A Multi-Centre, Web-Accessible and Quality Control-Checked Database of in vivo MR Spectra of Brain Tumour Patients

Julià‐Sapé, Margarida; Acosta, Dionisio; Mier, Mariola; Arús, Carles; Watson, D. M. S.

doi:10.1007/s10334-005-0023-x

Cited by 79 publications

(100 citation statements)

References 12 publications

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“…Acquisition protocols for clinical, radiological and histopathological data were defined to ensure the compatibility of the data acquired [22,24].…”

Section: Data Acquisitionmentioning

confidence: 99%

Accurate classification of childhood brain tumours by in vivo 1H MRS – A multi-centre study

Vicente

Fuster–Garcia

Tortajada

et al. 2013

European Journal of Cancer

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ElsevierVicente Robledo, J.; Fuster Garcia, E.; Tortajada Velert, S.; García Gómez, JM.; Davies, N.; Natarajan, K.; Wilson, M.... (2013) Integration. Linear Discriminant Analysis was applied to this data to produce diagnostic classifiers. An evaluation of the diagnostic accuracy was performed based on resampling to measure the Balanced Accuracy Rate (BAR). Results:The accuracy of the diagnostic classifiers for discriminating the three tumour types was found to be high (BAR 0.98) when a combination of TE was used. The combination of both TE significantly improved the classification performance (p < 0.01, Tukeyʼs test) compared with the use of one TE alone. 3Other tumour types were classified accurately as glial or primitive neuroectodermal (BAR 1.00). 12 cases failed the inclusion criteria for QC mainly due to poor SNR. Conclusions Classification and evaluation 9The diagnostic classification problem of discriminating between EPEN, PILOA and MED, the three most common pediatric tumour types, is addressed in this study. Since EPEN and PILOA tumours can be found in brain locations other than the PF whereas MED are found only in the PF, training was undertaken twice, once using the tumour cases located in the PF and then with those in any brain location. Classifiers were designed and evaluated using features from Short-TE and Long-TE alone and a combination of both TEs, ShortTE+Long-TE. Our results were compared with those in previous studies [27][28][29][30].Based on the results of previous studies [15,20,26,29 Results Spectral featuresSeveral key features allow visual discrimination of PILOA, EPEN and MED. Figures 1 and 2 show the Short-TE and Long-TE mean spectra of the tumour types. Minimum differences are found between the mean spectra of the tumours in the PF and those in any location. All tumour spectra display an increase in Cho peak (3.2ppm) with respect to Cr peak (3.0ppm). NAA (2.0ppm) presents a less prominent peak in MED and EPEN compared with 10 PILOA. Elevation of macromolecules and lipids (0.9ppm and 1.3ppm) is observed in Short-TE. Regarding Long-TE, the inverted peak of Lac at 1.3ppm is distinguished in PILOA and EPEN but not in MED. Tables 2 and 3 show the metabolite concentrations estimated with TARQUIN in Short-TE and Long-TE for the three tumour types found in any brain location. The Kruskal-Wallis test for the analysis of the variance (α=0.05) was applied to determine the significant differences in metabolite concentrations of PILOA, EPEN and MED. Both Cho components, Glycerophosphocholine (GPC) and Phosphocholine (PCh) (p≤0.01) showed significant differences. Cr and Tau concentrations were significantly different in both TEs (p≤0.01). Univariate metabolite comparisonDifferences in the mI concentrations (p≤0.01) were significant in Short-TE.Macromolecules and lipids at 0.9, 1.3 and 2.0ppm (p≤0.05, p≤0.01 and p≤0.01, respectively) exhibited statistical differences in Short-TE MRS. Classification DiscussionThis is the first study of MRS as a non-invasive diagnostic aid in childhood brain tumo...

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“…Acquisition protocols for clinical, radiological and histopathological data were defined to ensure the compatibility of the data acquired [22,24].…”

Section: Data Acquisitionmentioning

confidence: 99%

Accurate classification of childhood brain tumours by in vivo 1H MRS – A multi-centre study

Vicente

Fuster–Garcia

Tortajada

et al. 2013

European Journal of Cancer

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“…The consensus protocols developed by the INTERPRET project for acquisition of MRI and MRS data (25) are shown in Table I. Gadolinium contrast agents, when used, were given prior to MRS measurements.…”

Section: In Vivo Mrs Spectramentioning

confidence: 99%

Non-invasive grading of astrocytic tumours from the relative contents of myo-inositol and glycine measured by in vivo mrs

Candiota

Majós

Julià‐Sapé

2011

Journal of the Belgian Society of Radiology

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MRI and MRS are established methodologies for evaluating intracranial lesions. One MR spectral feature suggested for in vivo grading of astrocytic tumours is the apparent myo-Inositol (mI) intensity (ca 3.55ppm) at short echo times, although glycine (gly) may also contribute in vivo to this resonance. The purpose of this study was to quantitatively evaluate the mI + gly contribution to the recorded spectral pattern in vivo and correlate it with in vitro data obtained from perchloric acid extraction of tumour biopsies. Patient spectra (n = 95) at 1.5T at short (20-31 ms) and long (135-136 ms) echo times were obtained from the INTERPRET MRS database (http://gabrmn.uab.es/interpretvalidateddb/). Phantom spectra were acquired with a comparable protocol. Spectra were automatically processed and the ratios of the (mI + gly) to Cr peak heights ((mI + gly)/Cr) calculated. Perchloric acid extracts of brain tumour biopsies were analysed by high-resolution NMR at 9.4T. The ratio (mI + gly)/Cr decreased significantly with astrocytic grade in vivo between low-grade astrocytoma (A2) and glioblastoma multiforme (GBM). In vitro results displayed a somewhat different tendency, with anaplastic astrocytomas having significantly higher (mI + gly)/Cr than A2 and GBM. The discrepancy between in vivo and in vitro data suggests that the NMR visibility of glycine in glial brain tumours is restricted in vivo.

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“…47 An important fact to consider as well is that not all classes are homogeneous, that is to say, they may not follow a normal distribution. One example is the spectral pattern of glioblastoma, as assessed by 1 H MRS ( Figure 6). …”

Section: Lessons Learned From Previous Studiesmentioning

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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A Multi-Centre, Web-Accessible and Quality Control-Checked Database of in vivo MR Spectra of Brain Tumour Patients

Abstract: The validated-DB complies with ethics regulations and represents the population studied. It is accessible by neuroradiologists willing to use information provided by MRS to help in the non-invasive diagnosis of brain tumours.

Cited by 79 publications

References 12 publications

Accurate classification of childhood brain tumours by in vivo 1H MRS – A multi-centre study

Accurate classification of childhood brain tumours by in vivo 1H MRS – A multi-centre study

Non-invasive grading of astrocytic tumours from the relative contents of myo-inositol and glycine measured by in vivo mrs

Pattern Recognition Analysis of MR Spectra

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