[Show abstract][Hide abstract] ABSTRACT: This study prospectively investigates associations between youth moderate-to-vigorous-intensity physical activity (MVPA) and body composition in young adult women using data from the Dietary Intervention Study in Children (DISC) and the DISC06 Follow-Up Study. MVPA was assessed by questionnaire on 5 occasions between the ages 8 and 18 years and at age 25-29 years in 215 DISC female participants. Using whole body dual-energy x-ray absorptiometry (DXA), overall adiposity and body fat distribution were assessed at age 25-29 years by percent body fat (%fat) and android-to-gynoid (A:G) fat ratio, respectively. Linear mixed effects models and generalized linear latent and mixed models were used to assess associations of youth MVPA with both outcomes. Young adult MVPA, adjusted for other young adult characteristics, was significantly inversely associated with young adult %fat (%fat decreased from 37.4% in the lowest MVPA quartile to 32.8% in the highest (p-trend=0.02)). Adjusted for youth and young adult characteristics including young adult MVPA, youth MVPA also was significantly inversely associated with young adult %fat (β=-0.40 per 10 MET-hrs/wk, p=0.02) . No significant associations between MVPA and A:G fat ratio were observed. Results suggest that youth and young adult MVPA are important independent predictors of adiposity in young women.
[Show abstract][Hide abstract] ABSTRACT: Breast density is a strong risk factor for breast cancer and reflects epithelial and stromal content. Breast tissue is particularly sensitive to hormonal stimuli before it fully differentiates following the first full-term pregnancy. Few studies have examined associations between sex hormones and breast density among young women.
Cancer Epidemiology Biomarkers & Prevention 11/2014; · 4.32 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: New models describing anthropometrically adjusted normal values of bone mineral density and content in children have been created for the various measurement sites. The inclusion of multiple explanatory variables in the models provides the opportunity to calculate Z-scores that are adjusted with respect to the relevant anthropometric parameters.
Osteoporosis International 10/2014; · 4.17 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Breast density is an established predictor of breast cancer risk, and there is considerable interest in associations of modifiable lifestyle factors, such as diet, with breast density.
Journal of the American Academy of Nutrition and Dietetics 10/2014; · 2.44 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: IntroductionMammographic density is a strong breast cancer risk factor and a major determinant of screening sensitivity. However, there is currently no validated estimation method for full-field digital mammography (FFDM).Methods
The performance of three area-based approaches (BI-RADS, the semi-automated Cumulus, and the fully-automated ImageJ-based approach) and three fully-automated volumetric methods (Volpara, Quantra and single X-ray absorptiometry (SXA)) were assessed in 3168 FFDM images from 414 cases and 685 controls. Linear regression models were used to assess associations between breast cancer risk factors and density among controls, and logistic regression models to assess density-breast cancer risk associations, adjusting for age, body mass index (BMI) and reproductive variables.ResultsQuantra and the ImageJ-based approach failed to produce readings for 4% and 11% of the participants. All six density assessment methods showed that percent density (PD) was inversely associated with age, BMI, being parous and postmenopausal at mammography. PD was positively associated with breast cancer for all methods, but with the increase in risk per standard deviation increment in PD being highest for Volpara (1.83; 95% CI: 1.51 to 2.21) and Cumulus (1.58; 1.33 to 1.88) and lower for the ImageJ-based method (1.45; 1.21 to 1.74), Quantra (1.40; 1.19 to 1.66) and SXA (1.37; 1.16 to 1.63). Women in the top PD quintile (or BI-RADS 4) had 8.26 (4.28 to 15.96), 3.94 (2.26 to 6.86), 3.38 (2.00 to 5.72), 2.99 (1.76 to 5.09), 2.55 (1.46 to 4.43) and 2.96 (0.50 to 17.5) times the risk of those in the bottom one (or BI-RADS 1), respectively, for Volpara, Quantra, Cumulus, SXA, ImageJ-based method, and BI-RADS (P for trend <0.0001 for all). The ImageJ-based method had a slightly higher ability to discriminate between cases and controls (area under the curve (AUC) for PD¿=¿0.68, P¿=¿0.05), and Quantra slightly lower (AUC¿=¿0.63; P¿=¿0.06), than Cumulus (AUC¿=¿0.65).Conclusions
Fully-automated methods are valid alternatives to the labour-intensive ¿gold standard¿ Cumulus for quantifying density in FFDM. The choice of a particular method will depend on the aims and setting, but the same approach will be required for longitudinal density assessments.
Breast cancer research: BCR 09/2014; 16(5):439. · 5.88 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Background: Mammographic density (MD), the area of non-fatty appearing tissue divided by total breast area, is a strong breast cancer risk factor. Most MD analyses have employed visual categorizations or computer-assisted quantification, which ignore breast thickness. We explored MD volume and area, using a volumetric approach previously validated as predictive of breast cancer risk, in relation to risk factors among women undergoing breast biopsy. Methods: Among 413 primarily white women, ages 40-65, undergoing diagnostic breast biopsies between 2007-2010 at an academic facility in Vermont, MD volume (cm3) was quantified in cranio-caudal views of the breast contralateral to the biopsy target using a density phantom, while MD area (cm2) was measured on the same digital mammograms using thresholding software. Risk factor associations with continuous MD measurements were evaluated using linear regression. Results: Percent MD volume and area were correlated (r=0.81) and strongly and inversely associated with age, body mass index (BMI), and menopause. Both measures were inversely associated with smoking and positively associated with breast biopsy history. Absolute MD measures were correlated (r=0.46) and inversely related to age and menopause. Whereas absolute dense area was inversely associated with BMI, absolute dense volume was positively associated. Conclusions: Volume and area MD measures exhibit some overlap in risk factor associations, but divergence as well, particularly for BMI. Impact: Findings suggest that volume and area density measures differ in subsets of women; notably, among obese women, absolute density was higher with volumetric methods, suggesting that breast cancer risk assessments may vary for these techniques.
[Show abstract][Hide abstract] ABSTRACT: Introduction: Mammographic density is a strong risk factor for breast cancer and an important determinant of screening sensitivity, but its clinical utility is hampered due to the lack of objective and automated measures. We evaluated the performance of a fully automated volumetric method (Volpara®). Methods: A prospective cohort study including 41,102 women attending mammography screening, of whom 206 were diagnosed with breast cancer after a median follow-up of 15.2 months. Percent and absolute dense volume were estimated from raw digital mammograms. Genotyping was performed in a subset of the cohort (N = 2,122). We examined the agreement by side and view and compared density distributions across different mammography systems. We also studied associations with established density determinants and breast cancer risk. Results: The method showed good agreement by side and view and distributions of percent and absolute dense volume were similar across mammography systems. Volumetric density was positively associated with nulliparity, age at first birth, hormone use, benign breast disease and family history of breast cancer, and negatively with age and postmenopausal status. Associations were also observed with rs10995190 in the ZNF365 gene (P < 1.0 x 10-6) and breast cancer risk (hazard ratio for the highest versus lowest quartile = 2.93 [95% CI 1.73-4.96] and 1.63 [1.10-2.42] for percent and absolute dense volume respectively). Conclusions: In a high-throughput setting Volpara performs well and in accordance with the behavior of established density measures. Impact: Automated measurement of volumetric mammographic density is a promising tool for widespread breast cancer risk assessment.
[Show abstract][Hide abstract] ABSTRACT: Purpose
Pregnancy characteristics have been associated with breast cancer risk, but information is limited on their relationship with breast density. Our objective was to examine the relationship between first pregnancy characteristics and later life breast density, and whether the association is modified by genotype.
The Marin Women’s Study was initiated to examine breast cancer in a high-incidence mammography population (Marin County, CA). Reproductive characteristics and pregnancy information including pregnancy-induced hypertension (PIH) were self-reported at the time of mammography. Forty-seven candidate single nucleotide polymorphisms were obtained from saliva samples; seven were assessed in relation to PIH and percent fibroglandular volume (%FGV). Breast density assessed as %FGV was measured on full-field digital mammograms by the San Francisco Mammography Registry.
A multivariable regression model including 2,440 parous women showed that PIH during first pregnancy was associated with a statistically significant decrease in %FGV (b = −0.31, 95 % CI −0.52, −0.11), while each month of breast-feeding after first birth was associated with a statistically significant increase in %FGV (b = 0.01, 95 % CI 0.003, 0.02). PIH and breast-feeding associations with %FGV were modified by age at first birth. In a subsample of 1,240 women, there was evidence of modification in the association between PIH and %FGV by specific vascular endothelial growth factor (VEGF) (rs3025039) and insulin growth factor receptor-1 (IGFR1) (rs2016347) gene variants.
These findings suggest that first pregnancy characteristics may exert an influence on extent of breast density later in life and that this influence may vary depending on inherited IGFR1 and VEGF genotypes.
Cancer Causes and Control 07/2014; 25(7). · 2.96 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Background: Mammographic density is a strong risk factor for breast cancer. Methods: We present a novel approach to enhance area density measures which takes advantage of the relative density of the pectoral muscle that appears in lateral mammographic views. We hypothesised that the grey scale of film mammograms is normalised to volume breast density but not pectoral density and thus pectoral density becomes an independent marker of volumetric density. Results: From analysis of data from a Swedish case-control study (1286 breast cancer cases and 1391 control subjects, aged 50-75 years) we found that the mean intensity of the pectoral muscle (MIP) was highly associated with breast cancer risk (per s.d OR=0.82, 95% CI: (0.75, 0.88), p = 6×10-7) after adjusting for a validated computer-assisted measure of percent density (PD), Cumulus. The AUC changed from 0.600 to 0.618 due to using PD with the pectoral muscle as reference instead of a standard area-based PD measure. We showed that MIP is associated with a genetic variant known to be associated with mammographic density and breast cancer risk, rs10995190, in a subset of women with genetic data. We further replicated the association between MIP and rs10995190 in an additional cohort of 2655 breast cancer cases (combined p = 0.0002). Conclusions: MIP is a marker of volumetric density which can be used to complement area PD in mammographic density studies and breast cancer risk assessment. Impact: Inclusion of MIP in risk models should be considered for studies using area PD from analog films.
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A summary of the contribution of this paper is best put by one of the anonymous reviewers who handled the review of our manuscript: "This manuscript presents an interesting and novel approach to enhance mammographic density assessment from 2-dimensional images. It has been long been considered a weakness that the common assessment methods using film mammograms ignore the third dimension of breast thickness. Therefore, this paper makes a new contribution to the field of mammographic density research..."
Cancer Epidemiology Biomarkers & Prevention 04/2014; · 4.56 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The image shape and texture (appearance) estimation designed for facial recognition is a novel and promising approach for application in breast imaging. The purpose of this study was to apply a shape and appearance model to automatically estimate percent breast fibroglandular volume (%FGV) using digital mammograms. We built a shape and appearance model using 2000 full-field digital mammograms from the San Francisco Mammography Registry with known %FGV measured by single energy absorptiometry method. An affine transformation was used to remove rotation, translation and scale. Principal Component Analysis (PCA) was applied to extract significant and uncorrelated components of %FGV. To build an appearance model, we transformed the breast images into the mean texture image by piecewise linear image transformation. Using PCA the image pixels grey-scale values were converted into a reduced set of the shape and texture features. The stepwise regression with forward selection and backward elimination was used to estimate the outcome %FGV with shape and appearance features and other system parameters. The shape and appearance scores were found to correlate moderately to breast %FGV, dense tissue volume and actual breast volume, body mass index (BMI) and age. The highest Pearson correlation coefficient was equal 0.77 for the first shape PCA component and actual breast volume. The stepwise regression method with ten-fold cross-validation to predict %FGV from shape and appearance variables and other system outcome parameters generated a model with a correlation of r(2) = 0.8. In conclusion, a shape and appearance model demonstrated excellent feasibility to extract variables useful for automatic %FGV estimation. Further exploring and testing of this approach is warranted.
Proceedings - Society of Photo-Optical Instrumentation Engineers 03/2014; 9034:90342T.
[Show abstract][Hide abstract] ABSTRACT: Purpose. Investigate whether knowledge of the biologic image composition of mammographic lesions provides imagebased biomarkers above and beyond those obtainable from quantitative image analysis (QIA) of X-ray mammography. Methods. The dataset consisted of 45 in vivo breast lesions imaged with the novel 3-component breast (3CB) imaging technique based on dual-energy mammography (15 malignant, 30 benign diagnoses). The 3CB composition measures of water, lipid, and protein thicknesses were assessed and mathematical descriptors, ‘3CB features’, were obtained for the lesions and their periphery. The raw low-energy mammographic images were analyzed with an established in-house QIA method obtaining ‘QIA features’ describing morphology and texture. We investigated the correlation within the ‘3CB features’, within the ‘QIA features’, and between the two. In addition, the merit of individual features in the distinction between malignant and benign lesions was assessed. Results. Whereas many descriptors within the ‘3CB features’ and ‘QIA features’ were, often by design, highly correlated, correlation between descriptors of the two feature groups was much weaker (maximum absolute correlation coefficient 0.58, p<0.001) indicating that 3CB and QIA-based biomarkers provided potentially complementary information. Single descriptors from 3CB and QIA appeared equally well-suited for the distinction between malignant and benign lesions, with maximum area under the ROC curve 0.71 for a protein feature (3CB) and 0.71 for a texture feature (QIA). Conclusions. In this pilot study analyzing the new 3CB imaging modality, knowledge of breast tissue composition appeared additive in combination with existing mammographic QIA methods for the distinction between benign and malignant lesions.
[Show abstract][Hide abstract] ABSTRACT: To investigate whether biologic image composition of mammographic lesions can improve upon existing mammographic quantitative image analysis (QIA) in estimating the probability of malignancy.
The study population consisted of 45 breast lesions imaged with dual-energy mammography prior to breast biopsy with final diagnosis resulting in 10 invasive ductal carcinomas, 5 ductal carcinomain situ, 11 fibroadenomas, and 19 other benign diagnoses. Analysis was threefold: (1) The raw low-energy mammographic images were analyzed with an established in-house QIA method, "QIA alone," (2) the three-compartment breast (3CB) composition measure-derived from the dual-energy mammography-of water, lipid, and protein thickness were assessed, "3CB alone", and (3) information from QIA and 3CB was combined, "QIA + 3CB." Analysis was initiated from radiologist-indicated lesion centers and was otherwise fully automated. Steps of the QIA and 3CB methods were lesion segmentation, characterization, and subsequent classification for malignancy in leave-one-case-out cross-validation. Performance assessment included box plots, Bland-Altman plots, and Receiver Operating Characteristic (ROC) analysis.
The area under the ROC curve (AUC) for distinguishing between benign and malignant lesions (invasive and DCIS) was 0.81 (standard error 0.07) for the "QIA alone" method, 0.72 (0.07) for "3CB alone" method, and 0.86 (0.04) for "QIA+3CB" combined. The difference in AUC was 0.043 between "QIA + 3CB" and "QIA alone" but failed to reach statistical significance (95% confidence interval [-0.17 to + 0.26]).
In this pilot study analyzing the new 3CB imaging modality, knowledge of the composition of breast lesions and their periphery appeared additive in combination with existing mammographic QIA methods for the distinction between different benign and malignant lesion types.
Medical Physics 03/2014; 41(3):031915. · 3.01 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: We report on the design of the technique combining 3D optical imaging and dual-energy absorptiometry body scanning to estimate local body area compositions of three compartments. Dual-energy attenuation and body shape measures are used together to solve for the three compositional tissue thicknesses: water, lipid, and protein. We designed phantoms with tissue-like properties as our reference standards for calibration purposes. The calibration was created by fitting phantom values using non-linear regression of quadratic and truncated polynomials. Dual-energy measurements were performed on tissue-mimicking phantoms using a bone densitometer unit. The phantoms were made of materials shown to have similar x-ray attenuation properties of the biological compositional compartments. The components for the solid phantom were tested and their high energy/low energy attenuation ratios are in good correspondent to water, lipid, and protein for the densitometer x-ray region. The three-dimensional body shape was reconstructed from the depth maps generated by Microsoft Kinect for Windows. We used open-source Point Cloud Library and freeware software to produce dense point clouds. Accuracy and precision of compositional and thickness measures were calculated. The error contributions due to two modalities were estimated. The preliminary phantom composition and shape measurements are found to demonstrate the feasibility of the method proposed.
Proceedings - Society of Photo-Optical Instrumentation Engineers. 01/2014; 8937.
[Show abstract][Hide abstract] ABSTRACT: Early assessment of bone mass may be useful for predicting future osteoporosis risk if bone measures "track" during growth. This prospective longitudinal multicenter study examined tracking of bone measures in children and adolescents over 6 years to sexual and skeletal maturity.
A total of 240 healthy male and 293 healthy female patients, ages 6-17 years, underwent yearly evaluations of height, weight, body mass index, skeletal age, Tanner stage, and dual-energy x-ray absorptiometry (DXA) bone measurements of the whole body, spine, hip, and forearm for 6 years. All subjects were sexually and skeletally mature at final follow-up. Correlation was performed between baseline and 6-year follow-up measures, and change in DXA Z-scores was examined for subjects who had baseline Z < -1.5.
DXA Z-scores (r = 0.66-0.87) had similar tracking to anthropometric measures (r = 0.64-0.74). Tracking was stronger for bone mineral density compared with bone mineral content and for girls compared with boys. Tracking was weakest during mid- to late puberty but improved when Z-scores were adjusted for height. Almost all subjects with baseline Z < -1.5 had final Z-scores below average, with the majority remaining less than -1.0.
Bone status during childhood is a strong predictor of bone status in young adulthood, when peak bone mass is achieved. This suggests that bone mass measurements in children and adolescents may be useful for early identification of individuals at risk for osteoporosis later in life.
The Journal of pediatrics 01/2014; · 4.02 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Clinical scores of mammographic breast density are highly subjective. Automated technologies for mammography exist to quantify breast density objectively, but the technique that most accurately measures the quantity of breast fibroglandular tissue is not known.
To compare the agreement of three automated mammographic techniques for measuring volumetric breast density with a quantitative volumetric MRI-based technique in a screening population.
Women were selected from the UCSF Medical Center screening population that had received both a screening MRI and digital mammogram within one year of each other, had Breast Imaging Reporting and Data System (BI-RADS) assessments of normal or benign finding, and no history of breast cancer or surgery. Agreement was assessed of three mammographic techniques (Single-energy X-ray Absorptiometry [SXA], Quantra, and Volpara) with MRI for percent fibroglandular tissue volume, absolute fibroglandular tissue volume, and total breast volume.
Among 99 women, the automated mammographic density techniques were correlated with MRI measures with R(2) values ranging from 0.40 (log fibroglandular volume) to 0.91 (total breast volume). Substantial agreement measured by kappa statistic was found between all percent fibroglandular tissue measures (0.72 to 0.63), but only moderate agreement for log fibroglandular volumes. The kappa statistics for all percent density measures were highest in the comparisons of the SXA and MRI results. The largest error source between MRI and the mammography techniques was found to be differences in measures of total breast volume.
Automated volumetric fibroglandular tissue measures from screening digital mammograms were in substantial agreement with MRI and if associated with breast cancer could be used in clinical practice to enhance risk assessment and prevention.
PLoS ONE 12/2013; 8(12):e81653. · 3.53 Impact Factor