Literature DB >> 21427025

Strengths and weaknesses of 1.5T and 3T MRS data in brain glioma classification.

M G Kounelakis1, I N Dimou, M E Zervakis, I Tsougos, E Tsolaki, E Kousi, E Kapsalaki, K Theodorou.   

Abstract

Although magnetic resonance spectroscopy (MRS) methods of 1.5Tesla (T) and 3T have been widely applied during the last decade for noninvasive diagnostic purposes, only a few studies have been reported on the value of the information extracted in brain cancer discrimination. The purpose of this study is threefold. First, to show that the diagnostic value of the information extracted from two different MRS scanners of 1.5T and 3T is significantly influenced in terms of brain gliomas discrimination. Second, to statistically evaluate the discriminative potential of publicly known metabolic ratio markers, obtained from these two types of scanners in classifying low-, intermediate-, and high-grade gliomas. Finally, to examine the diagnostic value of new metabolic ratios in the discrimination of complex glioma cases where the diagnosis is both challenging and critical. Our analysis has shown that although the information extracted from 3T MRS scanner is expected to provide better brain gliomas discrimination; some factors like the features selected, the pulse-sequence parameters, and the spectroscopic data acquisition methods can influence the discrimination efficiency. Finally, it is shown that apart from the bibliographical known, new metabolic ratio features such as N-acetyl aspartate/ S, Choline/ S, Creatine/ S , and myo-Inositol/ S play significant role in gliomas grade discrimination.

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Year:  2011        PMID: 21427025     DOI: 10.1109/TITB.2011.2131146

Source DB:  PubMed          Journal:  IEEE Trans Inf Technol Biomed        ISSN: 1089-7771


  4 in total

1.  Automatic Brain Tumor Classification via Lion Plus Dragonfly Algorithm.

Authors:  B Leena; A N Jayanthi
Journal:  J Digit Imaging       Date:  2022-06-16       Impact factor: 4.903

2.  Integrating diffusion kurtosis imaging, dynamic susceptibility-weighted contrast-enhanced MRI, and short echo time chemical shift imaging for grading gliomas.

Authors:  Sofie Van Cauter; Frederik De Keyzer; Diana M Sima; Anca Croitor Sava; Felice D'Arco; Jelle Veraart; Ronald R Peeters; Alexander Leemans; Stefaan Van Gool; Guido Wilms; Philippe Demaerel; Sabine Van Huffel; Stefan Sunaert; Uwe Himmelreich
Journal:  Neuro Oncol       Date:  2014-07       Impact factor: 12.300

3.  Tumour Relapse Prediction Using Multiparametric MR Data Recorded during Follow-Up of GBM Patients.

Authors:  Adrian Ion-Margineanu; Sofie Van Cauter; Diana M Sima; Frederik Maes; Stefaan W Van Gool; Stefan Sunaert; Uwe Himmelreich; Sabine Van Huffel
Journal:  Biomed Res Int       Date:  2015-08-27       Impact factor: 3.411

4.  A novel semi-supervised methodology for extracting tumor type-specific MRS sources in human brain data.

Authors:  Sandra Ortega-Martorell; Héctor Ruiz; Alfredo Vellido; Iván Olier; Enrique Romero; Margarida Julià-Sapé; José D Martín; Ian H Jarman; Carles Arús; Paulo J G Lisboa
Journal:  PLoS One       Date:  2013-12-23       Impact factor: 3.240

  4 in total

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