Brain tumor classification based on long echo proton MRS signals

Artificial Intelligence in Medicine

Volumn 31, Issue 1, 2004, Pages 73-89

  Lukas, L ^a   Devos, A ^a   Suykens, J A K ^a   Vanhamme, L ^a   Howe, F A ^b   Majos C ^c   Moreno Torres, A ^d   Van Der Graaf, M ^e   Tate, A R ^b   Arus C ^f   Van Huffel, S ^a  

^a Katholieke Universiteit Leuven   (Belgium)

^b ST GEORGE'S UNIVERSITY OF LONDON   (United Kingdom)

^c CSU de Bellvitge   (Spain)

^d Centre Diagnostic Pedralbes   (Spain)

^e RADBOUD UNIVERSITY MEDICAL CENTER   (Netherlands)

^f UNIVERSITAT AUTÒNOMA DE BARCELONA   (Spain)

Author keywords

Brain tumors; Classification; Least squares support vector machine (LS SVM); Linear discriminant analysis (LDA); Magnetic resonance spectroscopy (MRS); Support vector machine (SVM)

Indexed keywords

DATA ACQUISITION; LEAST SQUARES APPROXIMATIONS; MAGNETIC RESONANCE SPECTROSCOPY; PARAMETER ESTIMATION; PROTONS; SIGNAL PROCESSING; TUMORS; VECTORS;

RADIAL BASIS FUNCTION (RBF); RECEIVER OPERATING CHARACTERISTICS (ROC); SUPPORT VECTOR MACHINES (SVM);

BIOMEDICAL ENGINEERING;

ARTICLE; ASTROCYTOMA; BRAIN METASTASIS; BRAIN TUMOR; CANCER CLASSIFICATION; GLIOBLASTOMA; INFORMATION PROCESSING; INTERMETHOD COMPARISON; LINEAR SYSTEM; MATHEMATICAL ANALYSIS; MENINGIOMA; NONLINEAR SYSTEM; PRIORITY JOURNAL; PROTON NUCLEAR MAGNETIC RESONANCE; SAMPLING;

ARTIFICIAL INTELLIGENCE; ASTROCYTOMA; BRAIN NEOPLASMS; DIAGNOSIS, COMPUTER-ASSISTED; DISCRIMINANT ANALYSIS; HUMANS; MAGNETIC RESONANCE SPECTROSCOPY; MENINGEAL NEOPLASMS; MENINGIOMA; NEOPLASM METASTASIS;

EID: 2942620732     PISSN: 09333657     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artmed.2004.01.001     Document Type: Article

References (43)

1. The Brain Tumor Society. http://www.tbts.org.
2. Mukherji SK, editor. Clinical applications of magnetic resonance spectroscopy. Wiley-Liss, 1998.
3. Smith I.C.P., Stewart L.C. Magnetic resonance spectroscopy in medicine: clinical impact. Prog. Nucl. Mag. Res. Sp. 40:2002;1-34
4. Cousins J.P. Clinical MR spectroscopy: fundamentals, current applications, and future potential. AJR Am. J. Roentgenol. 164:1995;1337-1347
5. Lindon J.C., Holmes E., Nicholson J.K. Pattern recognition methods and applications in biomedical magnetic resonance. Prog. Nucl. Mag. Res. Sp. 39:2001;1-40
6. Herminghaus S, Dierks T, Pilatus U, Möller-Hartmann W, Wittsack J, Marquardt G, et al. Determination of histopathological tumor grade in neuroepithelial brain tumors by using spectral pattern analysis of in vivo spectroscopic data. J Neurosurg 2003;98:74-81.
7. Simonetti A.W., Melssen W.J., van der Graaf M., Heerschap A., Buydens L.M.C. Brain tumor classification and probability maps using MRI and MRSI data. Anal. Chem. 75(20):2003;5352-5361
8. Szabo De Edelenyi F, Rubin C, Estève F, Grand S, Décorps M, Lefournier V, et al. Nature Med. 2000;6:1287-9.
9. Tate AR, Griffiths JR, Martínez-Pérez I, Moreno A, Barba I, Cabañas ME, et al. Towards a method for automated classification of 1H MRS spectra from brain tumours. NMR Biomed 1998;11:177-91.
10. 1 H brain tumor spectra: a multicenter study Magn. Reson. Med. 49:2003;29-36
11. Ye C.-Z., Yang J., Geng D.-Y., Zhou Y., Chen N.-Y. Fuzzy rules to predict degree of malignancy in brain glioma. Med. Biol. Eng. Comput. 40:2002;145-152
12. Swets J.A. ROC analysis applied to the evaluation of medical imaging techniques. Invest. Radiol. 14(2):1979;109-121
13. International network for pattern recognition of tumours using magnetic resonance. http://carbon.uab.es/INTERPRET/.
14. Ladroue C, Tate AR, Howe FA, Griffiths JR. Exploring magnetic resonance data with independent component analysis. In: Proceedings of the 19th Annual Meeting of the European Society for Magnetic Resonance in Medicine and Biology (ESMRMB02), Cannes, France, August 22-25, 2002. p. 147-8.
15. 1 H MRSI: clinical performance. In: Proceedings of the 19th Annual Meeting of the European Society for Magnetic Resonance in Medicine and Biology (ESMRMB02), Cannes, France, August 22-25, 2002. p. 91-2.
16. Lukas L, Devos A, Suykens JAK, Vanhamme L, Van Huffel S, Tate AR, et al. The use of LS-SVM in the classification of brain tumors based on magnetic resonance spectroscopy signals. In: Proceedings of the European Symposium for Artifical Neural Networks (ESANN), Bruges, Belgium, April 24-26, 2002. p. 131-5.
17. 1 H-MR spectroscopy signals. In: Proceedings of the IEE Symposium on Medical Applications of Signal Processing, Savoy Place, London, UK, October 7, 2002. p. 15/1-5.
18. Szabo De Edelenyi F, Estève F, Rémy C, Buydens L. Proceedings of the 19th Annual Meeting of the European Society for Magnetic Resonance in Medicine and Biology (ESMRMB02), Cannes, France, August 22-25, 2002. p. 91.
19. Tate AR, Griffiths JR, Howe FA, Pujol J, Arús C. Differentiating types of human brain tumours by MRS. A comparison of pre-processing methods and echo times. In: Proceedings of the Ninth Scientific Meeting & Exhibition (ISMRM01), Glasgow, Scotland, April 21-27, 2001. p. 2284.
20. Vapnik V. The nature of statistical learning theory. New York: Springer, 1995.
21. Vapnik V. Statistical learning theory. New York: Wiley, 1998.
22. Suykens J.A.K., Vandewalle J. Least squares support vector machine classifiers. Neur. Proc. Lett. 9(3):1999;293-300
23. Klose U. In vivo proton spectroscopy in presence of eddy currents. Magn. Reson. Med. 14:1990;26-30
24. Barkhuijsen H., De Beer R., Van Ormondt D. Improved algorithm for noniterative time-domain model fitting to exponentially damped magnetic resonance signals. J. Magn. Reson. 73:1987;553-557
25. Duda RO, Hart PE, Stork DG. Pattern classification. 2nd ed. New York: Wiley, 2001.
26. Ripley BD. Pattern recognition and neural networks. Cambridge: Cambridge University Press, 1996.
27. Suykens JAK, Van Gestel T, De Brabanter J, De Moor B, Vandewalle J. Least squares support vector machines. Singapore: World Scientific, 2002.
28. Gunn SR. Support vector machines for classification and regression. Technical Report. Image Speech and Intelligent Systems Research Group, University of Southampton, 1997.
29. MATLAB support vector machines toolbox. http://www.isis.ecs.soton.ac.uk/ isystems/kernel.
30. MATLAB/C LS-SVMlab toolbox. http://www.esat.kuleuven.ac.be/sista/ lssvmlab.
31. Pelckmans K, Suykens JAK, Van Gestel T, De Brabanter J, Lukas L, Hamers B, et al. LS-SVMlab Toolbox User's Guide. Internal Report 02-145. ESAT-SISTA, K. U. Leuven, Leuven, Belgium, 2002.
32. 1 H magnetic resonance spectroscopy. Magn Reson Med 2003;49:223-32.
33. 1 H) in the diagnosis and management of patients with brain tumors Curr. Opin. Oncol. 14:2002;292-298
34. Majós C, Alonso J, Aguilera C, Serrallonga M, Acebes JJ, Arús C, et al. Adult primitive neuroectodermal tumor: proton MR spectroscopic findings with possible application for differential diagnosis. Radiology 2002;225:556-66.
35. 1 H MRS) to clinical brain tumour diagnosis. Br J Neurosurg 2002;16(4):329-34.
36. Poptani H., Kaartinen J., Gupta R.K., Niemitz M., Hiltunen Y., Kauppinen R.A. Diagnostic assessment of brain tumours and non-neoplastic brain disorders in vivo using proton nuclear magnetic resonance spectroscopy and artificial neural networks. J. Cancer Res. Clin. Oncol. 125:1999;343-349
37. Hanley J.A., McNeil B.J. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology. 148:1983;839-843
38. Srinivasan A. Note on the location of optimal classifiers in n -dimensional ROC space. Technical Report PRG-TR-2-99. Oxford University Computing Laboratory, Oxford, England, 1999.
39. Ishimaru H., Morikawa M., Iwanaga S., Kaminogo M., Ochi M., Hayashi K. Differentiation between high-grade glioma and metastastic brain tumor using single-voxel proton MR spectroscopy. Eur. Radiol. 11:2001;1784-1791
40. Law M., Cha S., Knopp E.A., Johnson G., Arnett J., Litt A.W. High-grade gliomas and solitary metastases: differentiation by using perfusion and proton spectroscopic MR imaging. Radiology. 222:2002;715-721
41. Opstad KS, Griffiths JR, Bell BA, Howe FA. In vivo lipid T2 relaxation time measurements in high-grade tumors: differentiation of glioblastomas and metastases. In: Proceedings of the 11th Scientific Meeting and Exhibition (ISMRM 03), Toronto, Canada, July 10-16, 2003, p. 754.
42. Ernst T., Hennig J. Coupling effects in volume selective 1H spectroscopy of major brain metabolites. Magn. Reson. Med. 21:1991;82-96
43. Govindaraju V., Young K., Maudsley A.A. Proton NMR chemical shifts and coupling constants for brain metabolites. NMR Biomed. 13:2000;129-153