Differentiation of metastases from high‐grade gliomas using short echo time <sup>1</sup>H spectroscopy

Opstad, Kirstie S.; Murphy, Mary M.; Wilkins, P.; Bell, B. Anthony; Griffiths, John R.; Howe, Franklyn A.

doi:10.1002/jmri.20093

Cited by 99 publications

(94 citation statements)

References 27 publications

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“…SGUL training set: the intensities that were most highly correlated with the class index were all in the lipid region around the intensity at 0.8 ppm which was higher in Euclidian-normalized glioblastomas, in agreement with recent results described by SGUL (40). When this intensity was used eight out of 13 metastases and 14 out of 17 glioblastomas (22/30 ¼ 73%) of the training data were successfully assigned using the leave-one-out procedure and four out of five metastases and two out of three glioblastomas (6/8 ¼ 75%) of the test set were correctly classified.…”

Section: Mas Who Grade Ii) and Meningiomas (Who Grades I And Ii)supporting

confidence: 89%

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

et al. 2006

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A computer-based decision support system to assist radiologists in diagnosing and grading brain tumours has been developed by the multi-centre INTERPRET project. Spectra from a database of 1 H single-voxel spectra of different types of brain tumours, acquired in vivo from 334 patients at four different centres, are clustered according to their pathology, using automated pattern recognition techniques and the results are presented as a two-dimensional scatterplot using an intuitive graphical user interface (GUI). Formal quality control procedures were performed to standardize the performance of the instruments and check each spectrum, and teams of expert neuroradiologists, neurosurgeons, neurologists and neuropathologists clinically validated each case. The prototype decision support system (DSS) successfully classified 89% of the cases in an independent test set of 91 cases of the most frequent tumour types (meningiomas, low-grade gliomas and high-grade malignant tumours-glioblastomas and metastases). It also helps to resolve diagnostic difficulty in borderline cases. When the prototype was tested by radiologists and other clinicians it was favourably received. Results of the preliminary clinical analysis of the added value of using the DSS for brain tumour diagnosis with MRS showed a small but significant improvement over MRI used alone. In the comparison of individual pathologies, PNETs were significantly better diagnosed with the DSS than with MRI alone.

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Section: Mas Who Grade Ii) and Meningiomas (Who Grades I And Ii)supporting

confidence: 89%

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

et al. 2006

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“…This ratio was not different in various grades of gliomas similar to Arnold et al findings [19]. Significant increased ratio of PDE / βATP in metastases may be attributed to increase in PDE from degraded cell membranes from accelerated catabolism [1,25,26].…”

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confidence: 63%

Energy Status And Metabolism in Intracranial Space Occupying Lesions: A Prospective 31p Spectroscopic Study

Kamble¹

2014

JCDR

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Aim: Intracranial space occupying lesions can be infective or tumour. There are various advanced Magnetic resonance imaging techniques like perfusion, diffusion and proton spectroscopy which can differentiate between them. However, 31 Phosphorus spectroscopy studies the energy status and the metabolism pattern of various tissues and can be used potentially to differentiate between them depending on their Metabolism pattern. Thus, we aimed to study energy status of various intracranial lesions and try to differentiate between them including grades of gliomas. Materials and Methods:31 PMRS was done in 1.5T MRI in 43 patients prior to surgery or through/via stereo-tactic biopsy, of which 25 were men and 18 women with mean age 41.34 y ranging from 7-71 y. Single voxel phosphorus spectroscopy was done from the solid portion of the lesions and data was analysed and post processed.Results: Study includes Lymphoma (n=6), Grade 1 and 2 glioma (n=5), grade 3 glioma (n=9), grade 4 glioma(n=6), metastases (n=5), tuberculoma (n=7) and pyogenic abscesses (n=5). The integral values of PME, Pi, PDE, γ-ATP, α-ATP, β-ATP with reference to the position of PCr were calculated along with various ratios. Integral values of Pi and PDE were significantly increased in metastases but decreased in gliomas grade 1-2 compared to other pathologic conditions. Mean integral values of LEP (low energy phosphates) and total phosphates were significantly decreased in gliomas grades 1 and 2 and increased in metastases when compared with other pathologic conditions. PCr /Pi was increased in glioma grades 1, 2 and 3 but decreased in metastases; the significance was observed only in gliomas grade 3 and metastases. Metabolic ratios of PDE/βATP and Pi/ βATP were decreased in glioma grades 1 and 2 and increased in metastases with statistical significance. Conclusion:31 PMRS may help in differentiating primary from secondary lesions and assess grades of gliomas. P MR spectroscopy (TR = 400ms, TE=1ms, 512 acquisitions) using intravoxel in-vivo spectroscopy localization method with head quadrature dual-tuned coil. Care was taken to include mainly the solid portion of the lesion in the MRS voxel which ranged from 2 cm 3 -4 cm 3 . The duration of spectroscopy examination was about 5 min. Spectroscopy data were transferred to an installed software programme (Numaris spectroscopy analysis package VB33A). All post-processing techniques like apodization, zero and first order phase corrections were performed and spectra were plotted, analyzed and fitted to Lorentzian lines shapes.The following well resolved phosphorus metabolite peaks were observed in all patients-Phosphomonoesters (PME), Inorganic phosphate (Pi), Phosphodiesters (PDE), Phosphocreatine (PCr), γ, β, and α resonances of adenosine triphosphate (ATP). The chemical

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“…Moreover, lower classification rates (60-F o r P e e r R e v i e w 6 80%) or <0.8 average AUC were reported to distinguish GBM and MET in other previous studies (13,14,16,17,(22)(23)(24)(25).…”

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confidence: 72%

“…Since conventional structural MRI is ambiguous advanced techniques such as MR spectroscopy (7,13,14), perfusion MR (10,15) and Diffusion Tensor Imaging (DTI) (6,16), have been investigated for their accuracy in differentiation of GBM from solitary MET. Based on conventional structural and advanced MRI, a variety of sophisticated methods have been investigated to aid discrimination of GBMs and METs.…”

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confidence: 99%

Morphometric model for discrimination between glioblastoma multiforme and solitary metastasis using three‐dimensional shape analysis

Yang

Jones

Howe

et al. 2015

Magnetic Resonance in Med

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Purpose: Glioblastoma multiforme (GBM) and brain metastasis (MET) are the most common intra-axial brain neoplasms in adults and often pose a diagnostic dilemma using standard clinical MRI. These tumor types require different oncological and surgical management, which subsequently influence prognosis and clinical outcome. Methods: Here, we hypothesize that GBM and MET possess different three-dimensional (3D) morphological attributes based on their physical characteristics. A 3D morphological analysis was applied on the tumor surface defined by our diffusion tensor imaging (DTI) segmentation technique. It segments the DTI data into clusters representing different isotropic and anisotropic water diffusion characteristics, from which a distinct surface boundary between healthy and pathological tissue was identified. Morphometric features of shape index and curvedness were then computed for each tumor surface and used to build a morphometric model of GBM and MET pathology with the goal of developing a tumor classification method based on shape characteristics. Results: Our 3D morphometric method was applied on 48 untreated brain tumor patients. Cross-validation resulted in a 95.8% accuracy classification with only two shape features needed and that can be objectively derived from quantitative imaging methods. Conclusion: The proposed 3D morphometric analysis framework can be applied to distinguish GBMs from solitary METs

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Differentiation of metastases from high‐grade gliomas using short echo time ¹H spectroscopy

Cited by 99 publications

References 27 publications

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

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

Energy Status And Metabolism in Intracranial Space Occupying Lesions: A Prospective 31p Spectroscopic Study

Morphometric model for discrimination between glioblastoma multiforme and solitary metastasis using three‐dimensional shape analysis

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Differentiation of metastases from high‐grade gliomas using short echo time 1H spectroscopy

Cited by 99 publications

References 27 publications

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

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

Energy Status And Metabolism in Intracranial Space Occupying Lesions: A Prospective 31p Spectroscopic Study

Morphometric model for discrimination between glioblastoma multiforme and solitary metastasis using three‐dimensional shape analysis

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Differentiation of metastases from high‐grade gliomas using short echo time ¹H spectroscopy