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Esplugues de Llobregat, Spain

Postma G.J.,Radboud University Nijmegen | Luts J.,Catholic University of Leuven | Idema A.J.,Radboud University Nijmegen | Julia-Sape M.,Autonomous University of Barcelona | And 7 more authors.
Computers in Biology and Medicine | Year: 2011

In order to evaluate the relevance of magnetic resonance (MR) features selected by automatic feature selection techniques to build classifiers for differential diagnosis and tissue segmentation two data sets containing MR spectroscopy data from patients with brain tumours were investigated. The automatically selected features were evaluated using literature and clinical experience. It was observed that a significant part of the automatically selected features correspond to what is known from the literature and clinical experience. We conclude that automatic feature selection is a useful tool to obtain relevant and possibly interesting features, but evaluation of the obtained features remains necessary. © 2010 Elsevier Ltd.

Fuster-Garcia E.,University of Valencia | Fuster-Garcia E.,Polytechnic University of Valencia | Navarro C.,Quiron Valencia Hospital | Vicente J.,Polytechnic University of Valencia | And 30 more authors.
Magnetic Resonance Materials in Physics, Biology and Medicine | Year: 2011

Object: This study demonstrates that 3T SV-MRS data can be used with the currently available automatic brain tumour diagnostic classifiers which were trained on databases of 1.5T spectra. This will allow the existing large databases of 1.5T MRS data to be used for diagnostic classification of 3T spectra, and perhaps also the combination of 1.5T and 3T databases. Materials and methods: Brain tumour classifiers trained with 154 1.5T spectra to discriminate among high grade malignant tumours and common grade II glial tumours were evaluated with a subsequently-acquired set of 155 1.5T and 37 3T spectra. A similarity study between spectra and main brain tumour metabolite ratios for both field strengths (1.5T and 3T) was also performed. Results: Our results showed that classifiers trained with 1.5T samples had similar accuracy for both test datasets (0.87 ± 0.03 for 1.5T and 0.88 ± 0.03 for 3.0T). Moreover, non-significant differences were observed with most metabolite ratios and spectral patterns. Conclusion: These results encourage the use of existing classifiers based on 1.5T datasets for diagnosis with 3T 1H SV-MRS. The large 1.5T databases compiled throughout many years and the prediction models based on 1.5T acquisitions can therefore continue to be used with data from the new 3T instruments. © 2011 ESMRMB.

Tortajada S.,Polytechnic University of Valencia | Fuster-Garcia E.,Polytechnic University of Valencia | Vicente J.,Polytechnic University of Valencia | Wesseling P.,Radboud University Nijmegen | And 21 more authors.
Journal of Biomedical Informatics | Year: 2011

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, and validation of samples is expensive and time-consuming. Therefore, for a classical, non-incremental approach to ML, it is necessary to wait long enough to collect all the required data. In contrast, an incremental learning approach may allow us to build an initial classifier with a smaller number of samples and update it incrementally when new data are collected. In this study, an incremental learning algorithm for Gaussian Discriminant Analysis (iGDA) based on the Graybill and Deal weighted combination of estimators is introduced. Each time a new set of data becomes available, a new estimation is carried out and a combination with a previous estimation is performed. iGDA does not require access to the previously used data and is able to include new classes that were not in the original analysis, thus allowing the customization of the models to the distribution of data at a particular clinical center. An evaluation using five benchmark databases has been used to evaluate the behaviour of the iGDA algorithm in terms of stability-plasticity, class inclusion and order effect. Finally, the iGDA algorithm has been applied to automatic brain tumour classification with magnetic resonance spectroscopy, and compared with two state-of-the-art incremental algorithms. The empirical results obtained show the ability of the algorithm to learn in an incremental fashion, improving the performance of the models when new information is available, and converging in the course of time. Furthermore, the algorithm shows a negligible instance and concept order effect, avoiding the bias that such effects could introduce. © 2011 Elsevier Inc.

Moreno-Torres A.,Center Diagnostic Pedralbes | Moreno-Torres A.,CIBER ISCIII | Rosset-Llobet J.,Institute Of Fisiologia I Medicina Of Lart | Pujol J.,Institute DAlta Tecnologia | And 3 more authors.
PLoS ONE | Year: 2010

Background: Although non-specific pain in the upper limb muscles of workers engaged in mild repetitive tasks is a common occupational health problem, much is unknown about the associated structural and biochemical changes. In this study, we compared the muscle energy metabolism of the extrinsic finger extensor musculature in instrumentalists suffering from work-related pain with that of healthy control instrumentalists using non-invasive phosphorus magnetic resonance spectroscopy (31P-MRS). We hypothesize that the affected muscles will show alterations related with an impaired energy metabolism. Methodology/Principal Findings:We studied 19 volunteer instrumentalists (11 subjects with work-related pain affecting the extrinsic finger extensor musculature and 8 healthy controls). We used 31P-MRS to find deviations from the expected metabolic response to exercise in phosphocreatine (PCr), inorganic phosphate (Pi), Pi/PCr ratio and intracellular pH kinetics. We observed a reduced finger extensor exercise tolerance in instrumentalists with myalgia, an intracellular pH compartmentation in the form of neutral and acid compartments, as detected by Pi peak splitting in 31P-MRS spectra, predominantly in myalgic muscles, and a strong association of this pattern with the condition. Conclusions/Significance: Work-related pain in the finger extrinsic extensor muscles is associated with intracellular pH compartmentation during exercise, non-invasively detectable by 31P-MRS and consistent with the simultaneous energy production by oxidative metabolism and glycolysis. We speculate that a deficit in energy production by oxidative pathways may exist in the affected muscles. Two possible explanations for this would be the partial and/or local reduction of blood supply and the reduction of the muscle oxidative capacity itself. © 2010 Moreno-Torres et al.

Castells X.,Autonomous University of Barcelona | Castells X.,Research Center Biomedica en Red en Bioingenieria | Acebes J.J.,Idibell Hospital Universitari Of Bellvitge | Acebes J.J.,Research Center Biomedica en Red en Bioingenieria | And 11 more authors.
OMICS A Journal of Integrative Biology | Year: 2010

Development of molecular diagnostics that can reliably differentiate amongst different subtypes of brain tumors is an important unmet clinical need in postgenomics medicine and clinical oncology. A simple linear formula derived from gene expression values of four genes (GFAP, PTPRZ1, GPM6B, and PRELP) measured from cDNA microarrays (n=35) have distinguished glioblastoma and meningioma cases in a previous study. We herein extend this work further and report that the above predictor formula showed its robustness when applied to Affymetrix microarray data acquired prospectively in our laboratory (n=80) as well as publicly available data (n=98). Importantly, GFAP and GPM6B were both retained as being significant in the predictive model upon using the Affymetrix data obtained in our laboratory, whereas the other two predictor genes were SFRP2 and SLC6A2. These results collectively indicate the importance of the expression values of GFAP and GPM6B genes sampled from the two types of microarray technologies tested. The high prediction accuracy obtained in these instances demonstrates the robustness of the predictors across microarray platforms used. This result would require further validation with a larger population of meningioma and glioblastoma cases. At any rate, this study paves the way for further application of gene signatures to more stringent biopsy discrimination challenges. © 2010, Mary Ann Liebert, Inc.

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