Texture analysis of T1 - and T2 -weighted MR images and use of probabilistic neural network to discriminate posterior fossa tumours in children

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Texture analysis of T1 - and T2 -weighted MR images and use of probabilistic neural network to discriminate posterior fossa tumours in children. / Orphanidou, Eleni; Vlachos, Nikolaos; Davies, Nigel P.; Arvanitis, Theodoros N.; Grundy, Richard G.; Peet, Andrew C.

In: NMR in biomedicine, Vol. 27, No. 6, 01.06.2014, p. 632-639.

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Orphanidou, Eleni ; Vlachos, Nikolaos ; Davies, Nigel P. ; Arvanitis, Theodoros N. ; Grundy, Richard G. ; Peet, Andrew C. / Texture analysis of T1 - and T2 -weighted MR images and use of probabilistic neural network to discriminate posterior fossa tumours in children. In: NMR in biomedicine. 2014 ; Vol. 27, No. 6. pp. 632-639.

Bibtex

@article{015bb32af78f4add91463eac89eb38fa,
title = "Texture analysis of T1 - and T2 -weighted MR images and use of probabilistic neural network to discriminate posterior fossa tumours in children",
abstract = "Brain tumours are the most common solid tumours in children, representing 20% of all cancers. The most frequent posterior fossa tumours are medulloblastomas, pilocytic astrocytomas and ependymomas. Texture analysis (TA) of MR images can be used to support the diagnosis of these tumours by providing additional quantitative information. MaZda software was used to perform TA on T1 - and T2 -weighted images of children with pilocytic astrocytomas, medulloblastomas and ependymomas of the posterior fossa, who had MRI at Birmingham Children's Hospital prior to treatment. The region of interest was selected on three slices per patient in Image J, using thresholding and manual outlining. TA produced 279 features, which were reduced using principal component analysis (PCA). The principal components (PCs) explaining 95% of the variance were used in a linear discriminant analysis (LDA) and a probabilistic neural network (PNN) to classify the cases, using DTREG statistics software. PCA of texture features from both T1 - and T2 -weighted images yielded 13 PCs to explain >95% of the variance. The PNN classifier for T1 -weighted images achieved 100% accuracy on training the data and 90% on leave-one-out cross-validation (LOOCV); for T2 -weighted images, the accuracy was 100% on training the data and 93.3% on LOOCV. A PNN classifier with T1 and T2 PCs achieved 100% accuracy on training the data and 85.8% on LOOCV. LDA classification accuracies were noticeably poorer. The features found to hold the highest discriminating potential were all co-occurrence matrix derived, where adjacent pixels had highly correlated intensities. This study shows that TA can be performed on standard T1 - and T2 -weighted images of childhood posterior fossa tumours using readily available software to provide high diagnostic accuracy. Discriminatory features do not correspond to those used in the clinical interpretation of the images and therefore provide novel tumour information.",
keywords = "MRI, paediatric posterior fossa tumours, texture analysis",
author = "Eleni Orphanidou and Nikolaos Vlachos and Davies, {Nigel P.} and Arvanitis, {Theodoros N.} and Grundy, {Richard G.} and Peet, {Andrew C.}",
year = "2014",
month = jun,
day = "1",
doi = "10.1002/nbm.3099",
language = "English",
volume = "27",
pages = "632--639",
journal = "NMR in biomedicine",
issn = "0952-3480",
publisher = "Wiley",
number = "6",

}

RIS

TY - JOUR

T1 - Texture analysis of T1 - and T2 -weighted MR images and use of probabilistic neural network to discriminate posterior fossa tumours in children

AU - Orphanidou, Eleni

AU - Vlachos, Nikolaos

AU - Davies, Nigel P.

AU - Arvanitis, Theodoros N.

AU - Grundy, Richard G.

AU - Peet, Andrew C.

PY - 2014/6/1

Y1 - 2014/6/1

N2 - Brain tumours are the most common solid tumours in children, representing 20% of all cancers. The most frequent posterior fossa tumours are medulloblastomas, pilocytic astrocytomas and ependymomas. Texture analysis (TA) of MR images can be used to support the diagnosis of these tumours by providing additional quantitative information. MaZda software was used to perform TA on T1 - and T2 -weighted images of children with pilocytic astrocytomas, medulloblastomas and ependymomas of the posterior fossa, who had MRI at Birmingham Children's Hospital prior to treatment. The region of interest was selected on three slices per patient in Image J, using thresholding and manual outlining. TA produced 279 features, which were reduced using principal component analysis (PCA). The principal components (PCs) explaining 95% of the variance were used in a linear discriminant analysis (LDA) and a probabilistic neural network (PNN) to classify the cases, using DTREG statistics software. PCA of texture features from both T1 - and T2 -weighted images yielded 13 PCs to explain >95% of the variance. The PNN classifier for T1 -weighted images achieved 100% accuracy on training the data and 90% on leave-one-out cross-validation (LOOCV); for T2 -weighted images, the accuracy was 100% on training the data and 93.3% on LOOCV. A PNN classifier with T1 and T2 PCs achieved 100% accuracy on training the data and 85.8% on LOOCV. LDA classification accuracies were noticeably poorer. The features found to hold the highest discriminating potential were all co-occurrence matrix derived, where adjacent pixels had highly correlated intensities. This study shows that TA can be performed on standard T1 - and T2 -weighted images of childhood posterior fossa tumours using readily available software to provide high diagnostic accuracy. Discriminatory features do not correspond to those used in the clinical interpretation of the images and therefore provide novel tumour information.

AB - Brain tumours are the most common solid tumours in children, representing 20% of all cancers. The most frequent posterior fossa tumours are medulloblastomas, pilocytic astrocytomas and ependymomas. Texture analysis (TA) of MR images can be used to support the diagnosis of these tumours by providing additional quantitative information. MaZda software was used to perform TA on T1 - and T2 -weighted images of children with pilocytic astrocytomas, medulloblastomas and ependymomas of the posterior fossa, who had MRI at Birmingham Children's Hospital prior to treatment. The region of interest was selected on three slices per patient in Image J, using thresholding and manual outlining. TA produced 279 features, which were reduced using principal component analysis (PCA). The principal components (PCs) explaining 95% of the variance were used in a linear discriminant analysis (LDA) and a probabilistic neural network (PNN) to classify the cases, using DTREG statistics software. PCA of texture features from both T1 - and T2 -weighted images yielded 13 PCs to explain >95% of the variance. The PNN classifier for T1 -weighted images achieved 100% accuracy on training the data and 90% on leave-one-out cross-validation (LOOCV); for T2 -weighted images, the accuracy was 100% on training the data and 93.3% on LOOCV. A PNN classifier with T1 and T2 PCs achieved 100% accuracy on training the data and 85.8% on LOOCV. LDA classification accuracies were noticeably poorer. The features found to hold the highest discriminating potential were all co-occurrence matrix derived, where adjacent pixels had highly correlated intensities. This study shows that TA can be performed on standard T1 - and T2 -weighted images of childhood posterior fossa tumours using readily available software to provide high diagnostic accuracy. Discriminatory features do not correspond to those used in the clinical interpretation of the images and therefore provide novel tumour information.

KW - MRI

KW - paediatric posterior fossa tumours

KW - texture analysis

U2 - 10.1002/nbm.3099

DO - 10.1002/nbm.3099

M3 - Article

C2 - 24729528

VL - 27

SP - 632

EP - 639

JO - NMR in biomedicine

JF - NMR in biomedicine

SN - 0952-3480

IS - 6

ER -