80% predictive precision. This work represents a step forward to aid in the non-invasive diagnosis of paediatric brain tumours, using advanced clinical imaging.

KW - perfusion

KW - diffusion

KW - machine learning

U2 - 10.1016/j.nicl.2020.102172

DO - 10.1016/j.nicl.2020.102172

M3 - Article

C2 - 32032817

VL - 25

SP - 1

EP - 6

JO - NeuroImage: Clinical

JF - NeuroImage: Clinical

SN - 2213-1582

M1 - 102172

ER -