- -

The effect of combining two echo times in automatic brain tumor classification by MRS

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

The effect of combining two echo times in automatic brain tumor classification by MRS

Show full item record

García-Gómez, JM.; Tortajada, S.; Vidal, C.; Julià -Sapé, M.; Luts, J.; Moreno-Torres, À.; Van Huffel, S.... (2008). The effect of combining two echo times in automatic brain tumor classification by MRS. NMR in Biomedicine. 21(10):1112-1125. https://doi.org/10.1002/nbm.1288

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/151098

Files in this item

Item Metadata

Title: The effect of combining two echo times in automatic brain tumor classification by MRS
Author: García-Gómez, Juan M Tortajada, Salvador Vidal, César Julià -Sapé, Margalida Luts, Jan Moreno-Torres, Àngel Van Huffel, Sabine Arús, Carles Robles Viejo, Montserrat
UPV Unit: Universitat Politècnica de València. Departamento de Física Aplicada - Departament de Física Aplicada
Issued date:
Abstract:
[EN] H-1 MRS is becoming an accurate, non-invasive technique for initial examination of brain masses. We investigated if the combination of single-voxel H-1 MRS at 1.5 T at two different (TEs), short TE (PRESS or STEAM, ...[+]
Subjects: 1H MRS , Short TE , Long TE , Pattern recognition , Brain , Cancer , Decision support systems
Copyrigths: Cerrado
Source:
NMR in Biomedicine. (issn: 0952-3480 )
DOI: 10.1002/nbm.1288
Publisher:
John Wiley & Sons
Publisher version: https://doi.org/10.1002/nbm.1288
Project ID:
EC/FP6-2002-LIFESCIHEALTH 503094
UPV/PAID-00-06
EC/FP6-2005-IST 027213
EC/FP6-2002-IST 508803
BELSPO/P6/04
Thanks:
This work was partially funded by the European Commission: eTUMOUR (contract no. FP6-2002-LIFESCI-HEALTH 503094). HealthAgents (contract no. FP6-2005-IST 027213), BIOPATTERN (contract no. FP6-2002-IST 508803); Programa de ...[+]
Type: Artículo

References

Howe, F. A., & Opstad, K. S. (2003). 1H MR spectroscopy of brain tumours and masses. NMR in Biomedicine, 16(3), 123-131. doi:10.1002/nbm.822

Julià-Sapé, M., Acosta, D., Majós, C., Moreno-Torres, À., Wesseling, P., Acebes, J. J., … Arús, C. (2006). Comparison between neuroimaging classifications and histopathological diagnoses using an international multicenter brain tumor magnetic resonance imaging database. Journal of Neurosurgery, 105(1), 6-14. doi:10.3171/jns.2006.105.1.6

Lin, A. P., Tran, T. T., & Ross, B. D. (2006). Impact of evidence-based medicine on magnetic resonance spectroscopy. NMR in Biomedicine, 19(4), 476-483. doi:10.1002/nbm.1058 [+]
Howe, F. A., & Opstad, K. S. (2003). 1H MR spectroscopy of brain tumours and masses. NMR in Biomedicine, 16(3), 123-131. doi:10.1002/nbm.822

Julià-Sapé, M., Acosta, D., Majós, C., Moreno-Torres, À., Wesseling, P., Acebes, J. J., … Arús, C. (2006). Comparison between neuroimaging classifications and histopathological diagnoses using an international multicenter brain tumor magnetic resonance imaging database. Journal of Neurosurgery, 105(1), 6-14. doi:10.3171/jns.2006.105.1.6

Lin, A. P., Tran, T. T., & Ross, B. D. (2006). Impact of evidence-based medicine on magnetic resonance spectroscopy. NMR in Biomedicine, 19(4), 476-483. doi:10.1002/nbm.1058

Tate, A. R., Underwood, J., Acosta, D. M., Julià-Sapé, M., Majós, C., Moreno-Torres, À., … Arús, C. (2006). Development of a decision support system for diagnosis and grading of brain tumours usingin vivo magnetic resonance single voxel spectra. NMR in Biomedicine, 19(4), 411-434. doi:10.1002/nbm.1016

Galanaud, D., Nicoli, F., Chinot, O., Confort-Gouny, S., Figarella-Branger, D., Roche, P., … Cozzone, P. J. (2006). Noninvasive diagnostic assessment of brain tumors using combined in vivo MR imaging and spectroscopy. Magnetic Resonance in Medicine, 55(6), 1236-1245. doi:10.1002/mrm.20886

Barba, I., Moreno, Á., Martínez-Pérez, I., Tate, A. R., Cabañas, M. E., Baquero, M., … Arús, C. (2001). Magnetic resonance spectroscopy of brain hemangiopericytomas: high myoinositol concentrations and discrimination from meningiomas. Journal of Neurosurgery, 94(1), 55-60. doi:10.3171/jns.2001.94.1.0055

Howe, F. A., Barton, S. J., Cudlip, S. A., Stubbs, M., Saunders, D. E., Murphy, M., … Griffiths, J. R. (2003). Metabolic profiles of human brain tumors using quantitative in vivo1H magnetic resonance spectroscopy. Magnetic Resonance in Medicine, 49(2), 223-232. doi:10.1002/mrm.10367

Ishimaru, H., Morikawa, M., Iwanaga, S., Kaminogo, M., Ochi, M., & Hayashi, K. (2001). Differentiation between high-grade glioma and metastatic brain tumor using single-voxel proton MR spectroscopy. European Radiology, 11(9), 1784-1791. doi:10.1007/s003300000814

Kaminogo, M., Ishimaru, H., Morikawa, M., Ochi, M., Ushijima, R., Tani, M., … Shibata, S. (2001). Diagnostic potential of short echo time MR spectroscopy of gliomas with single-voxel and point-resolved spatially localised proton spectroscopy of brain. Neuroradiology, 43(5), 353-363. doi:10.1007/s002340000473

Lukas, L., Devos, A., Suykens, J. A. K., Vanhamme, L., Howe, F. A., Majós, C., … Van Huffel, S. (2004). Brain tumor classification based on long echo proton MRS signals. Artificial Intelligence in Medicine, 31(1), 73-89. doi:10.1016/j.artmed.2004.01.001

Gillies, R. J., & Morse, D. L. (2005). In Vivo Magnetic Resonance Spectroscopy in Cancer. Annual Review of Biomedical Engineering, 7(1), 287-326. doi:10.1146/annurev.bioeng.7.060804.100411

Lisboa, P. J., & Taktak, A. F. G. (2006). The use of artificial neural networks in decision support in cancer: A systematic review. Neural Networks, 19(4), 408-415. doi:10.1016/j.neunet.2005.10.007

Hagberg, G. (1998). From magnetic resonance spectroscopy to classification of tumors. A review of pattern recognition methods. NMR in Biomedicine, 11(4-5), 148-156. doi:10.1002/(sici)1099-1492(199806/08)11:4/5<148::aid-nbm511>3.0.co;2-4

Menze, B. H., Lichy, M. P., Bachert, P., Kelm, B. M., Schlemmer, H.-P., & Hamprecht, F. A. (2006). Optimal classification of long echo timein vivo magnetic resonance spectra in the detection of recurrent brain tumors. NMR in Biomedicine, 19(5), 599-609. doi:10.1002/nbm.1041

Simonetti, A. W., Melssen, W. J., Edelenyi, F. S. de, van Asten, J. J. A., Heerschap, A., & Buydens, L. M. C. (2005). Combination of feature-reduced MR spectroscopic and MR imaging data for improved brain tumor classification. NMR in Biomedicine, 18(1), 34-43. doi:10.1002/nbm.919

Huang, Y., Lisboa, P. J. G., & El-Deredy, W. (2002). Tumour grading from magnetic resonance spectroscopy: a comparison of feature extraction with variable selection. Statistics in Medicine, 22(1), 147-164. doi:10.1002/sim.1321

Tate, A. R., Majós, C., Moreno, A., Howe, F. A., Griffiths, J. R., & Arús, C. (2002). Automated classification of short echo time in in vivo1H brain tumor spectra: A multicenter study. Magnetic Resonance in Medicine, 49(1), 29-36. doi:10.1002/mrm.10315

Devos, A., Lukas, L., Suykens, J. A. K., Vanhamme, L., Tate, A. R., Howe, F. A., … Van Huffel, S. (2004). Classification of brain tumours using short echo time 1H MR spectra. Journal of Magnetic Resonance, 170(1), 164-175. doi:10.1016/j.jmr.2004.06.010

Suykens, J. A. K., & Vandewalle, J. (1999). Neural Processing Letters, 9(3), 293-300. doi:10.1023/a:1018628609742

Opstad, K. S., Ladroue, C., Bell, B. A., Griffiths, J. R., & Howe, F. A. (2007). Linear discriminant analysis of brain tumour1H MR spectra: a comparison of classification using whole spectra versus metabolite quantification. NMR in Biomedicine, 20(8), 763-770. doi:10.1002/nbm.1147

Kleihues, P., Burger, P. C., & Scheithauer, B. W. (1993). The New WHO Classification of Brain Tumours. Brain Pathology, 3(3), 255-268. doi:10.1111/j.1750-3639.1993.tb00752.x

Julià-Sapé, M., Acosta, D., Mier, M., Arùs, C., & Watson, D. (2006). A Multi-Centre, Web-Accessible and Quality Control-Checked Database of in vivo MR Spectra of Brain Tumour Patients. Magnetic Resonance Materials in Physics, Biology and Medicine, 19(1), 22-33. doi:10.1007/s10334-005-0023-x

Van der Graaf, M., Julià-Sapé, M., Howe, F. A., Ziegler, A., Majós, C., Moreno-Torres, A., … Heerschap, A. (2008). MRS quality assessment in a multicentre study on MRS-based classification of brain tumours. NMR in Biomedicine, 21(2), 148-158. doi:10.1002/nbm.1172

Klose, U. (1990). In vivo proton spectroscopy in presence of eddy currents. Magnetic Resonance in Medicine, 14(1), 26-30. doi:10.1002/mrm.1910140104

McIntyre, D. J. O., Charlton, R. A., Markus, H. S., & Howe, F. A. (2007). Long and short echo time proton magnetic resonance spectroscopic imaging of the healthy aging brain. Journal of Magnetic Resonance Imaging, 26(6), 1596-1606. doi:10.1002/jmri.21198

Ramsay, J. O., & Silverman, B. W. (Eds.). (2002). Applied Functional Data Analysis: Methods and Case Studies. Springer Series in Statistics. doi:10.1007/b98886

Vapnik, V. N. (1995). The Nature of Statistical Learning Theory. doi:10.1007/978-1-4757-2440-0

Kira K, Rendell L. A practical approach to feature selection. In Proceedings of the Ninth International Conference on Machine Learning, Morgan Kaufmann: San Francisco, CA, 1992; 249-256.

Kononenko, I. (1994). Estimating attributes: Analysis and extensions of RELIEF. Lecture Notes in Computer Science, 171-182. doi:10.1007/3-540-57868-4_57

Robnik-Šikonja, M., & Kononenko, I. (2003). Machine Learning, 53(1/2), 23-69. doi:10.1023/a:1025667309714

Martin JK, Hirschberg DS. Small sample statistics for classification error rates I: error rate measurements. Technical Report ICS-TR-96-22, 1996.

Berrar, D., Bradbury, I., & Dubitzky, W. (2006). Avoiding model selection bias in small-sample genomic datasets. Bioinformatics, 22(10), 1245-1250. doi:10.1093/bioinformatics/btl066

Kinoshita, Y., Kajiwara, H., Yokota, A., & Koga, Y. (1994). Proton Magnetic Resonance Spectroscopy of Brain Tumors. Neurosurgery, 35(4), 606-614. doi:10.1227/00006123-199410000-00005

Michaelis, T., Merboldt, K.-D., Hänicke, W., Gyngell, M. L., Bruhn, H., & Frahm, J. (1991). On the identification of cerebral metabolites in localized1H NMR spectra of human brainIn vivo. NMR in Biomedicine, 4(2), 90-98. doi:10.1002/nbm.1940040211

Martínez-Bisbal, M. C., Martí-Bonmatí, L., Piquer, J., Revert, A., Ferrer, P., Llácer, J. L., … Celda, B. (2004). 1H and13C HR-MAS spectroscopy of intact biopsy samplesex vivo andin vivo1H MRS study of human high grade gliomas. NMR in Biomedicine, 17(4), 191-205. doi:10.1002/nbm.888

KleihuesP, CavaneeW (eds). Pathology and Genetics of Tumours of the Nervous System. International Agency for Research on Cancer: Lyon, 1997.

Mischel, P. S., Shai, R., Shi, T., Horvath, S., Lu, K. V., Choe, G., … Nelson, S. F. (2003). Identification of molecular subtypes of glioblastoma by gene expression profiling. Oncogene, 22(15), 2361-2373. doi:10.1038/sj.onc.1206344

Phillips, H. S., Kharbanda, S., Chen, R., Forrest, W. F., Soriano, R. H., Wu, T. D., … Aldape, K. (2006). Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis. Cancer Cell, 9(3), 157-173. doi:10.1016/j.ccr.2006.02.019

Law, M., Cha, S., Knopp, E. A., Johnson, G., Arnett, J., & Litt, A. W. (2002). High-Grade Gliomas and Solitary Metastases: Differentiation by Using Perfusion and Proton Spectroscopic MR Imaging. Radiology, 222(3), 715-721. doi:10.1148/radiol.2223010558

[-]

recommendations

 

This item appears in the following Collection(s)

Show full item record