Radiomic and Artificial Intelligence Analysis with Textural Metrics, Morphological and Dynamic Perfusion Features Extracted by Dynamic Contrast-Enhanced Magnetic Resonance Imaging in the Classification of Breast Lesions

The aim of the study was to estimate the diagnostic accuracy of textural, morpho- logical and dynamic features, extracted by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images, by carrying out univariate and multivariate statistical analyses including artificial intelligence approaches. Methods: In total, 85 patients with known breast lesion were enrolled in this retrospective study according to regulations issued by the local Institutional Review Board. All patients underwent DCE-MRI examination. The reference standard was pathology from a surgical specimen for malignant lesions and pathology from a surgical specimen or fine needle aspiration cytology, core or Tru-Cut needle biopsy for benign lesions. In total, 91 samples of 85 patients were ana- lyzed. Furthermore, 48 textural metrics, 15 morphological and 81 dynamic parameters were extracted by manually segmenting regions of interest. Statistical analyses including univariate and multivari- ate approaches were performed: non-parametric Wilcoxon–Mann–Whitney test; receiver operating characteristic (ROC), linear classifier (LDA), decision tree (DT), k-nearest neighbors (KNN), and support vector machine (SVM) were utilized. A balancing approach and feature selection methods were used. Results: The univariate analysis showed low accuracy and area under the curve (AUC) for all considered features. Instead, in the multivariate textural analysis, the best performance (accuracy (ACC) = 0.78; AUC = 0.78) was reached with all 48 metrics and an LDA trained with balanced data. The best performance (ACC = 0.75; AUC = 0.80) using morphological features was reached with an SVM trained with 10-fold cross-variation (CV) and balanced data (with adaptive synthetic (ADASYN) function) and a subset of five robust morphological features (circularity, rectangularity, sphericity, gleaning and surface). The best performance (ACC = 0.82; AUC = 0.83) using dynamic features was reached with a trained SVM and balanced data (with ADASYN function). Conclusion: Multivariate analyses using pattern recognition approaches, including all morphological, textural and dynamic features, optimized by adaptive synthetic sampling and feature selection operations obtained the best results and showed the best performance in the discrimination of benign and malignant lesions

Digital breast tomosynthesis and contrast-enhanced dual-energy digital mammography alone and in combination compared to 2D digital synthetized mammography and MR imaging in breast cancer detection and classification.

To compare diagnostic performance of contrast-enhanced dual-energy digital mammography (CEDM) and digital breast tomosynthesis (DBT) alone and in combination compared to 2D digital mammography (MX) and dynamic contrast-enhanced MRI (DCE-MRI) in women with breast lesions. We enrolled 100 consecutive patients with breast lesions (BIRADS 3-5 at imaging or clinically suspicious). CEDM, DBT, and DCE-MRI 2D were acquired. Synthetized MX was obtained by DBT. A total of 134 lesions were investigated on 111 breasts of 100 enrolled patients: 53 were histopathologically proven as benign and 81 as malignant. Nonparametric statistics and receiver operating characteristic (ROC) curve were performed. Two-dimensional synthetized MX showed an area under ROC curve (AUC) of 0.764 (sensitivity 65%, specificity 80%), while AUC was of 0.845 (sensitivity 80%, specificity 82%) for DBT, of 0.879 (sensitivity 82%, specificity 80%) for CEDM, and of 0.892 (sensitivity 91%, specificity 84%) for CE-MRI. DCE-MRI determined an AUC of 0.934 (sensitivity 96%, specificity 88%). Combined CEDM with DBT findings, we obtained an AUC of 0.890 (sensitivity 89%, specificity 74%). A difference statistically significant was observed only between DCE-MRI and CEDM (P = .03). DBT, CEDM, CEDM combined to tomosynthesis, and DCE-MRI had a high ability to identify multifocal and bilateral lesions with a detection rate of 77%, 85%, 91%, and 95% respectively, while 2D synthetized MX had a detection rate for multifocal lesions of 56%. DBT and CEDM have superior diagnostic accuracy of 2D synthetized MX to identify and classify breast lesions, and CEDM combined with DBT has better diagnostic performance compared with DBT alone. The best results in terms of diagnostic performance were obtained by DCE-MRI. Dynamic information obtained by time-intensity curve including entire phase of contrast agent uptake allows a better detection and classification of breast lesions

Middle ear microbiome differences in indigenous Filipinos with chronic otitis media due to a duplication in the A2ML1 gene

Middle ear microbial profiles of indigenous Filipinos with chronic otitis media. All panels compare carriers with non-carriers of the A2ML1 duplication variant. Panel description: (A) ι-diversity by observed OTUs; (B) ι-diversity by the Shannon diversity index; (C) β-diversity from unweighted UniFrac principal coordinate analysis; (D) β-diversity from weighted UniFrac principal coordinate analysis. (PDF 1019 kb

Cannabidiol Reduces Intestinal Inflammation through the Control of Neuroimmune Axis

Enteric glial cells (EGC) actively mediate acute and chronic inflammation in the gut; EGC proliferate and release neurotrophins, growth factors, and pro-inflammatory cytokines which, in turn, may amplify the immune response, representing a very important link between the nervous and immune systems in the intestine. Cannabidiol (CBD) is an interesting compound because of its ability to control reactive gliosis in the CNS, without any unwanted psychotropic effects. Therefore the rationale of our study was to investigate the effect of CBD on intestinal biopsies from patients with ulcerative colitis (UC) and from intestinal segments of mice with LPS-induced intestinal inflammation. CBD markedly counteracted reactive enteric gliosis in LPS-mice trough the massive reduction of astroglial signalling neurotrophin S100B. Histological, biochemical and immunohistochemical data demonstrated that S100B decrease was associated with a considerable decrease in mast cell and macrophages in the intestine of LPS-treated mice after CBD treatment. Moreover the treatment of LPS-mice with CBD reduced TNF-α expression and the presence of cleaved caspase-3. Similar results were obtained in ex vivo cultured human derived colonic biopsies. In biopsies of UC patients, both during active inflammation and in remission stimulated with LPS+INF-γ, an increased glial cell activation and intestinal damage were evidenced. CBD reduced the expression of S100B and iNOS proteins in the human biopsies confirming its well documented effect in septic mice. The activity of CBD is, at least partly, mediated via the selective PPAR-gamma receptor pathway. CBD targets enteric reactive gliosis, counteracts the inflammatory environment induced by LPS in mice and in human colonic cultures derived from UC patients. These actions lead to a reduction of intestinal damage mediated by PPARgamma receptor pathway. Our results therefore indicate that CBD indeed unravels a new therapeutic strategy to treat inflammatory bowel diseases

Spectral analysis of ground thermal image temperatures at Solfatara crater

including thermal infrared cameras (TIRNet network). This last network is composed by 5 permanent stations. They acquire portions of the Solfatara area characterized by significant thermal anomalies. In this work the dataset is composed by 1347 daily samples from 2014 April 25th to 2017 December 31th, recorded by three TIR stations (Solf1, Solf2 and Ps1), and by environmental pressure and temperature variables. A pre-processing on the data was carried out in order to remove the components associated to the seasonality and the influence of the tides on all the variables. We chose the STL algorithm (Seasonal Decomposition of Time Series by Loess; Cleveland et al. 1990), since it allows to decompose a time series into three components: seasonal, trend and remainder. Then, we performed a harmonic analysis on the deseasonalized signals by using the T_Tide software (Pawlowicz et al., 2002) in order to identify and remove the main tidal constituents (diurnal, semidiurnal and long period). The analysis of the residual time series allows to highlight possible temporal temperature variations both due to endogenous dynamics, or affected by other factors. Possible correlation between thermal anomalies and the environmental parameters can be, then, underlined through spectral analysis (FFT). For the entire dataset, we calculated the periodograms in the band [10-120] day. This analysis permitted to evidence which are the components common with meto-environmental variables and which are features of a specific TIR-site. In particular, we found two spectral peaks, at about 30 and 50 days, common to all the considered variables. Moreover, Solf1 station shows a marked link to the external pressure for periods larger than 45 days, while Solf2 and Ps1 exhibit a behavior similar to external temperature starting from a period of 80 days.PublishedNaples4V. Processi pre-eruttiv

Tracking the Endogenous Dynamics of the Solfatara Volcano (Campi Flegrei, Italy) through the Analysis of Ground Thermal Image Temperatures

In the last decades, thermal infrared ground-based cameras have become effective tools to detect significant spatio-temporal anomalies in the hydrothermal/volcanic environment, possibly linked to impending eruptions. In this paper, we analyzed the temperature time-series recorded by the ground-based Thermal Infrared Radiometer permanent network of INGV-OV, installed inside the Solfatara-Pisciarelli area, the most active fluid emission zones of the Campi Flegrei caldera (Italy). We investigated the temperatures’ behavior in the interval 25 June 2016–29 May 2020, with the aim of tracking possible endogenous hydrothermal/volcanic sources. We performed the Independent Component Analysis, the time evolution estimation of the spectral power, the cross-correlation and the Changing Points’ detection. We compared the obtained patterns with the behavior of atmospheric temperature and pressure, of the time-series recorded by the thermal camera of Mt. Vesuvius, of the local seismicity moment rate and of the CO2 emission flux. We found an overall influence of exogenous, large scale atmospheric effect, which dominated in 2016–2017. Starting from 2018, a clear endogenous forcing overcame the atmospheric factor, and dominated strongly soil temperature variations until the end of the observations. This paper highlights the importance of monitoring and investigating the soil temperature in volcanic environments, as well as the atmospheric parameters

Schools for thought: Overview of the project and lessons learned from one of the sites

https://scholarlycommons.pacific.edu/ed-facbooks/1028/thumbnail.jp