Literature DB >> 16763971

Development of a decision support system for diagnosis and grading of brain tumours using in vivo magnetic resonance single voxel spectra.

Anne R Tate1, Joshua Underwood, Dionisio M Acosta, Margarida Julià-Sapé, Carles Majós, Angel Moreno-Torres, Franklyn A Howe, Marinette van der Graaf, Virginie Lefournier, Mary M Murphy, Alison Loosemore, Christophe Ladroue, Pieter Wesseling, Jean Luc Bosson, Miquel E Cabañas, Arjan W Simonetti, Witold Gajewicz, Jorge Calvar, Antoni Capdevila, Peter R Wilkins, B Anthony Bell, Chantal Rémy, Arend Heerschap, Des Watson, John R Griffiths, Carles Arús.   

Abstract

A computer-based decision support system to assist radiologists in diagnosing and grading brain tumours has been developed by the multi-centre INTERPRET project. Spectra from a database of 1H single-voxel spectra of different types of brain tumours, acquired in vivo from 334 patients at four different centres, are clustered according to their pathology, using automated pattern recognition techniques and the results are presented as a two-dimensional scatterplot using an intuitive graphical user interface (GUI). Formal quality control procedures were performed to standardize the performance of the instruments and check each spectrum, and teams of expert neuroradiologists, neurosurgeons, neurologists and neuropathologists clinically validated each case. The prototype decision support system (DSS) successfully classified 89% of the cases in an independent test set of 91 cases of the most frequent tumour types (meningiomas, low-grade gliomas and high-grade malignant tumours--glioblastomas and metastases). It also helps to resolve diagnostic difficulty in borderline cases. When the prototype was tested by radiologists and other clinicians it was favourably received. Results of the preliminary clinical analysis of the added value of using the DSS for brain tumour diagnosis with MRS showed a small but significant improvement over MRI used alone. In the comparison of individual pathologies, PNETs were significantly better diagnosed with the DSS than with MRI alone. Copyright 2006 John Wiley & Sons, Ltd.

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Year:  2006        PMID: 16763971     DOI: 10.1002/nbm.1016

Source DB:  PubMed          Journal:  NMR Biomed        ISSN: 0952-3480            Impact factor:   4.044


  52 in total

1.  Proton MR spectroscopy provides relevant prognostic information in high-grade astrocytomas.

Authors:  C Majós; J Bruna; M Julià-Sapé; M Cos; A Camins; M Gil; J J Acebes; C Aguilera; C Arús
Journal:  AJNR Am J Neuroradiol       Date:  2010-10-28       Impact factor: 3.825

2.  Molecular classification of brain tumor biopsies using solid-state magic angle spinning proton magnetic resonance spectroscopy and robust classifiers.

Authors:  Ovidiu C Andronesi; Konstantinos D Blekas; Dionyssios Mintzopoulos; Loukas Astrakas; Peter M Black; A Aria Tzika
Journal:  Int J Oncol       Date:  2008-11       Impact factor: 5.650

Review 3.  Glutamate and glutamine: a review of in vivo MRS in the human brain.

Authors:  Saadallah Ramadan; Alexander Lin; Peter Stanwell
Journal:  NMR Biomed       Date:  2013-10-04       Impact factor: 4.044

4.  Fast spectroscopic multiple analysis (FASMA) for brain tumor classification: a clinical decision support system utilizing multi-parametric 3T MR data.

Authors:  Evangelia Tsolaki; Patricia Svolos; Evanthia Kousi; Eftychia Kapsalaki; Ioannis Fezoulidis; Konstantinos Fountas; Kyriaki Theodorou; Constantine Kappas; Ioannis Tsougos
Journal:  Int J Comput Assist Radiol Surg       Date:  2014-07-15       Impact factor: 2.924

5.  Magnetic resonance spectroscopy in posterior fossa tumours: the tumour spectroscopic signature may improve discrimination in adults among haemangioblastoma, ependymal tumours, medulloblastoma, and metastasis.

Authors:  Paloma Mora; Albert Pons; Mónica Cos; Angels Camins; Amadeo Muntané; Carles Aguilera; Carles Arús; Carles Majós
Journal:  Eur Radiol       Date:  2018-12-19       Impact factor: 5.315

6.  Malignancy probability map as a novel imaging biomarker to predict malignancy distribution: employing MRS in GBM patients.

Authors:  Manijeh Beigi; Kevan Ghasemi; Parvin Mirzaghavami; Mohammadreza Khanmohammadi; Hamidreza SalighehRad
Journal:  J Neurooncol       Date:  2018-03-14       Impact factor: 4.130

7.  Ex-vivo HRMAS of adult brain tumours: metabolite quantification and assignment of tumour biomarkers.

Authors:  Alan J Wright; Greg A Fellows; John R Griffiths; M Wilson; B Anthony Bell; Franklyn A Howe
Journal:  Mol Cancer       Date:  2010-03-23       Impact factor: 27.401

8.  Artificial neural networks for classification in metabolomic studies of whole cells using 1H nuclear magnetic resonance.

Authors:  D F Brougham; G Ivanova; M Gottschalk; D M Collins; A J Eustace; R O'Connor; J Havel
Journal:  J Biomed Biotechnol       Date:  2010-09-15

9.  Multiproject-multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy.

Authors:  Juan M García-Gómez; Jan Luts; Margarida Julià-Sapé; Patrick Krooshof; Salvador Tortajada; Javier Vicente Robledo; Willem Melssen; Elies Fuster-García; Iván Olier; Geert Postma; Daniel Monleón; Angel Moreno-Torres; Jesús Pujol; Ana-Paula Candiota; M Carmen Martínez-Bisbal; Johan Suykens; Lutgarde Buydens; Bernardo Celda; Sabine Van Huffel; Carles Arús; Montserrat Robles
Journal:  MAGMA       Date:  2008-11-07       Impact factor: 2.310

Review 10.  In vivo magnetic resonance spectroscopy: basic methodology and clinical applications.

Authors:  Marinette van der Graaf
Journal:  Eur Biophys J       Date:  2009-08-13       Impact factor: 1.733

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