The effect of combining two echo times in automatic brain tumor classification by MRS

Juan M. García-Gómez, Salvador Tortajada, César Vidal, Margarida Julià-Sape, Jan Luts, Angel Moreno-Torres, Sabine Van Huffel, Carles Arús, Montserrat Robles

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36 Citations (Scopus)

Abstract

1H MRS is becoming an accurate, non-invasive technique for initial examination of brain masses. We investigated if the combination of sing le-voxel 1H MRS at 1.5 T at two different (TEs), short TE (PRESS or STEAM, 20¬32 ms) and long TE (PRESS, 135-136 ms), improves the classification of brain tumors over using only one echo TE.A clinically validated dataset of 50 low-grade meningiomas, 105 aggressive tumors (glioblastoma and metastasis), and 30 low-grade glial tumors (astrocytomas grade II, oligodendrogliomas and oligoastrocytomas) was used to fit predictive models based on the combination of features from short-TEs and long-TE spectra. A new approach that combines the two consecutively was used to produce a single data vector from which relevant features of the two TE spectra could be extracted by means of three algorithms: stepwise, reliefF, and principal components analysis. Least squares support vector machines and linear discriminant analysis were applied to fit the pairwise and multiclass classifiers, respectively. Significant differences in performance were found when short-TE, long-TE or both spectra combined were used as input. In our dataset, to discriminate meningiomas, the combination of the two TE acquisitions produced optimal performance. To discriminate aggressive tumors from low-grade glial tumours, the use of short-TE acquisition alone was preferable. The classifier development strategy used here lends itself to automated learning and test performance processes, which may be of use for future web-based multicentric classifier development studies. © 2008 John Wiley & Sons, Ltd.
Original languageEnglish
Pages (from-to)1112-1125
JournalNMR in Biomedicine
Volume21
DOIs
Publication statusPublished - 1 Dec 2008

Keywords

  • 1 H MRS
  • Brain
  • Cancer
  • Decision support systems
  • Long TE
  • Pattern recognition
  • Short TE

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