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

Rui Vasco Simões, Sandra Ortega-Martorell, Teresa Delgado-Goñi, Yann Le Fur, Martí Pumarola, Ana Paula Candiota, Juana Martín, Radka Stoyanova, Patrick J. Cozzone, Margarida Julià-Sapé, Carles Arús

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15 Scopus citations


Classifiers based on statistical pattern recognition analysis of MRSI data are becoming important tools for the non-invasive diagnosis of human brain tumors. Here we investigate the potential interest of perturbation-enhanced MRSI (PE-MRSI), in this case acute hyperglycemia, for improving the discrimination between mouse brain MRS patterns of glioblastoma multiforme (GBM), oligodendroglioma (ODG), and non-tumor brain parenchyma (NT). Six GBM-bearing mice and three ODG-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 all spectral vectors (spv) selected from the MRSI matrix were unit length normalized (UL2) and used either as a training set (76 GBM spv, four mice; 70 ODG spv, two mice; 54 NT spv) or as an independent testing set (61 GBM spv, two mice; 31 ODG, one mouse; 23 NT spv). All Fisher's LDA classifiers obtained were evaluated as far as their descriptive performance - correctly classified cases of the training set (bootstrapping) - and predictive accuracy - balanced error rate of independent testing set classification. MRSI-based classifiers at 12Hyp were consistently more efficient in separating GBM, ODG, and NT regions, with overall accuracies always >80% and up to 95-96%; remaining classifiers were within the 48-85% range. This was also confirmed by user-independent selection of training and testing sets, using leave-one-out (LOO). This highlights the potential interest of perturbation-enhanced MRSI protocols for improving the non-invasive characterization of preclinical brain tumors.

Original languageEnglish (US)
Pages (from-to)183-191
Number of pages9
JournalIntegrative Biology
Issue number2
StatePublished - Feb 1 2012

ASJC Scopus subject areas

  • Biophysics
  • Biochemistry

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    Simões, R. V., Ortega-Martorell, S., Delgado-Goñi, T., Fur, Y. L., Pumarola, M., Candiota, A. P., Martín, J., Stoyanova, R., Cozzone, P. J., Julià-Sapé, M., & Arús, C. (2012). Improving the classification of brain tumors in mice with perturbation enhanced (PE)-MRSI. Integrative Biology, 4(2), 183-191.