Rationale: Basic science research suggests a causal role for endothelial dysfunction in chronic obstructive pulmonary disease (COPD). Clinical studies examining endothelial function are lacking, particularly early in the disease. Flow-mediated dilation (FMD) is a physiologic measure of endothelial reactivity to endogenous nitric oxide. Objectives: We hypothesized that lower FMD among former smokers would be associated with lower post-bronchodilator FEV 1 , higher percentage of emphysema using computed tomography (CT) and lower diffusing capacity. Methods: We measured FMD, pulmonary function, and CT percentage of emphysema in a random sample of 107 cotinine-confirmed former smokers in the ongoing EMCAP study. FMD was defined as percentage change in the brachial artery diameter with reactive hyperemia. Generalized additive models were used to adjust for potential confounders and assess linearity. Measurements and Main Results: Mean age of participants was 71 6 5 years, 46% were female, and pack-years averaged 48 6 26. Mean FMD was 3.8 6 3.1%; mean post-bronchodilator FEV 1 , 2.3 6 0.8 L; and mean CT percentage of emphysema, 26 6 10%. A 1 SD decrease in FMD was associated with a 132-ml (95% confidence interval, 16-248 ml; P 5 0.03) decrement in post-bronchodilator FEV 1 and a 2.6% (95% confidence interval, 0.5-4.7%; P 5 0.02) increase in CT percentage of emphysema in fully adjusted models. These associations were linear across the spectrum from normality to disease, independent of smoking history, and also significant among participants without COPD. Associations with diffusing capacity were consistent but nonsignificant (P 5 0.09). The FMD-FEV 1 association was entirely attributable to percentage of emphysema. Conclusions: Impaired endothelial function, as measured by FMD, was associated with lower FEV 1 and higher CT percentage of emphysema in former smokers early in COPD.Keywords: pulmonary disease, chronic obstructive; bronchitis, chronic; pulmonary emphysema; endothelial dysfunction Recent research on the pathogenesis of chronic obstructive pulmonary disease (COPD) suggests that perturbations in the vasculature and, specifically, endothelial health may occur early in COPD (1). Decreased expression of vascular endothelial growth factor (VEGF) causes endothelial apoptosis, epithelial apoptosis, and emphysema (2-4). The second messenger lipid ceramide mediates VEGF blockade-induced apoptosis in COPD and ceramide instillation acutely triggers apoptosis in pulmonary endothelial cells, causing emphysema in mice (5).Apoptotic endothelial cells are present in the lungs of smokers with COPD (6, 7), and there are significant morphologic differences in the endothelium of smokers with mild, moderate, and severe COPD compared with smokers without COPD (8). Furthermore, nitric oxide (NO)-mediated, endotheliumdependent relaxation provoked by adenosine diphosphate and acetylcholine is attenuated in the excised pulmonary arteries of patients with COPD compared with those of smoking and nonsmoking control subjects (9, 10). Publ...

Purpose:The amount of fibroglandular tissue content in the breast as estimated mammographically, commonly referred to as breast percent density (PD%), is one of the most significant risk factors for developing breast cancer. Approaches to quantify breast density commonly focus on either semiautomated methods or visual assessment, both of which are highly subjective. Furthermore, most studies published to date investigating computer-aided assessment of breast PD% have been performed using digitized screen-film mammograms, while digital mammography is increasingly replacing screenfilm mammography in breast cancer screening protocols. Digital mammography imaging generates two types of images for analysis, raw (i.e., "FOR PROCESSING") and vendor postprocessed (i.e., "FOR PRESENTATION"), of which postprocessed images are commonly used in clinical practice. Development of an algorithm which effectively estimates breast PD% in both raw and postprocessed digital mammography images would be beneficial in terms of direct clinical application and retrospective analysis. Methods: This work proposes a new algorithm for fully automated quantification of breast PD% based on adaptive multiclass fuzzy c-means (FCM) clustering and support vector machine (SVM) classification, optimized for the imaging characteristics of both raw and processed digital mammography images as well as for individual patient and image characteristics. Our algorithm first delineates the breast region within the mammogram via an automated thresholding scheme to identify background air followed by a straight line Hough transform to extract the pectoral muscle region. The algorithm then applies adaptive FCM clustering based on an optimal number of clusters derived from image properties of the specific mammogram to subdivide the breast into regions of similar graylevel intensity. Finally, a SVM classifier is trained to identify which clusters within the breast tissue are likely fibroglandular, which are then aggregated into a final dense tissue segmentation that is used to compute breast PD%. Our method is validated on a group of 81 women for whom bilateral, mediolateral oblique, raw and processed screening digital mammograms were available, and agreement is assessed with both continuous and categorical density estimates made by a trained breast-imaging radiologist. Results: Strong association between algorithm-estimated and radiologist-provided breast PD% was detected for both raw (r = 0.82, p < 0.001) and processed (r = 0.85, p < 0.001) digital mammograms on a per-breast basis. Stronger agreement was found when overall breast density was assessed on a per-woman basis for both raw (r = 0.85, p < 0.001) and processed (0.89, p < 0.001) mammograms. Strong agreement between categorical density estimates was also seen (weighted Cohen's κ ≥ 0.79). Repeated measures analysis of variance demonstrated no statistically significant differences between the PD% estimates (p > 0.1) due to either presentation of the image (raw vs processed) or method of PD% assessment (ra...

BackgroundMyalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a multi-system illness characterized, in part, by increased fatigue following minimal exertion, cognitive impairment, poor recovery to physical and other stressors, in addition to other symptoms. Unlike healthy subjects and other diseased populations who reproduce objective physiological measures during repeat cardiopulmonary exercise tests (CPETs), ME/CFS patients have been reported to fail to reproduce results in a second CPET performed one day after an initial CPET. If confirmed, a disparity between a first and second CPET could serve to identify individuals with ME/CFS, would be able to document their extent of disability, and could also provide a physiological basis for prescribing physical activity as well as a metric of functional impairment.Methods22 subjects diagnosed with ME/CFS completed two repeat CPETs separated by 24 h. Measures of oxygen consumption (VO2), heart rate (HR), minute ventilation (Ve), workload (Work), and respiratory exchange ratio (RER) were made at maximal (peak) and ventilatory threshold (VT) intensities. Data were analyzed using ANOVA and Wilcoxon’s Signed-Rank Test (for RER).ResultsME/CFS patients showed significant decreases from CPET1 to CPET2 in VO2peak (13.8%), HRpeak (9 bpm), Ve peak (14.7%), and Work@peak (12.5%). Decreases in VT measures included VO2@VT (15.8%), Ve@VT (7.4%), and Work@VT (21.3%). Peak RER was high (≥1.1) and did not differ between tests, indicating maximum effort by participants during both CPETs. If data from only a single CPET test is used, a standard classification of functional impairment based on VO2peak or VO2@VT results in over-estimation of functional ability for 50% of ME/CFS participants in this study.ConclusionME/CFS participants were unable to reproduce most physiological measures at both maximal and ventilatory threshold intensities during a CPET performed 24 hours after a prior maximal exercise test. Our work confirms that repeated CPETs warrant consideration as a clinical indicator for diagnosing ME/CFS. Furthermore, if based on only one CPET, functional impairment classification will be mis-identified in many ME/CFS participants.

IntroductionMammography screening results in a significant number of false-positives. The use of pretest breast cancer risk factors to guide follow-up of abnormal mammograms could improve the positive predictive value of screening. We evaluated the use of the Gail model, body mass index (BMI), and genetic markers to predict cancer diagnosis among women with abnormal mammograms. We also examined the extent to which pretest risk factors could reclassify women without cancer below the biopsy threshold.MethodsWe recruited a prospective cohort of women referred for biopsy with abnormal (BI-RADS 4) mammograms according to the American College of Radiology’s Breast Imaging-Reporting and Data System (BI-RADS). Breast cancer risk factors were assessed prior to biopsy. A validated panel of 12 single-nucleotide polymorphisms (SNPs) associated with breast cancer were measured. Logistic regression was used to assess the association of Gail risk factors, BMI and SNPs with cancer diagnosis (invasive or ductal carcinoma in situ). Model discrimination was assessed using the area under the receiver operating characteristic curve, and calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test. The distribution of predicted probabilities of a cancer diagnosis were compared for women with or without breast cancer.ResultsIn the multivariate model, age (odds ratio (OR) = 1.05; 95% confidence interval (CI), 1.03 to 1.08; P < 0.001), SNP panel relative risk (OR = 2.30; 95% CI, 1.06 to 4.99, P = 0.035) and BMI (≥30 kg/m2 versus <25 kg/m2; OR = 2.20; 95% CI, 1.05 to 4.58; P = 0.036) were significantly associated with breast cancer diagnosis. Older women were more likely than younger women to be diagnosed with breast cancer. The SNP panel relative risk remained strongly associated with breast cancer diagnosis after multivariable adjustment. Higher BMI was also strongly associated with increased odds of a breast cancer diagnosis. Obese women (OR = 2.20; 95% CI, 1.05 to 4.58; P = 0.036) had more than twice the odds of cancer diagnosis compared to women with a BMI <25 kg/m2. The SNP panel appeared to have predictive ability among both white and black women.ConclusionsBreast cancer risk factors, including BMI and genetic markers, are predictive of cancer diagnosis among women with BI-RADS 4 mammograms. Using pretest risk factors to guide follow-up of abnormal mammograms could reduce the burden of false-positive mammograms.Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-014-0509-4) contains supplementary material, which is available to authorized users.

Purpose: Mammographic percent density (PD%) is known to be a strong risk factor for breast cancer. Recent studies also suggest that parenchymal texture features, which are more granular descriptors of the parenchymal pattern, can provide additional information about breast cancer risk. To date, most studies have measured mammographic texture within selected regions of interest (ROIs) in the breast, which cannot adequately capture the complexity of the parenchymal pattern throughout the whole breast. To better characterize patterns of the parenchymal tissue, the authors have developed a fully automated software pipeline based on a novel lattice-based strategy to extract a range of parenchymal texture features from the entire breast region. Methods: Digital mammograms from 106 cases with 318 age-matched controls were retrospectively analyzed. The lattice-based approach is based on a regular grid virtually overlaid on each mammographic image. Texture features are computed from the intersection (i.e., lattice) points of the grid lines within the breast, using a local window centered at each lattice point. Using this strategy, a range of statistical (gray-level histogram, co-occurrence, and run-length) and structural (edge-enhancing, local binary pattern, and fractal dimension) features are extracted. To cover the entire breast, the size of the local window for feature extraction is set equal to the lattice grid spacing and optimized experimentally by evaluating different windows sizes. The association between their lattice-based texture features and breast cancer was evaluated using logistic regression with leave-one-out cross validation and further compared to that of breast PD% and commonly used single-ROI texture features extracted from the retroareolar or the central breast region. Classification performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC). DeLong's test was used to compare the different ROCs in terms of AUC performance. Results: The average univariate performance of the lattice-based features is higher when extracted from smaller than larger window sizes. While not every individual texture feature is superior to breast PD% (AUC: 0.59, STD: 0.03), their combination in multivariate analysis has significantly better performance (AUC: 0.85, STD: 0.02, p < 0.001). The lattice-based texture features also outperform the single-ROI texture features when extracted from the retroareolar or the central breast region (AUC: 0.60-0.74, STD: 0.03). Adding breast PD% does not make a significant performance improvement to the lattice-based texture features or the single-ROI features (p > 0.05). Conclusions: The proposed lattice-based strategy for mammographic texture analysis enables to characterize the parenchymal pattern over the entire breast. As such, these features provide richer information compared to currently used descriptors and may ultimately improve breast cancer risk assessment. Larger studies are warranted to validate these findings and also compare...

Purpose:To evaluate the early implementation of synthesized two-dimensional (s2D) mammography in a population screened entirely with s2D and digital breast tomosynthesis (DBT) (referred to as s2D/DBT) and compare recall rates and cancer detection rates to historic outcomes of digital mammography combined with DBT (referred to as digital mammography/DBT) screening. Materials and Methods:This was an institutional review board-approved and HIPAA-compliant retrospective interpretation of prospectively acquired data with waiver of informed consent. Compared were recall rates, biopsy rates, cancer detection rates, and radiation dose for 15 571 women screened with digital mammography/DBT from October 1, 2011, to February 28, 2013, and 5366 women screened with s2D/ DBT from January 7, 2015, to June 30, 2015. Two-sample z tests of equal proportions were used to determine statistical significance. Results:Recall rate for s2D/DBT versus digital mammography/ DBT was 7.1% versus 8.8%, respectively (P , .001). Biopsy rate for s2D/DBT versus digital mammography/ DBT decreased (1.3% vs 2.0%, respectively; P = .001).There was no significant difference in cancer detection rate for s2D/DBT versus digital mammography/DBT (5.03 of 1000 vs 5.45 of 1000, respectively; P = .72). The average glandular dose was 39% lower in s2D/DBT versus digital mammography/DBT (4.88 mGy vs 7.97 mGy, respectively; P , .001). Conclusion:Screening with s2D/DBT in a large urban practice resulted in similar outcomes compared with digital mammography/DBT imaging. Screening with s2D/DBT allowed for the benefits of DBT with a decrease in radiation dose compared with digital mammography/DBT.q RSNA, 2016

RWL in collegiate wrestlers before a competition appears to cause physiological effects that are accompanied by transient mood reduction and impairment of short-term memory. The potential negative impact of this practice on the student-athlete should be considered.

IntroductionBreast density, commonly quantified as the percentage of mammographically dense tissue area, is a strong breast cancer risk factor. We investigated associations between breast cancer and fully automated measures of breast density made by a new publicly available software tool, the Laboratory for Individualized Breast Radiodensity Assessment (LIBRA).MethodsDigital mammograms from 106 invasive breast cancer cases and 318 age-matched controls were retrospectively analyzed. Density estimates acquired by LIBRA were compared with commercially available software and standard Breast Imaging-Reporting and Data System (BI-RADS) density estimates. Associations between the different density measures and breast cancer were evaluated by using logistic regression after adjustment for Gail risk factors and body mass index (BMI). Area under the curve (AUC) of the receiver operating characteristic (ROC) was used to assess discriminatory capacity, and odds ratios (ORs) for each density measure are provided.ResultsAll automated density measures had a significant association with breast cancer (OR = 1.47–2.23, AUC = 0.59–0.71, P < 0.01) which was strengthened after adjustment for Gail risk factors and BMI (OR = 1.96–2.64, AUC = 0.82–0.85, P < 0.001). In multivariable analysis, absolute dense area (OR = 1.84, P < 0.001) and absolute dense volume (OR = 1.67, P = 0.003) were jointly associated with breast cancer (AUC = 0.77, P < 0.01), having a larger discriminatory capacity than models considering the Gail risk factors alone (AUC = 0.64, P < 0.001) or the Gail risk factors plus standard area percent density (AUC = 0.68, P = 0.01). After BMI was further adjusted for, absolute dense area retained significance (OR = 2.18, P < 0.001) and volume percent density approached significance (OR = 1.47, P = 0.06). This combined area-volume density model also had a significantly (P < 0.001) improved discriminatory capacity (AUC = 0.86) relative to a model considering the Gail risk factors plus BMI (AUC = 0.80).ConclusionsOur study suggests that new automated density measures may ultimately augment the current standard breast cancer risk factors. In addition, the ability to fully automate density estimation with digital mammography, particularly through the use of publically available breast density estimation software, could accelerate the translation of density reporting in routine breast cancer screening and surveillance protocols and facilitate broader research into the use of breast density as a risk factor for breast cancer.Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-015-0626-8) contains supplementary material, which is available to authorized users.