Intraindividual variability in sleep among athletes: A systematic review of definitions, operationalizations, and key correlates.

Via systematic review with narrative synthesis of findings, we aimed to document the ways by which researchers have defined, operationalized, and examined sleep variability among athletes. We identified studies in which scholars examined intraperson variability in sleep among athletes via a search of six databases (Web of Science, Embase, Medline, PsycINFO, CINHAL Plus, and ProQuest Dissertations and Theses Global) using a protocol that included keywords for the target outcome (sleep*), population (athlet* OR sport*), and outcome operationalization (variability OR variation OR "standard deviation" OR fluctuate OR fluctuation OR stability OR instability OR reactivity OR IIV OR intraindividual). We complemented this primary search with citation searching of eligible articles. Assessments of study quality captured eight core elements, namely aims/hypotheses, sample size justification, sample representativeness, number of days sleep assessed, measures of sleep and its correlates, missing data, and inferences and conclusions. From a total of 1209 potentially relevant papers, we identified 16 studies as meeting our eligibility criteria. Concept definitions of variability were notably absent from this work and where available were vague. Quantitative deviations from one's typical level of target sleep metrics reflected the essence by which all but one of the research teams operationalized sleep variability. We assessed the overall quality of empirical work as moderate in nature. We propose a working definition of sleep variability that can inform knowledge generation on the temporal, day-to-day dynamics of sleep functioning that is required for personalized interventions for optimizing sleep health.

Agreement between Breast Percentage Density Estimations from Standard-Dose versus Synthetic Digital Mammograms: Results from a Large Screening Cohort Using Automated Measures.

Purpose To evaluate agreement between automated estimates of breast density made from standard-dose versus synthetic digital mammograms in a large cohort of women undergoing screening. Materials and Methods This study received institutional review board approval with waiver of consent. A total of 3668 negative (Breast Imaging Reporting and Data System category 1 or 2) digital breast tomosynthesis (DBT) screening examinations consecutively performed over a 4-month period at one institution for which both standard-dose and synthetic mammograms were available for analysis were retrospectively analyzed. All mammograms were acquired with a Selenia Dimensions system (Hologic, Bedford, Mass), and synthetic mammograms were generated by using the U.S. Food and Drug Administration-approved "C-View" software module. The "For Presentation" standard-dose mammograms and synthetic images were analyzed by using a fully automated algorithm. Agreement between density estimates was assessed by using Pearson correlation, linear regression, and Bland-Altman analysis. Differences were evaluated by using the paired Student t test. Results Breast percentage density (PD) estimates from synthetic and standard-dose mammograms were highly correlated (r = 0.92, P < .001), and the 95% Bland-Altman limits of agreement between PD estimates were -6.4% to 9.9%. Synthetic mammograms had PD estimates by an average of 1.7% higher than standard-dose mammograms (P < .001), with a larger disagreement by 1.56% in women with highly dense breast tissue (P < .0001). Conclusion Fully automated estimates of breast density made from synthetic mammograms are generally comparable to those made from standard-dose mammograms. This may be important, as standard two-dimensional mammographic images are increasingly being replaced by synthetic mammograms in DBT screening in an attempt to reduce radiation dose. © RSNA, 2017 Online supplemental material is available for this article.

The Impact of Acquisition Dose on Quantitative Breast Density Estimation with Digital Mammography: Results from ACRIN PA 4006.

Purpose To investigate the impact of radiation dose on breast density estimation in digital mammography. Materials and Methods With institutional review board approval and Health Insurance Portability and Accountability Act compliance under waiver of consent, a cohort of women from the American College of Radiology Imaging Network Pennsylvania 4006 trial was retrospectively analyzed. All patients underwent breast screening with a combination of dose protocols, including standard full-field digital mammography, low-dose digital mammography, and digital breast tomosynthesis. A total of 5832 images from 486 women were analyzed with previously validated, fully automated software for quantitative estimation of density. Clinical Breast Imaging Reporting and Data System (BI-RADS) density assessment results were also available from the trial reports. The influence of image acquisition radiation dose on quantitative breast density estimation was investigated with analysis of variance and linear regression. Pairwise comparisons of density estimations at different dose levels were performed with Student t test. Agreement of estimation was evaluated with quartile-weighted Cohen kappa values and Bland-Altman limits of agreement. Results Radiation dose of image acquisition did not significantly affect quantitative density measurements (analysis of variance, P = .37 to P = .75), with percent density demonstrating a high overall correlation between protocols (r = 0.88-0.95; weighted κ = 0.83-0.90). However, differences in breast percent density (1.04% and 3.84%, P < .05) were observed within high BI-RADS density categories, although they were significantly correlated across the different acquisition dose levels (r = 0.76-0.92, P < .05). Conclusion Precision and reproducibility of automated breast density measurements with digital mammography are not substantially affected by variations in radiation dose; thus, the use of low-dose techniques for the purpose of density estimation may be feasible. (©) RSNA, 2016 Online supplemental material is available for this article.

Implementation of Synthesized Two-dimensional Mammography in a Population-based Digital Breast Tomosynthesis Screening Program.

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. © RSNA, 2016 An earlier incorrect version of this article appeared online. This article was corrected on August 11, 2016.

A Comparison of Athletic Movement Among Talent-Identified Juniors From Different Football Codes in Australia: Implications for Talent Development.

Woods, CT, Keller, BS, McKeown, I, and Robertson, S. A comparison of athletic movement among talent-identified juniors from different football codes in Australia: implications for talent development. J Strength Cond Res 30(9): 2440-2445, 2016-This study aimed to compare the athletic movement skill of talent-identified (TID) junior Australian Rules football (ARF) and soccer players. The athletic movement skill of 17 TID junior ARF players (17.5-18.3 years) was compared against 17 TID junior soccer players (17.9-18.7 years). Players in both groups were members of an elite junior talent development program within their respective football codes. All players performed an athletic movement assessment that included an overhead squat, double lunge, single-leg Romanian deadlift (both movements performed on right and left legs), a push-up, and a chin-up. Each movement was scored across 3 essential assessment criteria using a 3-point scale. The total score for each movement (maximum of 9) and the overall total score (maximum of 63) were used as the criterion variables for analysis. A multivariate analysis of variance tested the main effect of football code (2 levels) on the criterion variables, whereas a 1-way analysis of variance identified where differences occurred. A significant effect was noted, with the TID junior ARF players outscoring their soccer counterparts when performing the overhead squat and push-up. No other criterions significantly differed according to the main effect. Practitioners should be aware that specific sporting requirements may incur slight differences in athletic movement skill among TID juniors from different football codes. However, given the low athletic movement skill noted in both football codes, developmental coaches should address the underlying movement skill capabilities of juniors when prescribing physical training in both codes.

Preliminary evaluation of the publicly available Laboratory for Breast Radiodensity Assessment (LIBRA) software tool: comparison of fully automated area and volumetric density measures in a case-control study with digital mammography.

INTRODUCTION: Breast 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). METHODS: Digital 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. RESULTS: All 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). CONCLUSIONS: Our 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.

The use of the Gail model, body mass index and SNPs to predict breast cancer among women with abnormal (BI-RADS 4) mammograms.

INTRODUCTION: Mammography 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. METHODS: We 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. RESULTS: In 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/m(2); 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. CONCLUSIONS: Breast 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.

Associations between breast density and a panel of single nucleotide polymorphisms linked to breast cancer risk: a cohort study with digital mammography.

BACKGROUND: Breast density and single-nucleotide polymorphisms (SNPs) have both been associated with breast cancer risk. To determine the extent to which these two breast cancer risk factors are associated, we investigate the association between a panel of validated SNPs related to breast cancer and quantitative measures of mammographic density in a cohort of Caucasian and African-American women. METHODS: In this IRB-approved, HIPAA-compliant study, we analyzed a screening population of 639 women (250 African American and 389 Caucasian) who were tested with a validated panel assay of 12 SNPs previously associated to breast cancer risk. Each woman underwent digital mammography as part of routine screening and all were interpreted as negative. Both absolute and percent estimates of area and volumetric density were quantified on a per-woman basis using validated software. Associations between the number of risk alleles in each SNP and the density measures were assessed through a race-stratified linear regression analysis, adjusted for age, BMI, and Gail lifetime risk. RESULTS: The majority of SNPs were not found to be associated with any measure of breast density. SNP rs3817198 (in LSP1) was significantly associated with both absolute area (p = 0.004) and volumetric (p = 0.019) breast density in Caucasian women. In African-American women, SNPs rs3803662 (in TNRC9/TOX3) and rs4973768 (in NEK10) were significantly associated with absolute (p = 0.042) and percent (p = 0.028) volume density respectively. CONCLUSIONS: The majority of SNPs investigated in our study were not found to be significantly associated with breast density, even when accounting for age, BMI, and Gail risk, suggesting that these two different risk factors contain potentially independent information regarding a woman's risk to develop breast cancer. Additionally, the few statistically significant associations between breast density and SNPs were different for Caucasian versus African American women. Larger prospective studies are warranted to validate our findings and determine potential implications for breast cancer risk assessment.

Performance Evaluation of an Automated Peritoneal Dialysis Solution Generation System in Patients Using Automated Peritoneal Dialysis.

Introduction: Most patients on peritoneal dialysis (PD) in the United States are on automated PD (APD) utilizing several liters of PD solution daily for their treatment. The ordering, delivery, and storage of PD solutions can be challenging and is an important factor that can dissuade patients from doing PD. The generation of PD solutions at home is a strategy that could potentially be used to overcome this problem. The APD Solution Generation System (SGS) allowed for PD solution generation using tap water in patients' homes. Methods: In this study, we set out to evaluate the performance of the SGS in prevalent, adult patients with end-stage kidney disease, who are on maintenance PD. We evaluated the primary safety (microbiological testing) and efficacy (chemical composition) of the product water generated by the SGS device. Results: Twenty-two patients from 12 different United States centers were enrolled, of which 14 patients completed the study. The results of the primary safety and efficacy end point analyses of the product water showed that all 64 samples met the International Organization for Standardization (ISO) specifications. Secondary safety analysis found a total of 34 adverse events (AEs) in 12 patients. Of these AEs, 3, namely, culture negative peritonitis, bacterial peritonitis, and atrial fibrillation were deemed serious treatment-emergent AEs. Conclusion: This study demonstrated that the SGS can successfully generate PD solution in patients' homes, while meeting chemical composition and ISO microbiological standards. Lessons learned from this clinical trial will be useful in optimizing product development and future clinical trials.

Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation.

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 screen-film 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 gray-level 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 (radiologist vs algorithm). CONCLUSIONS: The proposed fully automated algorithm was successful in estimating breast percent density from both raw and processed digital mammographic images. Accurate assessment of a woman's breast density is critical in order for the estimate to be incorporated into risk assessment models. These results show promise for the clinical application of the algorithm in quantifying breast density in a repeatable manner, both at time of imaging as well as in retrospective studies.

Multivariate compensation of quantitative pulmonary emphysema metric variation from low-dose, whole-lung CT scans.

OBJECTIVE: Emphysema is a disease of the lung characterized by the destruction of the alveolar sac walls. Several quantitative densitometric measures of emphysema from wholelung CT have been proposed for evaluating disease severity and progression. However, a concern with these quantitative measures has been the large interscan variability observed during longitudinal studies of emphysema. To account for and reduce inherent measure variability, this work implements and evaluates the use of a multivariate random-effects model for correcting longitudinal variation in densitometric scores of emphysema due to inspiration. MATERIALS AND METHODS: The method of multivariate compensation was validated on three of the most commonly reported densitometric measures of emphysema: the emphysema index, histogram percentile, and fractal dimension. Two short-interval, zero-change datasets, one for model development (n = 105) and one for validation (n = 106), were retrospectively identified and used to ensure that all variation was caused by inherent measure variability. RESULTS: A statistically significant (F test, p < 0.001) reduction of 42.40% in measurement limits of agreement could be obtained after model application, with compensated emphysema metric differences showing 31-33% of the variance compared with uncompensated metric variance. CONCLUSION: Compensation was still effective when the trained model was applied to the second validation dataset. Multivariate compensation was found to be useful in reducing interscan measurement variability and should be applied to future longitudinal studies of emphysema.

Impaired flow-mediated dilation is associated with low pulmonary function and emphysema in ex-smokers: the Emphysema and Cancer Action Project (EMCAP) Study.

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 +/- 5 years, 46% were female, and pack-years averaged 48 +/- 26. Mean FMD was 3.8 +/- 3.1%; mean post-bronchodilator FEV(1), 2.3 +/- 0.8 L; and mean CT percentage of emphysema, 26 +/- 10%. A 1 SD decrease in FMD was associated with a 132-ml (95% confidence interval, 16-248 ml; P = 0.03) decrement in post-bronchodilator FEV(1) and a 2.6% (95% confidence interval, 0.5-4.7%; P = 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 = 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.

Association between Breast Parenchymal Complexity and False-Positive Recall From Digital Mammography Versus Breast Tomosynthesis: Preliminary Investigation in the ACRIN PA 4006 Trial.

RATIONALE AND OBJECTIVES: We investigate associations between measures of mammographic parenchymal complexity and false-positive (FP) recall from screening with digital mammography (DM) versus digital breast tomosynthesis (DBT). MATERIALS AND METHODS: We retrospectively analyzed data from 541 women recruited by the American College of Radiology Imaging Network 4006 trial, designed to evaluate callback and detection rates from screening with DM versus combined DM and DBT. Of these, 68 and 56 were FPs based on DM alone versus the combined DM/DBT readings, respectively. Mammographic complexity was quantified with computerized texture analysis and percent density. Logistic regression was performed to evaluate associations between extracted features and FP recall, after adjusting for age and number of previous benign biopsies. Odds ratios and area under the curve (AUC) of the receiver operating characteristic were used to assess association strength. RESULTS: For DM, age, previous benign biopsies and texture features of correlation, inverse difference moment, sum average, and sum variance were deemed as significant predictors (P <.05) of FP recall, with an AUC = 0.77. For DBT, age was the only significant predictor of FP recall with AUC = 0.64. Using exploratory receiver operating characteristic thresholds for which no true-positives would be missed, a potential FP reduction of 23.5% and 8.9% was demonstrated, respectively, for DM alone versus DM/DBT. CONCLUSION: Measures of breast complexity measured on 2D digital mammograms are indicative of the likelihood for FP recall from screening with DM, and could help identify women who could benefit from supplemental screening, including DBT.

Breast parenchymal patterns in processed versus raw digital mammograms: A large population study toward assessing differences in quantitative measures across image representations.

PURPOSE: With raw digital mammograms (DMs), which retain the relationship with x-ray attenuation of the breast tissue, not being routinely available, processed DMs are often the only viable means to acquire imaging measures. The authors investigate differences in quantitative measures of breast density and parenchymal texture, shown to have value in breast cancer risk assessment, between the two DM representations. METHODS: The authors report data from 8458 pairs of bilateral raw ("FOR PROCESSING") and processed ("FOR PRESENTATION") DMs acquired from 4278 women undergoing routine screening evaluation, collected with DM units from two different vendors. Breast dense tissue area and percent density (PD), as well as a range of quantitative descriptors of breast parenchymal texture (statistical, co-occurrence, run-length, and structural descriptors), were measured using previously validated, fully automated software. Feature measurements were compared using matched-pairs Wilcoxon signed-ranks test, correlation (r), and linear-mixed-effects (LME) models, where potential interactions with woman- and system-specific factors were also assessed. The authors also compared texture feature correlations with the established risk factors of the Gail lifetime risk score (rG) and breast PD (rPD), and evaluated the within woman intraclass feature correlation (ICC), a measure of bilateral breast-tissue symmetry, in raw versus processed images. RESULTS: All density measures and most of the texture features were strongly (r ≥ 0.6) or moderately (0.4 ≤ r < 0.6) correlated between raw and processed images. However, measurements were significantly different between the two imaging formats (Wilcoxon signed-ranks test, pw < 0.05). The association between measurements varied across features and vendors, and was substantially modified by woman- and system-specific image acquisition factors, such as age, BMI, and mAs/kVp, respectively. The strongest correlation, combined with minimal LME-model interactions, was observed for structural texture features. Overall, texture measures from either image representation were weakly associated with Gail lifetime risk (-0.2 ≤ rG ≤ 0.2), weakly to moderately associated with breast PD (-0.6 ≤ rPD ≤ 0.6), and had overall strong bilateral symmetry (ICC ≥ 0.6). CONCLUSIONS: Differences in measures from processed versus raw DM depend highly on the feature, the DM vendor, and image acquisition settings, where structural features appear to be more robust across the different DM settings. The reported findings may serve as a reference in the design of future large-scale studies on mammographic features and breast cancer risk assessment involving multiple DM representations.

Racial Differences in Quantitative Measures of Area and Volumetric Breast Density.

BACKGROUND: Increased breast density is a strong risk factor for breast cancer and also decreases the sensitivity of mammographic screening. The purpose of our study was to compare breast density for black and white women using quantitative measures. METHODS: Breast density was assessed among 5282 black and 4216 white women screened using digital mammography. Breast Imaging-Reporting and Data System (BI-RADS) density was obtained from radiologists' reports. Quantitative measures for dense area, area percent density (PD), dense volume, and volume percent density were estimated using validated, automated software. Breast density was categorized as dense or nondense based on BI-RADS categories or based on values above and below the median for quantitative measures. Logistic regression was used to estimate the odds of having dense breasts by race, adjusted for age, body mass index (BMI), age at menarche, menopause status, family history of breast or ovarian cancer, parity and age at first birth, and current hormone replacement therapy (HRT) use. All statistical tests were two-sided. RESULTS: There was a statistically significant interaction of race and BMI on breast density. After accounting for age, BMI, and breast cancer risk factors, black women had statistically significantly greater odds of high breast density across all quantitative measures (eg, PD nonobese odds ratio [OR] = 1.18, 95% confidence interval [CI] = 1.02 to 1.37, P = .03, PD obese OR = 1.26, 95% CI = 1.04 to 1.53, P = .02). There was no statistically significant difference in BI-RADS density by race. CONCLUSIONS: After accounting for age, BMI, and other risk factors, black women had higher breast density than white women across all quantitative measures previously associated with breast cancer risk. These results may have implications for risk assessment and screening.

Inhibition of ileal bile acid uptake protects against nonalcoholic fatty liver disease in high-fat diet-fed mice.

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease in the Western world, and safe and effective therapies are needed. Bile acids (BAs) and their receptors [including the nuclear receptor for BAs, farnesoid X receptor (FXR)] play integral roles in regulating whole-body metabolism and hepatic lipid homeostasis. We hypothesized that interruption of the enterohepatic BA circulation using a luminally restricted apical sodium-dependent BA transporter (ASBT) inhibitor (ASBTi; SC-435) would modify signaling in the gut-liver axis and reduce steatohepatitis in high-fat diet (HFD)-fed mice. Administration of this ASBTi increased fecal BA excretion and messenger RNA (mRNA) expression of BA synthesis genes in liver and reduced mRNA expression of ileal BA-responsive genes, including the negative feedback regulator of BA synthesis, fibroblast growth factor 15. ASBT inhibition resulted in a marked shift in hepatic BA composition, with a reduction in hydrophilic, FXR antagonistic species and an increase in FXR agonistic BAs. ASBT inhibition restored glucose tolerance, reduced hepatic triglyceride and total cholesterol concentrations, and improved NAFLD activity score in HFD-fed mice. These changes were associated with reduced hepatic expression of lipid synthesis genes (including liver X receptor target genes) and normalized expression of the central lipogenic transcription factor, Srebp1c Accumulation of hepatic lipids and SREBP1 protein were markedly reduced in HFD-fed Asbt(-/-) mice, providing genetic evidence for a protective role mediated by interruption of the enterohepatic BA circulation. Together, these studies suggest that blocking ASBT function with a luminally restricted inhibitor can improve both hepatic and whole body aspects of NAFLD.

Parenchymal texture analysis in digital mammography: A fully automated pipeline for breast cancer risk assessment.

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 to standard demographic and reproductive risk factors.

Parenchymal texture analysis in digital mammography: robust texture feature identification and equivalence across devices.

An analytical framework is presented for evaluating the equivalence of parenchymal texture features across different full-field digital mammography (FFDM) systems using a physical breast phantom. Phantom images (FOR PROCESSING) are acquired from three FFDM systems using their automated exposure control setting. A panel of texture features, including gray-level histogram, co-occurrence, run length, and structural descriptors, are extracted. To identify features that are robust across imaging systems, a series of equivalence tests are performed on the feature distributions, in which the extent of their intersystem variation is compared to their intrasystem variation via the Hodges-Lehmann test statistic. Overall, histogram and structural features tend to be most robust across all systems, and certain features, such as edge enhancement, tend to be more robust to intergenerational differences between detectors of a single vendor than to intervendor differences. Texture features extracted from larger regions of interest (i.e., [Formula: see text]) and with a larger offset length (i.e., [Formula: see text]), when applicable, also appear to be more robust across imaging systems. This framework and observations from our experiments may benefit applications utilizing mammographic texture analysis on images acquired in multivendor settings, such as in multicenter studies of computer-aided detection and breast cancer risk assessment.

Feasibility study of particulate extracellular matrix (P-ECM) and left ventricular assist device (HVAD) therapy in chronic ischemic heart failure bovine model.

Myocardial recovery with left ventricular assist device (LVAD) support is uncommon and unpredictable. We tested the hypothesis that injectable particulate extracellular matrix (P-ECM) with LVAD support promotes cell proliferation and improves cardiac function. LVAD, P-ECM, and P-ECM + LVAD therapies were investigated in chronic ischemic heart failure (IHF) calves induced using coronary embolization. Particulate extracellular matrix emulsion (CorMatrix, Roswell, GA) was injected intramyocardially using a 7 needle pneumatic delivery tool. Left ventricular assist devices (HVAD, HeartWare) were implanted in a left ventricle (LV) apex to proximal descending aorta configuration. Cell proliferation was identified using BrdU (5 mg/kg) injections over the last 45 treatment days. Echocardiography was performed weekly. End-organ regional blood flow (RBF) was quantified at study endpoints using fluorescently labeled microspheres. Before treatment, IHF calves had an ejection fraction (EF) of 33 ± 2% and left ventricular end-diastolic volume of 214 ± 18 ml with cardiac cachexia (0.69 ± 0.06 kg/day). Healthy weight gain was restored in all groups (0.89 ± 0.03 kg/day). EF increased with P-ECM + HVAD from 36 ± 5% to 75 ± 2%, HVAD 38 ± 4% to 58 ± 5%, and P-ECM 27 ± 1% to 66 ± 6%. P-ECM + HVAD demonstrated the largest increase in cell proliferation and end-organ RBF. This study demonstrates the feasibility of combined LVAD support with P-ECM injection to stimulate new cell proliferation and improve cardiac function, which warrants further investigation.

Inhibition of ileal bile acid transport lowers plasma cholesterol levels by inactivating hepatic farnesoid X receptor and stimulating cholesterol 7 alpha-hydroxylase.

We investigated the effect of SC-435, a competitive inhibitor of ileal apical sodium-dependent bile acid cotransporter (ASBT) on ileal bile acid absorption and the hepatic nuclear receptor FXR (farnesoid X receptor), which regulates cholesterol 7 alpha-hydroxylase (CYP7A1) activity and mRNA levels. Eighteen New Zealand White (NZW) rabbits were divided into 2 groups: controls (n = 10) and fed SC-435 125 mg/kg/d for 1 week (n = 8). In rabbits treated with SC-435, fecal bile acid outputs increased by more than 8 times, reflecting substantial bile acid malabsorption. Plasma cholesterol levels decreased 26%, while bile acid pool sizes and biliary bile acid outputs did not change after treatment. CYP7A1 activity increased 64% and mRNA rose by 4 times after treatment. The expression of FXR target genes in the liver, short heterodimer partner (SHP) and bile salt export pump (BSEP), decreased 11.6 and 2.6 times, respectively, after treatment, which indicates inactivation of hepatic FXR. However, the mRNA levels of ileal bile acid binding protein (IBABP) did not change significantly, while ileal ASBT mRNA expression increased by 2.4 times after treatment. Rabbits treated with SC-435 developed ileal bile acid malabsorption, which decreased the return of bile acids (FXR ligands) to the liver to inactivate hepatic FXR, which upregulated CYP7A1 and lowered plasma cholesterol levels. Although fecal bile acid malabsorption was substantial, increased bile acid production from hepatic cholesterol kept biliary bile acid outputs intact. Thus, a new balance was reached in the liver, where increased bile acid synthesis compensated for diminished ileal bile acid absorption to maintain the circulating enterohepatic bile acid pool.