M. Carmen Martínez-Bisbal

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Lower levels of N-acetylaspartate (NAA), a marker of axonal damage, have been found in the normal-appearing white matter (NAWM) of relapsing-remitting multiple sclerosis (RRMS) patients with low physical disability. However, its relation to the clinical status of these patients remains unclear. We explored the association between NAA levels [normalized to(More)
JUSTIFICATION Automatic brain tumor classification by MRS has been under development for more than a decade. Nonetheless, to our knowledge, there are no published evaluations of predictive models with unseen cases that are subsequently acquired in different centers. The multicenter eTUMOUR project (2004-2009), which builds upon previous expertise from the(More)
The eTUMOUR (eT) multi-centre project gathered in vivo and ex vivo magnetic resonance (MR) data, as well as transcriptomic and clinical information from brain tumour patients, with the purpose of improving the diagnostic and prognostic evaluation of future patients. In order to carry this out, among other work, a database--the eTDB--was developed. In(More)
HRMAS NMR is considered a valuable technique to obtain detailed metabolic profile of unprocessed tissues. To properly interpret the HRMAS metabolomic results, detailed information of the actual state of the sample inside the rotor is needed. MRM (Magnetic Resonance Microscopy) was applied for obtaining structural and spatially localized metabolic(More)
HR-MAS (High-Resolution Magic Angle Spinning) is considered a powerful technique for metabolomic studies of biological samples that provides " intact " tissue spectra (Cheng et al. The performance of HR-MAS, followed by quantitative histopathology has demonstrated that, despite some changes, HR-MAS can preserve approximately the tissue histopathologic(More)
In the last decade, machine learning (ML) techniques have been used for developing classifiers for automatic brain tumour diagnosis. However, the development of these ML models rely on a unique training set and learning stops once this set has been processed. Training these classifiers requires a representative amount of data, but the gathering, preprocess,(More)
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