Accurate, noninvasive diagnosis of human brain tumors by using proton magnetic resonance spectroscopy

Mc, Preul; Caramanos, Zografos; Collins, D. Louis; Villemure, J.-G.; Leblanc, Richard; Olivier, André; Pokrupa, Ronald; Arnold, Douglas L.

doi:10.1038/nm0396-323

Cited by 463 publications

(294 citation statements)

References 8 publications

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“…This technique provides metabolic information about the tumor and has been shown to contribute to the prediction of the histological type (11)(12)(13)(14). Newly diagnosed gliomas have increased choline relative to normal brain tissue, which arises from the synthesis and degradation of mobile phospholipids, and decreased N-acetylaspartate (NAA), which denotes reduced neuronal function (15).…”

Section: Introductionmentioning

confidence: 99%

Perfusion, diffusion and spectroscopy values in newly diagnosed cerebral gliomas

et al. 2006

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Purpose To evaluate perfusion, diffusion, and spectroscopy values in enhancing and non-enhancing lesions for patients with newly diagnosed gliomas of different grades. Materials and Methods Sixty-seven patients with newly diagnosed glioma were entered into the study 20 grade II, 26 grade III and 21 grade IV. MR data were acquired at 1.5T and included diffusion weighted images (59/67 patients), dynamic perfusion weighted images (30/67 patients) and 3D H-1 MR spectroscopy (64/67 patients). Enhancing and non-enhancing lesions were delineated by a neuroradiologist and applied to maps of relative cerebral blood volume (rCBV), apparent diffusion coefficient (ADC), relative anisotropy (RA) and metabolite intensities. Results The median rCBV within enhancing regions of grade IV gliomas was significantly elevated relative to enhancing regions in grade III gliomas and normal brain. ADC was elevated relative to normal brain, but was not significantly different between grades or between enhancing and non-enhancing regions. The RA was higher in the non-enhancing region of grade IV gliomas relative to grade II and grade III. Levels of lactate plus lipid were significantly elevated in grade IV relative to grade II and grade III gliomas. Both enhancing and non-enhancing regions in grade IV gliomas showed significant correlations between CBV, ADC and choline levels. Conclusion The data were consistent with grade IV gliomas having higher membrane turnover, increased cell density and increased vascularity within enhancing lesions. Analysis of the correlations among parameters within grade IV gliomas suggested that high vascularity (high rCBV) was correlated with increased cellularity (low ADC) and increased membrane turnover (high choline) in these lesions. The non-enhancing region of grades II and III gliomas had MR parameters consistent with increased cellularity and/or membrane turnover.

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Section: Introductionmentioning

confidence: 99%

Perfusion, diffusion and spectroscopy values in newly diagnosed cerebral gliomas

et al. 2006

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“…To decrease the use of an invasive biopsy, which is very stressful for the patient, the usefulness of proton magnetic resonance spectroscopy (MRS) for tumor typing is under investigation. [1][2][3] Proton MRS provides completely different information as compared with imaging tools, but can be performed with most MRI instruments used in routine radiology. Proton MR spectra contain information from important brain metabolites like glutamate, myo-inositol, choline, creatine, N-acetyl aspartate (NAA), lactate and fatty acids.…”

Section: Introductionmentioning

confidence: 99%

Combination of feature-reduced MR spectroscopic and MR imaging data for improved brain tumor classification

et al. 2005

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The purpose of this paper is to evaluate the effect of the combination of magnetic resonance spectroscopic imaging (MRSI) data and magnetic resonance imaging (MRI) data on the classification result of four brain tumor classes. Suppressed and unsuppressed short echo time MRSI and MRI were performed on 24 patients with a brain tumor and four volunteers. Four different feature reduction procedures were applied to the MRSI data: simple quantitation, principal component analysis, independent component analysis and LCModel. Water intensities were calculated from the unsuppressed MRSI data. Features were extracted from the MR images which were acquired with four different contrasts to comply with the spatial resolution of the MRSI. Evaluation was performed by investigating different combinations of the MRSI features, the MRI features and the water intensities. For each data set, the isolation in feature space of the tumor classes, healthy brain tissue and cerebrospinal fluid was calculated and visualized. A test set was used to calculate classification results for each data set. Finally, the effect of the selected feature reduction procedures on the MRSI data was investigated to ascertain whether it was more important than the addition of MRI information. Conclusions are that the combination of features from MRSI data and MRI data improves the classification result considerably when compared with features obtained from MRSI data alone. This effect is larger than the effect of specific feature reduction procedures on the MRSI data. The addition of water intensities to the data set also increases the classification result, although not significantly. We show that the combination of data from different MR investigations can be very important for brain tumor classification, particularly if a large number of tumors are to be classified simultaneously.

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“…H MRS is a powerful non-invasive tool for the assessment of brain tumours (1)(2)(3). 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).…”

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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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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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Accurate, noninvasive diagnosis of human brain tumors by using proton magnetic resonance spectroscopy

Cited by 463 publications

References 8 publications

Perfusion, diffusion and spectroscopy values in newly diagnosed cerebral gliomas

Perfusion, diffusion and spectroscopy values in newly diagnosed cerebral gliomas

Combination of feature-reduced MR spectroscopic and MR imaging data for improved brain tumor classification

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

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