Search

In this paper medical applications on a Grid infrastructure, the MAGIC-5 Project, are presented and discussed. MAGIC-5 aims at developing Computer Aided Detection (CADe) software for the analysis of medical images on distributed databases by means of GRID Services. The use of automated systems for analyzing medical images improves radiologists’ performance; in addition, it could be of paramount importance in screening programs, due to the huge amount of data to check and the cost of related manpower. The need for acquiring and analyzing data stored in different locations requires the use of Grid Services for the management of distributed computing resources and data. Grid technologies allow remote image analysis and interactive online diagnosis, with a relevant reduction of the delays presently associated with the diagnosis in the screening programs. The MAGIC-5 project develops algorithms for the analysis of mammographies for breast cancer detection, Computed- Tomography (CT) images for lung cancer detection and Positron Emission Tomography (PET) images for the early diagnosis of Alzheimer Disease (AD). A Virtual Organization (VO) has been deployed, so that authorized users can share data and resources and implement the following use cases: screening, tele-training and tele-diagnosis for mammograms and lung CT scans, statistical diagnosis by comparison of candidates to a distributed data-set of negative PET scans for the diagnosis of the AD. A small-scale prototype of the required Grid functionality was already implemented for the analysis of digitized mammograms.

Researches concerning the psychological and behavioral alterations following exposure to Intimate Partner Violence, associated with post-traumatic stress disorder (PTSD) as well, represent an interesting area of analyses. The aim of this work was to study the variations of event related brain potentials (ERPs), specifically N200 and P300, related to the establishment of visual emotional stimuli in women with Post-Traumatic Stress Disorder due to IPV. We administered the Beck Depression Inventory II (BDI II), Beck Anxiety Inventory (BAI) and a PTSD questionnaire to select a group of women recruited in a Center against Family Violence. Two categories of visual stimuli, emotional pictures with negative valence (EP) and positive and neutral pictures (NP), were presented during a decisional task, utilizing an oddball paradigm. The task was to press a button when the subject saw an emotional image. The IPV group was composed of 14 women (mean age: 39). The Control Group was composed of 14 women (mean age: 33) without PTDS or IPV nor depressive/anxiety symptomatology (measured with BDI II and BAI). Univariate ANOVAs were computed separately for each ERP component in amplitude and latency for electrode and for Lateralization. Both N200 and P300 had shown significant differences in amplitude (p

Neuroimaging and neuropsychological literature show functional dissociations in brain activity during processing of stimuli belonging to different semantic categories (e.g., animals, tools, faces, places). The aim of the study was to provide informations about the processing of stimuli from different phylogenetic domains, in order to observe the emergence of non-linguistic conceptual knowledge perceptualand categorizational pathway. Event related potentials (ERPs), N200 and P300, were recorded with an odd ball paradigm, in 24healthy right-handed individuals as they performed a perceptual recognition task on 56 of images of animals, man-made objects (i.e., artifacts) and background-color.The animals were grouped according to the phylogenetic scale in Fish (F), Reptiles (R) and Mammals (M). All stimuli were matched according to the familiarity of representation. The Task consisted in 4 sessions, one for every stimulus.The task was to press a button when the subject saw the stimulus between the background colors. Univariate ANOVAs were computed separately for each ERP component in amplitude (V) and latency (L) for Electrode and for Lateralization. Both N200 and P300 had shown significant differences in Frontal Channels (Fp2, F4, F8) V (p

Summary Background Joint replacement surgery has been on the increase in recent decades and prosthesis infection remains the most critical complication. Many aspects of the primary prevention and clinical management of such prosthesis infections still need to be clarified. Controversial issues The aim of this GISIG (Gruppo Italiano di Studio sulle Infezioni Gravi) working group – a panel of multidisciplinary experts – was to define recommendations for the following controversial issues: (1) Is a conservative surgical approach for the management of prosthetic joint infections effective? (2) Is the one-stage or the two-stage revision for the management of prosthetic joint infections more effective? (3) What is the most effective treatment for the management of prosthetic joint infections due to methicillin-resistant staphylococci? Results are presented and discussed in detail. Methods A systematic literature search using the MEDLINE database for the period 1988 to 2008 of randomized controlled trials and/or non-randomized studies was performed. A matrix was created to extract evidence from original studies using the CONSORT method to evaluate randomized clinical trials and the Newcastle–Ottawa Quality Assessment Scale for case–control studies, longitudinal cohorts, and retrospective studies. The GRADE method for grading quality of evidence and strength of recommendation was applied.

In this paper we investigate the feasibility of some typical techniques of pattern recognition for the classification of medical examples. The learning of the classifiers is not made in the traditional features space but it can be made by constructing decision rules on dissimilarity (distance) representations. In such a recognition process a new object is described by its distances to (a subset of) the training samples. Purpose of this work is the development of an automatic classification system which could be useful for radiologists in the investigation of breast cancer. The software has been designed in the framework of the MAGIC-5 collaboration. In the automatic classification system the suspicious regions with high probability to include a lesion are extracted from the image as regions of interest (ROIs). Each ROI is characterized by some features extracted from co-occurrence matrix containing spatial statistics information on ROI pixel gray tones. A dissimilarity representation of these features is made before the classification. A Feed-Forward Neural Network (FF-NN), a K-Nearest Neighbour (K-NN) and a Linear Discriminant Analysis (LDA) are employed to distinguish pathological records from not-pathological ones by the new features. The results obtained in terms of sensitivity (percentage of pathological ROIs correctly classified) and specificity (percentage of healthy ROIs correctly classified) will be comparatively presented. The K-NN classifier gives slightly better results than FF-NN and LDA accuracy (percentage of cases correctly classified) on two-classes problem (pathologic or healthy patients).

The mammography is the most effective procedure for an early diagnosis of the breast cancer. In this paper, an algorithm for detecting massive lesions in mammographic images will be presented. The database consists of 3762 digital images acquired in several hospitals belonging to the MAGIC-5 collaboration. A reduction of the surface under investigation is achieved, without loss of meaningful information, through segmentation of the whole image, by means of a ROI Hunter algorithm. In the following classification step, feature extraction plays a fundamental role: some features give geometrical information, other ones provide shape parameters. Once the features are computed for each ROI, they are used as inputs to a supervised neural network with momentum. The output neuron provides the probability that the ROI is pathological or not. Results are provided in terms of ROC and FROC curves; the area under the ROC curve was found to be A(Z) = (85.6 +/- 0.8) %. This software is included in the CAD station actually working in the hospitals belonging to the MAGIC-5 Collaboration.

Glucocorticoids are the only drugs available for the treatment of Duchenne muscular dystrophy (DMD), but it is unclear whether their efficacy is dependent on their anti-inflammatory activity.To address this issue, mdx mice were treated daily with methylprednisolone and non-steroidal anti-inflammatory drugs (NSAIDs: aspirin, ibuprofen, parecoxib).NSAID treatment was effective in ameliorating muscle morphology and reducing macrophage infiltration and necrosis. The percentage of regenerating myofibers was not modified by the treatments. The drugs were effective in reducing COX-2 expression and inflammatory cytokines, but they did not affect utrophin levels. The effects of the treatments on contractile performance were analyzed. Isometric tension did not differ in treated and untreated muscle, but the resistance to fatigue was decreased by treatment with methylprednisolone and aspirin.NSAIDs have a beneficial effect on mdx muscle morphology, pointing to a crucial role of inflammation in the progression of DMD.

A fully automated and three-dimensional (3D) segmentation method for the identification of the pulmonary parenchyma in thorax X-ray computed tomography (CT) datasets is proposed. It is meant to be used as preprocessing step in the computer-assisted detection (CAD) system for malignant lung nodule detection that is being developed by the Medical Applications in a Grid Infrastructure Connection (MAGIC-5) Project. In this new approach the segmentation of the external airways (trachea and bronchi), is obtained by 3D region growing with wavefront simulation and suitable stop conditions, thus allowing an accurate handling of the hilar region, notoriously difficult to be segmented. Particular attention was also devoted to checking and solving the problem of the apparent ‘fusion’ between the lungs, caused by partialvolume effects, while 3D morphology operations ensure the accurate inclusion of all the nodules (internal, pleural, and vascular) in the segmented volume. The newalgorithm was initially developed and tested on a dataset of 130 CT scans fromthe Italung-CT trial, andwas then applied to the ANODE09-competition images (55 scans) and to the LIDC database (84 scans), giving very satisfactory results. In particular, the lung contour was adequately located in 96% of the CT scans, with incorrect segmentation of the external airways in the remaining cases. Segmentation metrics were calculated that quantitatively express the consistency between automatic and manual segmentations: themean overlap degree of the segmentationmasks is 0.96±0.02, and the mean and the maximum distance between the mask borders (averaged on the whole dataset) are 0.74±0.05 and 4.5±1.5, respectively, which confirms that the automatic segmentations quite correctly reproduce the borders traced by the radiologist. Moreover, no tissue containing internal and pleural nodules was removed in the segmentation process, so that this method proved to be fit for the use in the framework of a CAD system. Finally, in the comparison with a two-dimensional segmentation procedure, inter-slice smoothness was calculated, showing that the masks created by the 3D algorithm are significantly smoother than those calculated by the 2D-only procedure.