Demographic and behavioural profiles of patients with common oral mucosal lesions by a homogeneity analysis.

OBJECTIVE: The aim of this study is to assess the main oral mucosal lesions (OMLs) within a hospital base and to provide an anamnestic, diagnostic model based on homogeneity analysis of some variables. METHODS: The demographic and behavioural data (i.e. gender, age, smoking status, alcohol consumption and therapeutic drug usage) of 1753 patients with at least one OML were considered. Multiple correspondence analysis (MCA) and multivariate tests of the simultaneous marginal homogeneity hypothesis (SMH) were used to analyse the evidence of any differences between the demographic and behavioural profiles relating to OMLs diagnoses. Statistical significance of P < 0.05 was chosen. RESULTS: With respect to the model used, patients affected by oral squamous cell carcinoma (n = 65; 3.5%) and oral leukoplakia (n = 73; 4.0%) differed significantly for demographic and behavioural characteristics analyzed, in particular with respect to gender (63.9%vs 50.1% males) and alcohol consumption (29.1%vs 12.1%). Patients affected by burning mouth syndrome (n = 134; 7.3%) and bisphosphonate-related osteonecrosis of the jaw (n = 40; 2.2%) differed significantly for chronic use of drugs (45.7%vs 71.6%). Finally, patients with halitosis (n = 60; 3.3%) and recurrent aphthous stomatitis (n = 103; 5.6%) showed similar profile, mainly in terms of men (47.6%), drinker (4.8%), drug user (34.9%), ≥60 years old (20.8%) and smoker (6.4%). CONCLUSION: Knowledge of some similarities in patients' profile could help in positing the likely presence of OML when making diagnosis process by either general physicians or dentists, especially those without extensive experience in the field of oral medicine.

ApoE-modified solid lipid nanoparticles: A feasible strategy to cross the blood-brain barrier.

Solid lipid nanoparticles (SLN) are colloidal drug delivery systems characterized by higher entrapment efficiency, good scalability of the preparation process and increased sustained prolonged release of the payload compared to other nanocarriers. The possibility to functionalize the surface of SLN with ligands to achieve a site specific targeting makes them attractive to overcome the limited blood-brain barrier (BBB) penetration of therapeutic compounds. SLN are prepared for brain targeting by exploiting the adaptability of warm microemulsion process for the covalent surface modification with an Apolipoprotein E-derived peptide (SLN-mApoE). Furthermore, the influence of the administration route on SLN-mApoE brain bioavailability is here evaluated. SLN-mApoE are able to cross intact a BBB in vitro model. The pulmonary administration of SLN-mApoE is related to a higher confinement in the brain of Balb/c mice compared to the intravenous and intraperitoneal administration routes, without inducing any acute inflammatory reaction in the lungs. These results promote the pulmonary administration of brain-targeted SLN as a feasible strategy for improving brain delivery of therapeutics.

Identification of two mutations in cis in the SCN1A gene in a family showing genetic epilepsy with febrile seizures plus (GEFS+) and idiopathic generalized epilepsy (IGE).

Mutations in the SCN1A gene causing either loss or gain of function have been frequently found in patients affected by genetic epilepsy with febrile seizures plus (GEFS+) or Dravet syndrome (also named severe myoclonic epilepsy in infancy SMEI). By mutation screening of the SCN1A gene, we identified for the first time a case of two missense mutations in cis (p.[Arg1525Gln;Thr297Ile]) in all affected individuals of an Italian family showing GEFS+ and idiopathic generalized epilepsy (IGE). The p.Arg1525Gln mutation was not previously reported yet and was predicted to be pathological by prediction tools, whereas the p.Thr297Ile was already identified in patients showing SMEI. Functional studies revealed that the Nav1.1 channels harboring both mutations were characterized by a significant shift in the activation curve towards more positive potentials. Our data demonstrate that the p.Arg1525Gln represents a novel mutation in the SCN1A gene altering the channel properties in the co-presence of the p.Thr297Ile.

A completely automated CAD system for mass detection in a large mammographic database.

Mass localization plays a crucial role in computer-aided detection (CAD) systems for the classification of suspicious regions in mammograms. In this article we present a completely automated classification system for the detection of masses in digitized mammographic images. The tool system we discuss consists in three processing levels: (a) Image segmentation for the localization of regions of interest (ROIs). This step relies on an iterative dynamical threshold algorithm able to select iso-intensity closed contours around gray level maxima of the mammogram. (b) ROI characterization by means of textural features computed from the gray tone spatial dependence matrix (GTSDM), containing second-order spatial statistics information on the pixel gray level intensity. As the images under study were recorded in different centers and with different machine settings, eight GTSDM features were selected so as to be invariant under monotonic transformation. In this way, the images do not need to be normalized, as the adopted features depend on the texture only, rather than on the gray tone levels, too. (c) ROI classification by means of a neural network, with supervision provided by the radiologist's diagnosis. The CAD system was evaluated on a large database of 3369 mammographic images [2307 negative, 1062 pathological (or positive), containing at least one confirmed mass, as diagnosed by an expert radiologist]. To assess the performance of the system, receiver operating characteristic (ROC) and free-response ROC analysis were employed. The area under the ROC curve was found to be Az = 0.783 +/- 0.008 for the ROI-based classification. When evaluating the accuracy of the CAD against the radiologist-drawn boundaries, 4.23 false positives per image are found at 80% of mass sensitivity.

GPCALMA: a Grid-based tool for mammographic screening.

OBJECTIVES: The next generation of high energy physics (HEP) experiments requires a GRID approach to a distributed computing system: the key concept is the Virtual ORGANISATION (VO), a group of distributed users with a common goal and the will to share their resources. METHODS: A similar approach, applied to a group of hospitals that joined the GPCALMA project (Grid Platform for Computer Assisted Library for MAmmography), will allow common screening programs for early diagnosis of breast and, in the future, lung cancer. The application code makes use of neural networks for the image analysis and is useful in improving the radiologists' diagnostic performance. GRID services allow remote image analysis and interactive online diagnosis, with a potential for a relevant reduction of the delays presently associated with screening programs. RESULTS AND CONCLUSIONS: A prototype of the system, based on AliEn GRID Services [1], is already available, with a central server running common services [2] and several clients connecting to it. Mammograms can be acquired in any location; the related information required to select and access them at any time is stored in a common service called Data Catalogue, which can be queried by any client. Thanks to the PROOF facility [3], the result of a query can be used as input for analysis algorithms, which are executed on the nodes where the input images are stored,. The selected approach avoids data transfers for all the images with a negative diagnosis and allows an almost real time diagnosis for the set of images with high cancer probability.

Automatic detection of lung nodules in CT datasets based on stable 3D mass-spring models.

We propose a computer-aided detection (CAD) system which can detect small-sized (from 3mm) pulmonary nodules in spiral CT scans. A pulmonary nodule is a small lesion in the lungs, round-shaped (parenchymal nodule) or worm-shaped (juxtapleural nodule). Both kinds of lesions have a radio-density greater than lung parenchyma, thus appearing white on the images. Lung nodules might indicate a lung cancer and their early stage detection arguably improves the patient survival rate. CT is considered to be the most accurate imaging modality for nodule detection. However, the large amount of data per examination makes the full analysis difficult, leading to omission of nodules by the radiologist. We developed an advanced computerized method for the automatic detection of internal and juxtapleural nodules on low-dose and thin-slice lung CT scan. This method consists of an initial selection of nodule candidates list, the segmentation of each candidate nodule and the classification of the features computed for each segmented nodule candidate.The presented CAD system is aimed to reduce the number of omissions and to decrease the radiologist scan examination time. Our system locates with the same scheme both internal and juxtapleural nodules. For a correct volume segmentation of the lung parenchyma, the system uses a Region Growing (RG) algorithm and an opening process for including the juxtapleural nodules. The segmentation and the extraction of the suspected nodular lesions from CT images by a lung CAD system constitutes a hard task. In order to solve this key problem, we use a new Stable 3D Mass-Spring Model (MSM) combined with a spline curves reconstruction process. Our model represents concurrently the characteristic gray value range, the directed contour information as well as shape knowledge, which leads to a much more robust and efficient segmentation process. For distinguishing the real nodules among nodule candidates, an additional classification step is applied; furthermore, a neural network is applied to reduce the false positives (FPs) after a double-threshold cut. The system performance was tested on a set of 84 scans made available by the Lung Image Database Consortium (LIDC) annotated by four expert radiologists. The detection rate of the system is 97% with 6.1 FPs/CT. A reduction to 2.5 FPs/CT is achieved at 88% sensitivity. We presented a new 3D segmentation technique for lung nodules in CT datasets, using deformable MSMs. The result is a efficient segmentation process able to converge, identifying the shape of the generic ROI, after a few iterations. Our suitable results show that the use of the 3D AC model and the feature analysis based FPs reduction process constitutes an accurate approach to the segmentation and the classification of lung nodules.

Computer-assisted diagnosis (CAD) in mammography: comparison of diagnostic accuracy of a new algorithm (Cyclopus, Medicad) with two commercial systems.

PURPOSE: The study compares the diagnostic accuracy (correct identification of cancer) of a new computer-assisted diagnosis (CAD) system (Cyclopus) with two other commercial systems (R2 and CADx). MATERIALS AND METHODS: Cyclopus was tested on a set of 120 mammograms on which the two compared commercial systems had been previously tested. The set consisted of mammograms reported as negative, preceding 31 interval cancers reviewed as screening error or minimal sign, and of 89 verified negative controls randomly selected from the same screening database. RESULTS: Cyclopus sensitivity was 74.1% (R2=54.8%; CADx=41.9%) and was higher for interval cancers reviewed as screening error (90.9%; R2=54.5%; CADx=81.8%) compared with those reviewed as minimal sign (65.0%; R2=55.0%; CADx=20.0%). Specificity was 15.7% (R2=29.2%; CADx=17.9%). Overall accuracy was 30.8% (R2=35.8%; CADx=24.1%). The positive predictive value of a case with CAD marks [regions of interest (ROI)] was 23.4% (23/98; R2=16.0%; CADx=15.1%). Average ROI number per view among negative controls was 1.13 (R2=0.93; CADx=0.99). Cyclopus was more sensitive for masses compared with isolated microcalcifications (208 vs 62 ROI; R2=90 vs 213; CADx=192 vs 130). CONCLUSIONS: Compared with two other commercial systems, Cyclopus was more sensitive (R2 p=0.14; CADx p=0.02) and less specific (R2 p=0.02; CADx p=0.64).

MAGIC-5: an Italian mammographic database of digitised images for research.

The implementation of a database of digitised mammograms is discussed. The digitised images were collected beginning in 1999 by a community of physicists in collaboration with radiologists in several Italian hospitals as a first step in developing and implementing a computer-aided detection (CAD) system. All 3,369 mammograms were collected from 967 patients and classified according to lesion type and morphology, breast tissue and pathology type. A dedicated graphical user interface was developed to visualise and process mammograms to support the medical diagnosis directly on a high-resolution screen. The database has been the starting point for developing other medical imaging applications, such as a breast CAD, currently being upgraded and optimised for use in a distributed environment with grid services, in the framework of the Instituto Nazionale di Fisicia Nucleare (INFN)-funded Medical Applications on a Grid Infrastructure Connection (MAGIC)-5 project.