Large-scale analysis of structural brain asymmetries during neurodevelopment: Associations with age and sex in 4265 children and adolescents.

Only a small number of studies have assessed structural differences between the two hemispheres during childhood and adolescence. However, the existing findings lack consistency or are restricted to a particular brain region, a specific brain feature, or a relatively narrow age range. Here, we investigated associations between brain asymmetry and age as well as sex in one of the largest pediatric samples to date (n = 4265), aged 1-18 years, scanned at 69 sites participating in the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) consortium. Our study revealed that significant brain asymmetries already exist in childhood, but their magnitude and direction depend on the brain region examined and the morphometric measurement used (cortical volume or thickness, regional surface area, or subcortical volume). With respect to effects of age, some asymmetries became weaker over time while others became stronger; sometimes they even reversed direction. With respect to sex differences, the total number of regions exhibiting significant asymmetries was larger in females than in males, while the total number of measurements indicating significant asymmetries was larger in males (as we obtained more than one measurement per cortical region). The magnitude of the significant asymmetries was also greater in males. However, effect sizes for both age effects and sex differences were small. Taken together, these findings suggest that cerebral asymmetries are an inherent organizational pattern of the brain that manifests early in life. Overall, brain asymmetry appears to be relatively stable throughout childhood and adolescence, with some differential effects in males and females.

Strategies to develop radiomics and machine learning models for lung cancer stage and histology prediction using small data samples.

Predictive models based on radiomics and machine-learning (ML) need large and annotated datasets for training, often difficult to collect. We designed an operative pipeline for model training to exploit data already available to the scientific community. The aim of this work was to explore the capability of radiomic features in predicting tumor histology and stage in patients with non-small cell lung cancer (NSCLC). We analyzed the radiotherapy planning thoracic CT scans of a proprietary sample of 47 subjects (L-RT) and integrated this dataset with a publicly available set of 130 patients from the MAASTRO NSCLC collection (Lung1). We implemented intra- and inter-sample cross-validation strategies (CV) for evaluating the ML predictive model performances with not so large datasets. We carried out two classification tasks: histology classification (3 classes) and overall stage classification (two classes: stage I and II). In the first task, the best performance was obtained by a Random Forest classifier, once the analysis has been restricted to stage I and II tumors of the Lung1 and L-RT merged dataset (AUC = 0.72 ± 0.11). For the overall stage classification, the best results were obtained when training on Lung1 and testing of L-RT dataset (AUC = 0.72 ± 0.04 for Random Forest and AUC = 0.84 ± 0.03 for linear-kernel Support Vector Machine). According to the classification task to be accomplished and to the heterogeneity of the available dataset(s), different CV strategies have to be explored and compared to make a robust assessment of the potential of a predictive model based on radiomics and ML.

Lower gray matter volumes of frontal lobes and insula in adolescents with anorexia nervosa restricting type: Findings from a Brain Morphometry Study.

BACKGROUND: Brain atrophy in anorexia nervosa (AN) is one of the most marked structural brain changes observed in mental disorders. In this study, we propose a whole brain analysis approach to characterize global and regional cerebral volumes in adolescents with restricting-type anorexia nervosa (AN-r). METHODS: A total of 48 adolescent females (age range 13-18 years) were enrolled in the study (24 right-handed AN-r in the early stages of the illness and treated in the same clinical setting and 24 age-matched healthy controls [HC]). High-resolution T1-weighted magnetic resonance images were acquired. Cerebral volumes, including the total amounts of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) were obtained with the Statistical Parametric Mapping software (SPM8); specific cortical regional volumes were computed by applying an atlas-based cortical parcellation to the SPM8 GM segments. Analysis of variance (ANOVA) was performed to identify any significant between-group differences in global and regional brain volumes. RESULTS: The analyses revealed reduced total GM volumes (p = 0.02) and increased CSF (p = 0.05) in AN-r, compared with HC. No significant between-group difference was found in WM volumes. At the regional level, significantly lower GM volumes in both frontal lobes (p = 0.006) and in the left insula (p = 0.016) were detected. No significant relationships were found between cerebral volumes and duration of illness, psychiatric comorbidities, psychopharmacological treatment, prepubertal phase, or presence of amenorrhea. CONCLUSIONS: The topographic distribution of GM reduction in a homogenous group of AN-r involves regions responsible for the emotional and cognitive deficits associated with the illness. These findings are discussed in relation to the roles of the insular cortex and the frontal lobes.

Evaluation of the intra- and inter-method agreement of brain MRI segmentation software packages: A comparison between SPM12 and FreeSurfer v6.0.

PURPOSE: The lack of inter-method agreement can produce inconsistent results in neuroimaging studies. We evaluated the intra-method repeatability and the inter-method reproducibility of two widely-used automatic segmentation methods for brain MRI: the FreeSurfer (FS) and the Statistical Parametric Mapping (SPM) software packages. METHODS: We segmented the gray matter (GM), the white matter (WM) and subcortical structures in test-retest MRI data of healthy volunteers from Kirby-21 and OASIS datasets. We used Pearson's correlation (r), Bland-Altman plot and Dice index to study intra-method repeatability and inter-method reproducibility. In order to test whether different processing methods affect the results of a neuroimaging-based group study, we carried out a statistical comparison between male and female volume measures. RESULTS: A high correlation was found between test-retest volume measures for both SPM (r in the 0.98-0.99 range) and FS (r in the 0.95-0.99 range). A non-null bias between test-retest FS volumes was detected for GM and WM in the OASIS dataset. The inter-method reproducibility analysis measured volume correlation values in the 0.72-0.98 range and the overlap between the segmented structures assessed by the Dice index was in the 0.76-0.83 range. SPM systematically provided significantly greater GM volumes and lower WM and subcortical volumes with respect to FS. In the male vs. female brain volume comparisons, inconsistencies arose for the OASIS dataset, where the gender-related differences appear subtler with respect to the Kirby dataset. CONCLUSIONS: The inter-method reproducibility should be evaluated before interpreting the results of neuroimaging studies.

Semiautomated Evaluation of the Primary Motor Cortex in Patients with Amyotrophic Lateral Sclerosis at 3T.

BACKGROUND AND PURPOSE: Amyotrophic lateral sclerosis is a neurodegenerative disease involving the upper and lower motor neurons. In amyotrophic lateral sclerosis, pathologic changes in the primary motor cortex include Betz cell depletion and the presence of reactive iron-loaded microglia, detectable on 7T MR images as atrophy and T2*-hypointensity. Our purposes were the following: 1) to investigate the signal hypointensity-to-thickness ratio of the primary motor cortex as a radiologic marker of upper motor neuron involvement in amyotrophic lateral sclerosis with a semiautomated method at 3T, 2) to compare 3T and 7T results, and 3) to evaluate whether semiautomated measurement outperforms visual image assessment. MATERIALS AND METHODS: We investigated 27 patients and 13 healthy subjects at 3T, and 19 patients and 18 healthy subjects at 7T, performing a high-resolution 3D multiecho T2*-weighted sequence targeting the primary motor cortex. The signal hypointensity-to-thickness ratio of the primary motor cortex was calculated with a semiautomated method depicting signal intensity profiles of the cortex. Images were also visually classified as "pathologic" or "nonpathologic" based on the primary motor cortex signal intensity and thickness. RESULTS: The signal hypointensity-to-thickness ratio of the primary motor cortex was greater in patients than in controls (P < .001), and it correlated with upper motor neuron impairment in patients (ρ = 0.57, P < .001). The diagnostic accuracy of the signal hypointensity-to-thickness ratio was high at 3T (area under the curve = 0.89) and even higher at 7T (area under the curve = 0.94). The sensitivity of the semiautomated method (0.81) outperformed the sensitivity of the visual assessment (0.56-0.63) at 3T. CONCLUSIONS: The signal hypointensity-to-thickness ratio of the primary motor cortex calculated with a semiautomated method is suggested as a radiologic marker of upper motor neuron burden in patients with amyotrophic lateral sclerosis. This semiautomated method may be useful for improving the subjective radiologic evaluation of upper motor neuron pathology in patients suspected of having amyotrophic lateral sclerosis.

Pleural nodule identification in low-dose and thin-slice lung computed tomography.

A completely automated system for the identification of pleural nodules in low-dose and thin-slice computed tomography (CT) of the lung has been developed. The directional-gradient concentration method has been applied to the pleura surface and combined with a morphological opening-based procedure to generate a list of nodule candidates. Each nodule candidate is characterized by 12 morphological and textural features, which are analyzed by a rule-based filter and a neural classifier. This detection system has been developed and validated on a dataset of 42 annotated CT scans. The k-fold cross validation has been used to evaluate the neural classifier performance. The system performance variability due to different ground truth agreement levels is discussed. In particular, the poor 44% sensitivity obtained on the ground truth with agreement level 1 (nodules annotated by only one radiologist) with six FP per scan grows up to the 72% if the underlying ground truth is changed to the agreement level 2 (nodules annotated by two radiologists).

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.

Lung nodule detection in low-dose and thin-slice computed tomography.

A computer-aided detection (CAD) system for the identification of small pulmonary nodules in low-dose and thin-slice CT scans has been developed. The automated procedure for selecting the nodule candidates is mainly based on a filter enhancing spherical-shaped objects. A neural approach based on the classification of each single voxel of a nodule candidate has been purposely developed and implemented to reduce the amount of false-positive findings per scan. The CAD system has been trained to be sensitive to small internal and sub-pleural pulmonary nodules collected in a database of low-dose and thin-slice CT scans. The system performance has been evaluated on a data set of 39 CT containing 75 internal and 27 sub-pleural nodules. The FROC curve obtained on this data set shows high values of sensitivity to lung nodules (80-85% range) at an acceptable level of false positive findings per patient (10-13 FP/scan).

A CAD system for nodule detection in low-dose lung CTs based on region growing and a new active contour model.

A computer-aided detection (CAD) system for the selection of lung nodules in computer tomography (CT) images is presented. The system is based on region growing (RG) algorithms and a new active contour model (ACM), implementing a local convex hull, able to draw the correct contour of the lung parenchyma and to include the pleural nodules. The CAD consists of three steps: (1) the lung parenchymal volume is segmented by means of a RG algorithm; the pleural nodules are included through the new ACM technique; (2) a RG algorithm is iteratively applied to the previously segmented volume in order to detect the candidate nodules; (3) a double-threshold cut and a neural network are applied to reduce the false positives (FPs). After having set the parameters on a clinical CT, the system works on whole scans, without the need for any manual selection. The CT database was recorded at the Pisa center of the ITALUNG-CT trial, the first Italian randomized controlled trial for the screening of the lung cancer. The detection rate of the system is 88.5% with 6.6 FPs/CT on 15 CT scans (about 4700 sectional images) with 26 nodules: 15 internal and 11 pleural. A reduction to 2.47 FPs/CT is achieved at 80% efficiency.

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.

Antifungal agents. 9. 3-Aryl-4-[alpha-(1H-imidazol-1-yl)arylmethyl]pyrroles: a new class of potent anti-Candida agents.

A new class of potent antifungal agents, namely, 3-aryl-4-[alpha-(1H-imidazol-1-yl)arylmethyl]-pyrroles, is described. These compounds are related to bifonazole and pyrrolnitrin, two compounds belonging to the class of antimycotic drugs. The synthesis of the title pyrroles has been performed starting from 1,3-diaryl-2-propen-1-ones, which were reacted with tosylmethyl isocyanide to give 3-aroyl-4-arylpyrroles. Reduction of the resulting compounds by lithium aluminum hydride furnished the related alcohols, which were treated with 1,1'-carbonyldimidazole to afford the required imidazole derivatives. Forty-four new pyrroles which incorporate an (arylmethyl)imidazole moiety in the 3-arylpyrrole structure were prepared by the above procedure and tested in vitro against Candida albicans and Candida spp. Among test compounds, 10 were found to be highly active against C. albicans. The most active derivative (27) was twice as potent (MIC90) as bifonazole, and its activity was 4 times greater than those of miconazole and ketoconazole. The other nine compounds showed antifungal activity of the same order of that of bifonazole and were ca. 2 times as active as miconazole and ketoconazole. Derivatives 21 and 27 tested in vivo against C. albicans A170 were shown to be highly effective in rabbit skin candidosis. Pharmacological studies on compounds 27 and other related pyrroles (19, 35, 36, 38, 39, and 49) are in progress to select one of them as a potential candidate for clinical experiments.

Antifungal agents. VIII. Synthesis and antifungal activities of bipyrryl analogues of bifonazole.

Various bipyrryl analogues of bifonazole were synthesized starting from aryl-3-pyrryl-1-imidazolylmethanes. The introduction of a second pyrryl portion was performed by linking an acrylate moiety at 1-position of the pyrrole ring and then by treatment with TosMIC. The bipyrryl esters were hydrolyzed and decarboxylated to afford the required imidazoles. All new imidazole derivatives were tested against Candida albicans and Candida spp using as standard controls miconazole, bifonazole and ketoconazole.

Antifungal agents. 7. Dichlorophenylpyrrylimidazolylmethane derivatives: synthesis and antifungal activities.

The synthesis and antifungal activities of some dichlorophenyl-1H-pyrrol-2-yl-1H-imidazol-1-ylmethane derivatives substituted at pyrrole nitrogen are reported. When tested against Candida albicans and Candida spp., some derivatives were found to be from two to four times less active than miconazole, bifonazole and ketoconazole, used as standard controls.

Antifungal agents, II: Synthesis and antifungal activities of aryl-1H-pyrrol-2-yl-1H-imidazol-1-yl-methane derivatives with unsaturated chains.

The synthesis and antifungal activities of aryl-1H-pyrrol-2-yl-1H-imidazol-1-yl-methanes having allyl, crotyl, and acrylate chains linked to the N-pyrrole atom and substituted at phenyl ring by Cl, F, CH3, and NO2 groups are reported. In vitro tests against Candida albicans and Candida spp. showed 2,4-dichlorophenyl-1-allyl-1H-pyrrol-2-yl-1H-imidazol-1-yl-methane to be the most potent derivative with activities comparable to those of ketoconazole and slightly inferior to those of bifonazole and miconazole. Some structure-activity relationships are discussed.

Antifungal agents. VI. In vitro antifungal activities of halobenzoyl esters of cis- and trans-[2-(1,1'-biphenyl-4-yl)-2-(1H-imidazol-1-ylmethyl)-1,3- dioxolan-4-yl]carbinols.

The synthesis and the in vitro antifungal activities against Candida albicans and Candida spp of a number of halobenzoyl esters of cis- and trans- [2-(1,1'-biphenyl-4-yl)-2-(1H-imidazol-1-ylmethyl)-1,3-dioxolan -4- yl]carbinols is reported. Some new imidazoles were found more active than ketoconazole and sometimes as potent as bifonazole against Candida albicans. All derivatives were found scarcely active against Candida spp.

Antifungal agents. III. Naphthyl and thienyl derivatives of 1H-imidazol-1-yl-4-phenyl-1H-pyrrol-3-ylmethane.

The in vitro antifungal activities of some naphthyl and thienyl derivatives of 1H-imidazol-1-yl-4-phenyl-1H-pyrrol-3-ylmethane against Candida albicans and Candida spp are reported. The title derivatives were prepared starting from proper arylstirylketones, which were reacted with tosylmethylisocyanide (Tos-MIC) to afford the related 4-phenyl-1H-pyrrol-3-yl aryl ketones. Reduction of ketones to the corresponding carbinols followed by treatment of the last compounds with 1,1'-carbonyldiimidazole (CDI) gave the title imidazoles. The related N-methylpyrrole derivatives are also described.

Researches on antibacterial and antifungal agents, XIV: Thiophene analogues of bifonazole.

Some thiophene analogues of bifonazole have been synthesized by standard procedures and their antifungal activity has been tested against Candida albicans. Among test derivatives biphenyl-4-yl-5-chloro-thien-2-ylimidazol-1-ylmethane and its 5-deschlorothien-2-yl analogue resulted to be the most active. Their antifungal potency was almost comparable to that of control substances, such as miconazole, ketoconazole, and bifonazole. Replacement of benzene by the pyrrole ring in the biphenyl portion retained almost quantitatively the antifungal activity, whereas substitution with other azoles and with nitrogen alicyclic rings led always to less potent derivatives.

Macrophage lysozyme stimulation by fructose-1-6-diphosphate.

FDP produces an increase of serum lysozyme concentration which may be related to stimulation of the phagocytic activity. Mice macrophages in vitro produce extracellular and intracellular LSZ (lysozyme) and FDP (fructose-1-6-diphosphate) increases this production. Also in vivo FDP stimulates the macrophages intracellular lysozyme production. The toxic activity in vitro and the protection in vivo against Staphylococcus pyogenes after FDP administration can also be related to macrophage stimulation.

Action of the ketoconazole-alkylaminoethylglycine combination on Candida albicans.

High concentrations of ketoconazole (Ktc) exhibit poor antimicrobial activity in microbicidal test. Alkylaminoethylglycine (AAEG), an amphoteric surface-active agent, employed in subinhibitory concentrations, can enhance the action of ketoconazole. The Ktc-AAEG combination causes a higher rate of potassium ion release compared to both drugs separately. The increased ketoconazole activity may be ascribed to the membrane damage indicated by increased permeability to potassium ions.