Publication:
The INTERPRET Decision-Support System version 3.0 for evaluation of Magnetic Resonance Spectroscopy data from human brain tumours and other abnormal brain masses

Thumbnail Image
Files

Files

The INTERPRET decision- support system version 3.0 for evaluation of Magnetic Resonance Spectroscopy data from human brain tumours and other abnormal brain masses.pdf (3.31 MB)
View Statistics
Reference Managers
Mendeley
Indexers
Google Scholar CORE
QR Code
QR Code
Authors

Authors

Julià-Sapé, Margarida
Mercadal, Guillem
Olier, Iván
Majós, Carles
Arus, Carles
Pérez Ruiz, Alexander
Abstract (Spanish)
Antecedentes: La espectroscopia por resonancia magnética (RM) de protones es una técnica ampliamente disponible para aquellos centros clínicos equipados con escáneres de RM. A diferencia del resto de técnicas basadas en la RM, la MRS no produce imágenes sino sino espectros de metabolitos en los tejidos. En situaciones patológicas, el perfil de MRS cambia y esto se ha descrito especialmente en tumores cerebrales. especialmente en los tumores cerebrales. Sin embargo, los radiólogos no suelen estar familiarizados con la interpretación de los datos de MRS, por lo que se ha estudiado la utilidad de los sistemas de ayuda a la toma de decisiones (DSS) en el análisis de datos de MRS. Resultados: Este trabajo presenta la versión 3.0 de INTERPRET DSS, analizando las mejoras introducidas desde su primera versión en 2002. en 2002. La versión 3.0 pretende ser un programa que, en primer lugar, se pueda utilizar fácilmente con cualquier caso nuevo de cualquier fabricante de escáneres de RM y, en segundo lugar, mejore el análisis inicial de los datos. de cualquier fabricante de escáneres de RM y, en segundo lugar, mejore las capacidades de análisis iniciales de la primera versión. Las principales mejoras son base de datos integrada, cuentas de usuario, más capacidades de discriminación diagnóstica y la posibilidad de analizar datos adquiridos en condiciones de adquisición de datos adicionales. Otras mejoras son una interfaz gráfica de usuario (GUI) personalizable. interfaz gráfica de usuario (GUI) personalizable. La mayoría de los problemas de diagnóstico incluidos se han abordado mediante un enfoque basado en el reconocimiento de patrones en el que se han entrenado y probado clasificadores basados en el análisis discriminante lineal (LDA). Conclusiones: El INTERPRET DSS 3.0 permite a radiólogos, físicos médicos, bioquímicos o, en general, a cualquier persona con un conocimiento mínimo de lo que es un espectro de RM, introducir sus propios datos SV en bruto, adquiridos a 1,5 T, y analizarlos. Se espera que el sistema ayude a clasificar los espectros de RM de masas cerebrales anormales.
Abstract (English)
Background: Proton Magnetic Resonance (MR) Spectroscopy (MRS) is a widely available technique for those clinical centres equipped with MR scanners. Unlike the rest of MR-based techniques, MRS yields not images but spectra of metabolites in the tissues. In pathological situations, the MRS profile changes and this has been particularly described for brain tumours. However, radiologists are frequently not familiar to the interpretation of MRS data and for this reason, the usefulness of decision-support systems (DSS) in MRS data analysis has been explored. Results: This work presents the INTERPRET DSS version 3.0, analysing the improvements made from its first release in 2002. Version 3.0 is aimed to be a program that 1st, can be easily used with any new case from any MR scanner manufacturer and 2nd, improves the initial analysis capabilities of the first version. The main improvements are an embedded database, user accounts, more diagnostic discrimination capabilities and the possibility to analyse data acquired under additional data acquisition conditions. Other improvements include a customisable graphical user interface (GUI). Most diagnostic problems included have been addressed through a pattern-recognition based approach, in which classifiers based on linear discriminant analysis (LDA) were trained and tested. Conclusions: The INTERPRET DSS 3.0 allows radiologists, medical physicists, biochemists or, generally speaking, any person with a minimum knowledge of what an MR spectrum is, to enter their own SV raw data, acquired at 1.5 T, and to analyse them. The system is expected to help in the categorisation of MR Spectra from abnormal brain masses.
Extent
12 páginas
URI: https://repositorio.escuelaing.edu.co/handle/001/2325
Collections

Collections

AB - Ecitrónica
References

Julia-Sape M, Acosta D, Majos C, Moreno-Torres A, Wesseling P, Acebes JJ, Griffiths JR, Arus C: Comparison between neuroimaging classifications and histopathological diagnoses using an international multicenter brain tumor magnetic resonance imaging database. J Neurosurg 2006, 105(1):6-14

Negendank W: Studies of human tumors by MRS: a review. NMR Biomed 1992, 5(5):303-324.

Taylor JS: The trouble with spectroscopy papers, 15 years later. NMR Biomed 2006, 19(4):409-410.

Tate AR, Underwood J, Acosta DM, Julia-Sape M, Majos C, Moreno-Torres A, Howe FA, van der Graaf M, Lefournier V, Murphy MM, et al: Development of a decision support system for diagnosis and grading of brain tumours using in vivo magnetic resonance single voxel spectra. NMR Biomed 2006, 19(4):411-434.

Julia-Sape M, Acosta D, Mier M, Arus C, Watson D: A multi-centre, webaccessible and quality control-checked database of in vivo MR spectra of brain tumour patients. Magn Reson Mater Phy 2006, 19(1):22-33.

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

van der Graaf M, Julia-Sape M, Howe FA, Ziegler A, Majos C, MorenoTorres A, Rijpkema M, Acosta D, Opstad KS, van der Meulen YM, et al: MRS quality assessment in a multicentre study on MRS-based classification of brain tumours. NMR Biomed 2008, 21(2):148-158.

Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, Burger PC, Jouvet A, Scheithauer BW, Kleihues P: The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol 2007, 114(2):97-109.

Daumas-Duport C: Histological grading of gliomas. Curr Opin Neurol Neurosurg 1992, 5(6):924-931.

Naressi A, Couturier C, Castang I, de Beer R, Graveron-Demilly D: Javabased graphical user interface for MRUI, a software package for quantitation of in vivo/medical magnetic resonance spectroscopy signals. Comput Biol Med 2001, 31(4):269-286.

Naressi A, Couturier C, Devos JM, Janssen M, Mangeat C, de Beer R, Graveron-Demilly D: Java-based graphical user interface for the MRUI quantitation package. Magn Reson Mater Phy 2001, 12(2-3):141-152.

Pijnappel WWF, van den Boogaart A, de Beer R, van Ormondt D: SVDbased quantification of magnetic resonance signals. Journal of Magnetic Resonance (1969) 1992, 97(1):122-134.

Garcia-Gomez JM, Tortajada S, Vidal C, Julia-Sape M, Luts J, MorenoTorres A, Van Huffel S, Arus C, Robles M: The effect of combining two echo times in automatic brain tumor classification by MRS. NMR Biomed 2008, 21(10):1112-1125.

Majos C, Aguilera C, Alonso J, Julia-Sape M, Castaner S, Sanchez JJ, Samitier A, Leon A, Rovira A, Arus C: Proton MR spectroscopy improves discrimination between tumor and pseudotumoral lesion in solid brain masses. AJNR Am J Neuroradiol 2009, 30(3):544-551.

Devos A, Lukas L, Suykens JA, Vanhamme L, Tate AR, Howe FA, Majos C, Moreno-Torres A, van der Graaf M, Arus C, et al: Classification of brain tumours using short echo time 1 H MR spectra. J Magn Reson 2004, 170(1):164-175.

Lukas L, Devos A, Suykens JA, Vanhamme L, Howe FA, Majos C, MorenoTorres A, Van der Graaf M, Tate AR, Arus C, et al: Brain tumor classification based on long echo proton MRS signals. Artif Intell Med 2004, 31(1):73-89.

Majos C, Julia-Sape M, Alonso J, Serrallonga M, Aguilera C, Acebes JJ, Arus C, Gili J: Brain tumor classification by proton MR spectroscopy: comparison of diagnostic accuracy at short and long TE. AJNR Am J Neuroradiol 2004, 25(10):1696-1704.

Ortega-Martorell S, Olier I, Julia-Sape M, Arus C: SpectraClassifier 1.0: a user friendly, automated MRS-based classifier-development system. BMC Bioinformatics 2010, 11:106.

Julià-Sapé M, Majós C, Ortega-Martorell S, Olier I, Cos M, Aguilera C, Arús C: Choosing optimal classifiers for 1.5 T SV 1H-MRS data for pseudotumoural brain diseases. Magn Reson Mater Phy: October 2009 2009; Antalya, Turkey 2009, 322

Mercadal G, Julià-Sapé M, Coronel I, Aguilera C, Underwood J, Arús C: Version 1.2 of an interactive decision-support system for brain tumour diagnosis using single voxel MRS data. Magn Reson Mater Phy: 2005; Basel, Switzerland 2005, 218-219.

Nelson SJ, Graves E, Pirzkall A, Li X, Antiniw Chan A, Vigneron DB, McKnight TR: In vivo molecular imaging for planning radiation therapy of gliomas: an application of 1 H MRSI. J Magn Reson Imaging 2002, 16(4):464-476.

Luts J, Laudadio T, Idema AJ, Simonetti AW, Heerschap A, Vandermeulen D, Suykens JA, Van Huffel S: Nosologic imaging of the brain: segmentation and classification using MRI and MRSI. NMR Biomed 2009, 22(4):374-390.

Simonetti AW, Melssen WJ, van der Graaf M, Postma GJ, Heerschap A, Buydens LM: A chemometric approach for brain tumor classification using magnetic resonance imaging and spectroscopy. Anal Chem 2003, 75(20):5352-5361.

Preul MC, Caramanos Z, Collins DL, Villemure JG, Leblanc R, Olivier A, Pokrupa R, Arnold DL: Accurate, noninvasive diagnosis of human brain tumors by using proton magnetic resonance spectroscopy. Nat Med 1996, 2(3):323-325.

Callot V, Galanaud D, Le Fur Y, Confort-Gouny S, Ranjeva JP, Cozzone PJ: (1) H MR spectroscopy of human brain tumours: a practical approach. Eur J Radiol 2008, 67(2):268-274.

Howe FA, Opstad KS: 1 H MR spectroscopy of brain tumours and masses. NMR Biomed 2003, 16(3):123-131.

Julià-Sapé M, Majós C, Arús C: Diagnosis and Staging of Brain Tumours: Magnetic Resonance Single Voxel Spectra. In Methods of Cancer Diagnosis, Therapy, and Prognosis. Volume 8. Edited by: Hayat MA. Springer: the Netherlands; 2010:227-243.

Opstad KS, Murphy MM, Wilkins PR, Bell BA, Griffiths JR, Howe FA: Differentiation of metastases from high-grade gliomas using short echo time 1 H spectroscopy. J Magn Reson Imaging 2004, 20(2):187-192.

Garcia-Gomez JM, Luts J, Julia-Sape M, Krooshof P, Tortajada S, Robledo JV, Melssen W, Fuster-Garcia E, Olier I, Postma G, et al: Multiprojectmulticenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy. Magn Reson Mater Phy 2009, 22(1):5-18.

De Edelenyi FS, Rubin C, Esteve F, Grand S, Decorps M, Lefournier V, Le Bas JF, Remy C: A new approach for analyzing proton magnetic resonance spectroscopic images of brain tumors: nosologic images. Nat Med 2000, 6(11):1287-1289.