Skip to content


BMC Proceedings

Open Access

Improving the classification of brain tumors in mice with perturbation enhanced (PE)-MRSI

  • Rui V Simões1, 2Email author,
  • Sandra Ortega-Martorell1, 2, 3,
  • Teresa Delgado-Goñi1, 2,
  • Yann le Fur4,
  • Marti Pumarola5,
  • Ana P Candiota1, 2,
  • Patrick J Cozzone4,
  • Margarida Juliá-Sapè1, 2, 3 and
  • Carles Arús1, 2, 3
BMC Proceedings20104(Suppl 2):P65

Published: 24 September 2010


Brain TumorGlioblastoma MultiformeOligodendrogliomaHuman Brain TumorAcute Hyperglycemia

Classifiers based on pattern recognition analysis of MRS(I) data are becoming important tools for the non-invasive diagnosis of human brain tumors [1, 2]. Here we investigate the potential interest of perturbation-enhanced MRSI (PE-MRSI), in this case acute hyperglycemia, during moderate brain hypothermia [3, 4], for improving the discrimination between mouse brain MRS patterns of glioblastoma multiforme (GBM), low grade oligodendroglioma (ODG2), and non-tumor brain parenchyma (NT). Six GBM-bearing mice and two ODG2-bearing mice were scanned at 7 Tesla by PRESS-MRSI with 12 and 136 ms echo-time, during euglycemia (Eug) and also during induced acute hyperglycemia (Hyp), generating altogether four datasets per animal (echo time + glycemic condition): 12Eug, 136Eug, 12Hyp, and 136Hyp. For classifier development, using in-house built software SpectraClassifier 2.0 [5, 6], all spectral vectors (spv) selected were unit length normalized (UL2) and used either as training set (76 GBM spv, four mice; 70 ODG2 spv, 2 mice; 54 NT spv, 6 mice) or as independent test set (2 mice, 61 GBM spv and 17 NT spv). All Fisher’s LDA classifiers obtained had very good descriptive performance when extracting at least 10 features from the training sets as evaluated by Bootstrapping: correctly classified cases ≥ 99 %. Evaluation of predictive performance with the independent test set clearly revealed that 12Hyp MRSI-based classifiers with at least 5 features provided the best robustness: balanced error rate (BER) for spv prediction < 0.9 %. This highlights the potential interest of perturbation-enhanced MRSI protocols for improving the non-invasive characterization of brain tumors at a preclinical level.

Authors’ Affiliations

Bioquímica i Biologia Molecular, Facultat de Biociències, Universitat Autònoma de Barcelona, Barcelona, Spain
Centro de Investigación Biomédica en Red – Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Spain
Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, Barcelona, Spain
Centre de Résonance Magnétique Biologique et Médicale (CRMBM) UMR CNRS 6612, Marseille, France
Banc de Teixits Animals de Catalunya, Departament de Medicina i Cirurgia Animals, Universitat Autònoma de Barcelona, Barcelona, Spain


  1. De Edelenyi FS, Rubin C, Estève F, Grand S, Décorps M, Lefournier V, Le Bas J-F, Rémy C: A new approach for analyzing proton magnetic resonance spectroscopic images of brain tumors: nosologic images. Nat Med. 2000, 6: 1287-1289. 10.1038/81401.View ArticlePubMedGoogle Scholar
  2. 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, Cabañas ME, Simonetti AW, Gajewicz W, Calvar J, Capdevila A, Wilkins PR, Bell BA, Rémy C, Heerschap A, Watson D, Griffiths JR, Arús C: 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: 411-434. 10.1002/nbm.1016.View ArticlePubMedGoogle Scholar
  3. Simões RV, García-Martín ML, Cerdán S, Arús C: Perturbation of mouse glioma MRS pattern by induced acute hyperglycemia. NMR Biomed. 2008, 21: 251-264. 10.1002/nbm.1188.View ArticlePubMedGoogle Scholar
  4. Simões RV, Delgado-Goñi T, Lope-Piedrafita S, Arús C: 1H-MRSI pattern perturbation in a mouse glioma: the effects of acute hyperglycemia and moderate hypothermia. NMR Biomed. 2010, 23: 23-33. 10.1002/nbm.1421.View ArticlePubMedGoogle Scholar
  5. Ortega-Martorell S, Olier I, Juliá-Sapè M, Arús C: SpectraClassifier 1.0: a user friendly, automated MRS-based classifier-development system. BMC Bioinformatics. 2010, 11: 106-10.1186/1471-2105-11-106.PubMed CentralView ArticlePubMedGoogle Scholar
  6. SpectraClassifier (SC). []


© Simões et al; licensee BioMed Central Ltd. 2010

This article is published under license to BioMed Central Ltd.