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Compatibility between 3T 1H SV-MRS data and automatic brain tumour diagnosis support systems based on databases of 1.5T 1H SV-MRS spectra

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Compatibility between 3T 1H SV-MRS data and automatic brain tumour diagnosis support systems based on databases of 1.5T 1H SV-MRS spectra

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Fuster García, E.; Navarro., C.; Vicente Robledo, J.; Tortajada Velert, S.; García Gómez, JM.; Sáez Silvestre, C.; Calvar ., J.... (2011). Compatibility between 3T 1H SV-MRS data and automatic brain tumour diagnosis support systems based on databases of 1.5T 1H SV-MRS spectra. Magnetic Resonance Materials in Physics, Biology and Medicine. 24(1):35-42. doi:10.1007/s10334-010-0241-8

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Title: Compatibility between 3T 1H SV-MRS data and automatic brain tumour diagnosis support systems based on databases of 1.5T 1H SV-MRS spectra
Author:
UPV Unit: Universitat Politècnica de València. Instituto Universitario de Aplicaciones de las Tecnologías de la Información - Institut Universitari d'Aplicacions de les Tecnologies de la Informació
Universitat Politècnica de València. Departamento de Física Aplicada - Departament de Física Aplicada
Issued date:
Abstract:
Object: This study demonstrates that 3T SV-MRS data can be used with the currently available automatic brain tumour diagnostic classifiers which were trained on databases of 1.5T spectra. This will allow the existing large ...[+]
Subjects: Brain tumours , Clinical decision support systems , Magnetic resonance spectroscopy
Copyrigths: Reserva de todos los derechos
Source:
Magnetic Resonance Materials in Physics, Biology and Medicine. (issn: 0968-5243 )
DOI: 10.1007/s10334-010-0241-8
Publisher:
Springer Verlag (Germany)
Publisher version: http://link.springer.com/article/10.1007/s10334-010-0241-8
Description: The final publication is available at Springer via http://dx.doi.org/10.1007/s10334-010-0241-8
Thanks:
We would like to thank Miriam Camison-Sanchez for help in the quality control of the MRS data and diagnosis validation. This work was partially funded by the European Commission (FP6-2002-LIFESCIHEALTH 503094) and ...[+]
Type: Artículo

References

García-Gómez JM, Luts J, Julià-Sapé M, Krooshof P, Tortajada S, Vicente Robledo J, Melssen W, Fuster-García E, Olier I, Postma G, Monleón D, Moreno-Torres À, Pujol J, Candiota AP, Martínez-Bisbal M, Suykens J, Buydens L, Celda B, Van Huffel S, Arús C, Robles M (2009) Multiproject-multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy. Magn Reson Mater Phy 22: 5–18

Lukas L, Devos A, Suykens JAK, Vanhamme L, Howe FA, Majós C, Moreno-Torres A, Vander Graaf M, Tate AR, Arús C, Van Huffel S (2004) Brain tumor classification based on long echo proton MRS signals. Artif Intell Med 31: 73–89

Devos A, Lukas L, Suykens JAK, Vanhamme L, Tate AR, Howe FA, Majos C, Moreno-Torres A, Van der Graaf M, Arús C, Van Huffel S (2004) Classification of brain tumours using short echo time 1H MR spectra. J Magn Reson 170: 164–175 [+]
García-Gómez JM, Luts J, Julià-Sapé M, Krooshof P, Tortajada S, Vicente Robledo J, Melssen W, Fuster-García E, Olier I, Postma G, Monleón D, Moreno-Torres À, Pujol J, Candiota AP, Martínez-Bisbal M, Suykens J, Buydens L, Celda B, Van Huffel S, Arús C, Robles M (2009) Multiproject-multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy. Magn Reson Mater Phy 22: 5–18

Lukas L, Devos A, Suykens JAK, Vanhamme L, Howe FA, Majós C, Moreno-Torres A, Vander Graaf M, Tate AR, Arús C, Van Huffel S (2004) Brain tumor classification based on long echo proton MRS signals. Artif Intell Med 31: 73–89

Devos A, Lukas L, Suykens JAK, Vanhamme L, Tate AR, Howe FA, Majos C, Moreno-Torres A, Van der Graaf M, Arús C, Van Huffel S (2004) Classification of brain tumours using short echo time 1H MR spectra. J Magn Reson 170: 164–175

Tate AR, Majós C, Moreno A, Howe FA, Griffiths JR, Arús C (2003) Automated classification of short echo time in in vivo 1H brain tumor spectra: a multicenter study. Magn Reson Med 49: 29–36

Tate AR, Underwood J, Acosta DM, Julià-Sapé M, Majós C, Moreno-Torres A, Howe FA, Van der Graaf M, Lefournier V, Murphy MM, Loosemore A, Ladroue C, Wesseling P, Luc Bosson J, Cabanas ME, Simonetti AW, Gajewicz W, Calvar J, Capdevila A, Wilkins PR, Bell BA, Remy C, Heerschap A, Watson D, Griffiths JR, Arús C (2006) Development of a decision support system for diagnosis and grading of brain tumours using in vivo magnetic resonance single voxel spectra. NMR Biomed 19: 411–434

Celda B, Monleón D, Martínez-Bisbal MC, Esteve V, Martínez-Granados B, Piñero E, Ferrer R, Piquer J, Martí-Bonmatí L, Cervera J (2006) MRS as endogenous molecular imaging for brain and prostate tumors: FP6 project “eTUMOR”. Adv Exp Med Biol 587: 285–302

Laudadio T, Martínez-Bisbal MC, Celda B,Van Huffel S (2008) Fast nosological imaging using canonical correlation analysis of brain data obtained by two-dimensional turbo spectroscopic imaging. NMR Biomed 21: 311–321

Martínez-Bisbal M, Celda B, Martí-Bonmatí L, Ferrer P, Revert A, Piquer J, Mollá E, Arana E, Dosdá R (2002) Contribution of magnetic resonance spectroscopy to the classification of hogh glial tumours. Predictive value of macromolecules. Rev Neurol 34: 309–313

Opstad K, Provencher S, Bell B, Griffiths J, Howe F (2009) Detection of elevated glutathione in meningiomas by quantitative in vivo 1H MRS. Magn Reson Med 49: 632–637

Sundgren P, Nagesh V, Elias A, Tsien C, Junck L, Gomez~Hassan D, Lawrence T, Chenevert T, Rogers L, McKeever P, Cao Y (2003) Metabolic alterations: a biomarker for radiation-induced normal brain injury-an MR spectroscopy study. J Magn Reson Imaging 29: 291–297

Hattingen E, Raab P, Franz K, Lanfermann H, Setzer M, Gerlach R, Zanella F, Pilatus U (2008) Prognostic value of choline and creatine in WHO grade II gliomas. Neuroradiology 50: 759–767

Server A, Josefsen R, Kulle B, Maehlen J, Schellhorn T, Gadmar, Kumar T, Haakonsen M, Langberg C, Nakstad P (2010) Proton magnetic resonance spectroscopy in the distinction of high-grade cerebral gliomas from single metastatic brain tumors. Acta Radiol 51: 316–325

Theodoridis S, Koutroumbas K (2006) Pattern recognition. 3rd edn. Academic Press, San Diego

Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, New York

Duda RO, Hart PE, Stork DG (2000) Pattern classification. 2nd edn. Wiley-Interscience, New York

Kim J, Chang K, Na DG, Song IC, Kim SJ, Kwon BJ, Han MH (2006) Comparison of 1.5T and 3T 1H-MR spectroscopy for human brain tumors. Korean J Radiol 7: 156–161

Barker PB, Hearshen DO, Boska MD (2001) Single-voxel proton MRS of the human brain at 1.5T and 3.0T. Magn Reson Med 45: 765–769

Roser W, Hagberg G, Mader I, Dellas S, Seelig J, Radue E, Steinbrich W (1997) Assignment of glial brain tumors in humans by in vivo 1H-magnetic resonance spectroscopy and multidimensional metabolic classification. Magn Reson Mater Phy 5: 179–183

Berner, ES (eds) (2007) Clinical decision support systems: theory and practice. Springer, New York

Sim I, Gorman P, Greenes RA, Haynes RB, Kaplan B, Lehmann H, Tang PC (2001) Clinical decision support systems for the practice of evidence-based medicine. J Am Med Inform Assn 8: 527–534

González-Vélez H, Mier M, Julià-Sapé M, Arvanitis T, García-Gómez JM, Robles M, Lewis P, Dasmahapatra S, Dupplaw D, Peet A, Arús C, Celda B, Van Huffel S, Lluch-Ariet M (2009) HealthAgents: distributed multi-agent brain tumor diagnosis and prognosis. Appl Intell 30: 191–202

Sáez C, García-Gómez JM, Vicente J, Tortajada S, Esparza M, Navarro A, Fuster-Garcia E, Robles M, Martí-Bonmatí L, Arús C (2008) A generic decision support system featuring an assembled view of predictive models for magnetic Resonance and clinical data. In: ESMRMB 25th annual meeting, Valencia, 2–4. Springer

García-Gómez JM, Tortajada S, Vidal C, Julia-Sape M, Luts J, Van Huffel S, Arús C, Robles M (2008) The influence of combining two echo times in automatic brain tumor classification by Magnetic Resonance Spectroscopy. NMR Biomed 21: 1112–1125

The eTUMOUR Consortium (2007) eTUMOUR: web accessible MR decision support system for brain tumour diagnosis and prognosis, incorporating in vivo and ex vivo genomic and metabolomic data, VI framework programme, EC, http://www.etumour.net . Technical Report FP6-2002-LIFESCIHEALTH 503094

Vander Graaf M, Juliá-Sapè M, Howe FA, Ziegler A, Majós C, Moreno-Torres A, Rijpkema M, Acosta D, Opstad KS, Van der Meulen Y, Arús C, Heerschap A (2008) MRS quality assessment in a multicentre study on MRS-based classification of brain tumours. NMR Biomed 21: 148–158

Pérez-Ruiz A, Olier-Caparroso IA, Julià-Sapé M, Candiota AP, Arús C (2008) Brain tumor diagnosis with MRS: the single voxel INTERPRET decision-support system version 2.0. In: ESMRMB 25th annual meeting, Valencia, 2–4. Magn Reson Mater Phy

Klose U (1990) In vivo proton spectroscopy in presence of eddy currents. Magn Reson Med 14: 26–30

Van den Boogaart A, Van Hecke P, Van Huffel S, Graveron-Demilly S, Van Ormondt D, de Beer R (1996) MRUI: a graphical user interface for accurate routine MRS data analysis. In: ESMRMB 13th annual meeting, Prague, 12–15. Springer

Luts J, Poullet J, Garcia-Gomez J, Heerschap A, Robles M, Suykens J, Van Huffel S (2008) Effect of feature extraction for brain tumor classification based on short echo time 1H MR spectra. Magn Reson Mater Phy 60: 88–98

Russell SJ, Norvig P (1995) Artificial intelligence: a modern approach. Prentice-Hall, Upper Saddle River

Opstad KS, Ladroue C, Bell BA, Griffiths JR, Howe FA (2007) Linear discriminant analysis of brain tumour 1H MR spectra: a comparison of classification using whole spectra versus metabolite quantification. NMR Biomed 20: 763–770

Fisher RA (1936) The use of multiple measurements in taxonomic problems. Ann Eugenic 7: 179–188

Kubat M, Matwin S (1997) Addressing the curse of imbalanced training sets: one-sided selection. In: Proceedings of 14th international conference on machine learning, pp 179–186. Morgan Kaufmann

Giraud-Carrier C (2000) A note on the utility of incremental learning. AI Commun 13: 215–223

Gonen O, Gruber S, Li B, Mlynrik V, Moser E (2001) Multivoxel 3D proton spectroscopy in the brain at 1.5 versus 3.0 T: signal-to-noise ratio and resolution comparison. Am J Neuroradiol 22: 1727–1731

Dydak U, Meier D, Lamerichs R, Boesiger P (2006) Effect of feature extraction for brain tumor classification based on short echo time 1H MR spectra. Am J Neuroradiol 27: 1441–1446

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