Characterization of the canine rostral ventricular-subventricular zone: Morphological, immunohistochemical, ultrastructural, and neurosphere assay studies.
The mammalian ventricular-subventricular zone (V-SVZ) presents the highest neurogenic potential in the brain of the adult individual. In rodents, it is mainly composed of chains of neuroblasts. In humans, it is organized in layers where neuroblasts do not form chains. The aim of this study is to describe the cytoarchitecture of canine V-SVZ (cV-SVZ), to assess its neurogenic potential, and to compare our results with those previously described in other species. We have studied by histology, immunohistochemistry (IHC), electron microscopy and neurosphere assay the morphology, cytoarchitecture and neurogenic potential of cV-SVZ. Age groups of animals were performed. Histological and ultrastru…
On the Implementation of HealthAgents: Agent-Based Brain Tumour Diagnosis
This paper introduces HealthAgents, an EC-funded research project to improve the classification of brain tumours through multi-agent decision support over a secure and distributed network of local databases or Data Marts. HealthAgents will not only develop new pattern recognition methods for distributed classification and analysis of in vivo MRS and ex vivo/in vitro HRMAS and DNA data, but also define a method to assess the quality and usability of a new candidate local database containing a set of new cases, based on a compatibility score. Using its Multi-Agent architecture, HealthAgents intends to apply cutting-edge agent technology to the Biomedical field and develop the HealthAgents net…
In vivo and ex vivo magnetic resonance spectroscopy of the infarct and the subventricular zone in experimental stroke
Ex vivo high-resolution magic-angle spinning (HRMAS) provides metabolic information with higher sensitivity and spectral resolution than in vivo magnetic resonance spectroscopy (MRS). Therefore, we used both techniques to better characterize the metabolic pattern of the infarct and the neural progenitor cells (NPCs) in the ipsilateral subventricular zone (SVZi). Ischemic stroke rats were divided into three groups: G0 (non-stroke controls, n = 6), G1 (day 1 after stroke, n = 6), and G7 (days 6 to 8 after stroke, n =12). All the rats underwent MRS. Three rats per group were analyzed by HRMAS. The remaining rats were used for immunohistochemical studies. In the infarct, both techniques detect…
Incremental Gaussian Discriminant Analysis based on Graybill and Deal weighted combination of estimators for brain tumour diagnosis
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…
Strategies for annotation and curation of translational databases: the eTUMOUR project
Altres ajuts: LSHC/CT2004-503094 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 addition to complex permission rules and software and management quality control (QC), it was necessary to develop anonymization, processing and data visualization tools for the data uploaded. It was also necessary to develop sophisticated curation strategies that involved on one hand, dedicated fields for QC-gene…
Multiproject–multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy
[EN] 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 INTERPRET project (2000-2002) has allowed such an evaluation to take place. A total of 253 pairwise classifiers for glioblastoma, meningioma, metastasis, and low-grade glial diagnosis were inferred based on 211 SV short TE INTERPRET MR spectra obtained at 1.5 T (PRESS or STEAM, 20-32 ms) and automatically pre-processed. Afterwards, the classifiers …
Quantification and classification of high-resolution magic angle spinning data for brain tumor diagnosis.
The goal of this work is to propose a complete protocol (preprocessing, processing and classification) for classifying brain tumors with proton high-resolution magic-angle spinning ((1)H HR-MAS) data. The different steps of the procedure are detailed and discussed. Feature extraction techniques such as peak integration, including also the automated quantitation method AQSES, were combined with linear (LDA) and non-linear (least-squares support vector machine or LS-SVM) classifiers. Classification accuracy was assessed using a stratified random sampling scheme. The results suggest that LS-SVM performs better than LDA while AQSES performs better than the standard peak integration feature extr…
Automated quality control protocol for MR spectra of brain tumors.
Item does not contain fulltext eTUMOUR (http://www.etumour.net/) is acquiring a large database of brain tumor (1)H MR spectra to develop automated pattern recognition methods and decision support system (DSS) for tumor diagnosis. Development of accurate pattern-recognition algorithms requires spectra undistorted by artifacts, low signal-to-noise, or broad lines. eTUMOUR currently uses panels of expert spectroscopists to subjectively grade spectra as being acceptable or unacceptable. Automated quality control (QC) would be more satisfactory for several reasons: 1) to provide a reproducible objective classification of spectrum quality; 2) for use within the future DSS to prevent misdiagnosis …