Development of a decision support system for diagnosis and grading of brain tumours using <i>in vivo</i> magnetic resonance single voxel spectra

Tate, A. Rosemary; Underwood, Joshua; Acosta, Dionisio; Julià‐Sapé, Margarida; Majós, Carles; Moreno-Torres, Àngel; Howe, Franklyn A.; Graaf, Marinette van der; Lefournier, V.; Murphy, Mary M.; Loosemore, Alison; Ladroue, Christophe; Wesseling, Pieter; Bosson, Jean Luc; Cabañas, Miquel E.; Simonetti, Arjan W.; Gajewicz, Witold; Calvar, Jorge; Capdevila, Antoni; Wilkins, P.; Bell, B. Anthony; Rémy, Chantal; Heerschap, Arend; Watson, D. M. S.; Griffiths, John R.; Arús, Carles

doi:10.1002/nbm.1016

Cited by 210 publications

(348 citation statements)

References 33 publications

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“…Potential roles have been identified in pre-surgical diagnosis of tumour type and grade (4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19), monitoring of treatment response (20), and evaluation of tumour recurrence (21). However, optimised MRS analysis is required to enable the widespread clinical use of the technique.…”

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confidence: 99%

“…However, optimised MRS analysis is required to enable the widespread clinical use of the technique. Pattern classification of MRS data alone or in combination with MRI features has proven successful for accurately classifying brain tumours (1,(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17). Although brain tumours are the most common solid tumours in children and a major cause of childhood mortality, they are rarer than in adults, and only small numbers have been reported in the few studies using MRS to discriminate paediatric brain tumours (15)(16)(17)(18)(19).…”

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confidence: 99%

“…Pattern classification studies of MRS have commonly used features derived directly from the spectra as the input (8)(9)(10)(11). However, the use of prior knowledge to estimate metabolite quantities, including those with subtle spectral representation, may improve the discrimination of tumour types.…”

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Identification and characterisation of childhood cerebellar tumours by in vivo proton MRS

et al. 2008

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1 H MRS has great potential for the clinical investigation of childhood brain tumours, but the low incidence in, and difficulties of performing trials on, children have hampered progress in this area. Most studies have used a long-TE, thus limiting the metabolite information obtained, and multivariate analysis has been largely unexplored. Thirty-five children with untreated cerebellar tumours (18 medulloblastomas, 12 pilocytic astrocytomas and five ependymomas) were investigated using a single-voxel short-TE PRESS sequence on a 1.5 T scanner. Spectra were analysed using LCModel TM to yield metabolite profiles, and key metabolite assignments were verified by comparison with high-resolution magic-angle-spinning NMR of representative tumour biopsy samples. In addition to univariate metabolite comparisons, the use of multivariate classifiers was investigated. Principal component analysis was used for dimension reduction, and linear discriminant analysis was used for variable selection and classification. A bootstrap cross-validation method suitable for estimating the true performance of classifiers in small datasets was used. The discriminant function coefficients were stable and showed that medulloblastomas were characterised by high taurine, phosphocholine and glutamate and low glutamine, astrocytomas were distinguished by low creatine and high N-acetylaspartate, and ependymomas were differentiated by high myo-inositol and glycerophosphocholine. The same metabolite features were seen in NMR spectra of ex vivo samples. Successful classification was achieved for glial-cell (astrocytoma þ ependymoma) versus non-glial-cell (medulloblastoma) tumours, with a bootstrap 0.632 þ error, e B.632þ , of 5.3%. For astrocytoma vs medulloblastoma and astrocytoma vs medulloblastoma vs ependymoma classification, the e B.632þ was 6.9% and 7.1%, respectively. The study showed that 1 H MRS detects key differences in the metabolite profiles for the main types of childhood cerebellar tumours and that discriminant analysis of metabolite profiles is a promising tool for classification. The findings warrant confirmation by larger multi-centre studies.

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Identification and characterisation of childhood cerebellar tumours by in vivo proton MRS

et al. 2008

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“…6 For this reason, the features containing little discriminative power must be discarded, which means that we will need a large number of cases (about three to five times more cases than the number of features to extract) from which to extract those features. 7,8 However, in real life, the number of available cases will be limited by epidemiology and budget. This problem is called the curse of dimensionality.…”

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confidence: 99%

Pattern Recognition Analysis of MR Spectra

Ortega‐Martorell

Julià‐Sapé

Lisboa

et al. 2016

eMagRes

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The need for multivariate analysis of magnetic resonance spectroscopy (MRS) data was recognized about 20 years ago, when it became evident that spectral patterns were characteristic of some diseases. Despite this, there is no generally accepted methodology for performing pattern recognition (PR) analysis of MRS data sets. Here, the data acquisition and processing requirements for performing successful PR as applied to human MRS studies are introduced, and the main techniques for feature selection, extraction, and classification are described. These include methods of dimensionality reduction such as principal component analysis (PCA), independent component analysis (ICA), non-negative matrix factorization (NMF), and feature selection. Supervised methods such as linear discriminant analysis (LDA), logistic regression (LogR), and nonlinear classification are discussed separately from unsupervised and semisupervised classification techniques, including k -means clustering. Methods for testing and metrics for gauging the performance of PR models (sensitivity and specificity, the 'Confusion Matrix', 'k -fold cross-validation', 'Leave One Out', 'Bootstrapping', the 'Receiver Operating Characteristic curve', and balanced error and accuracy rates) are briefly described. This article ends with a summary of the main lessons learned from PR applied to MRS to date.

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“…The aim of this project is to help improve brain tumour classification by providing alternative, non invasive techniques. A predecessor project, Interpret [7], has shown that MRI and single voxel MRS data can aid in improving brain tumour classification. HealthAgents builds on top of these results and further employs multi voxel MRS data, as well as genomic DNA micro-array information for better classification results.…”

Section: Introductionmentioning

confidence: 99%

A Conceptual Graph Description of Medical Data for Brain Tumour Classification

Croitoru

Dashmapatra

et al.

Conceptual Structures: Knowledge Architectures for Smart Applications

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Abstract. HealthAgents proposes an agent-based distributed decision support system for brain tumour diagnosis and prognosis which employs Magnetic Resonance Imaging and Magnetic Resonance Spectroscopy techniques and genomic profiles. From a knowledge representation view point the distributed nature and the heterogeneity of the data to be integrated pose a number of challenging problems. This paper shows how Conceptual Graphs can be employed to describe the data sources in the HealthAgents system. Such knowledge representation based description of data allows for reasoning power when querying and for data modularisation capabilities.

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Development of a decision support system for diagnosis and grading of brain tumours using in vivo magnetic resonance single voxel spectra

Cited by 210 publications

References 33 publications

Identification and characterisation of childhood cerebellar tumours by in vivo proton MRS

Identification and characterisation of childhood cerebellar tumours by in vivo proton MRS

Pattern Recognition Analysis of MR Spectra

A Conceptual Graph Description of Medical Data for Brain Tumour Classification

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