Breast Density Measurement Research Articles

Inverse association between estrogen receptor-\u03b1 DNA methylation and breast composition in adolescent Chilean girls

BackgroundEstrogen receptor-α (ER-α) is a transcriptional regulator, which mediates estrogen-dependent breast development, as well as breast tumorigenesis. The influence of epigenetic regulation of ER-α on adolescent breast composition has not been previously studied and could serve as a marker of pubertal health and susceptibility to breast cancer. We investigated the association between ER-α DNA methylation in leukocytes and breast composition in adolescent Chilean girls enrolled in the Growth and Obesity Cohort Study (GOCS) in Santiago, Chile. Breast composition (total breast volume (BV; cm3), fibroglandular volume (FGV; cm3), and percent fibroglandular volume (%FGV)) was measured at breast Tanner stage 4 (B4). ER-α promoter DNA methylation was assessed by pyrosequencing in blood samples collected at breast Tanner stages 2 (B2; n = 256) and B4 (n = 338).ResultsAfter adjusting for fat percentage at breast density measurement, ER-α methylation at B2, and cellular heterogeneity, we observed an inverse association between B4 average ER-α DNA methylation and BV and FGV. Geometric mean BV was 15% lower (95% CI: − 28%, − 1%) among girls in the highest quartile of B4 ER-α methylation (6.96–23.60%) relative to the lowest (0.78–3.37%). Similarly, FGV was 19% lower (95% CI: − 33%, − 2%) among girls in the highest quartile of B4 ER-α methylation relative to the lowest. The association between ER-α methylation and breast composition was not significantly modified by body fat percentage and was not influenced by pubertal timing.ConclusionsThese findings suggest that the methylation profile of ER-α may modulate adolescent response to estrogen and breast composition, which may influence breast cancer risk in adulthood.

Measuring breast density: Comparing computer-automated breast density quantification with an observer-based method in a South African academic context.

BackgroundDense breast tissue may not only ‘mask’ small, non-calcified cancers but also represents an independent risk factor for the development of breast cancer. Computer-automated breast density quantification (CABD) software tools have been developed for the calculation of volumetric breast density.ObjectivesThis study sought: (1) to compare observer-based breast density scores, using the fifth edition of the Breast Imaging Reporting and Data System (BI-RADS), with the breast density scores calculated using CABD quantification software tools, (2) to determine inter-reader variability in breast density scoring between qualified radiologists, between radiologists in training (registrars) and between these two groups and (3) to determine intra-reader reliability in breast density scoring.MethodsA cross-sectional study was performed using the data of 100 patients (200 breasts). Three qualified radiologists and three registrars were asked to review the mammograms in question and to assign a breast density score according to the fifth edition of the Breast Imaging Reporting and Data System (BI-RADS) reporting system. Two readings took place at a minimum of 30 days apart. The percentage agreement between the automated and observer-based scores was calculated and intra-reader and inter-reader reliability values were determined.ResultsThe study found that there was poor agreement between the breast densities calculated by CABD and the more subjective observer-based BI-RADS density scores. These results further reflect a statistically significant degree of inter-reader and intra-reader variability in the evaluation of breast density.ConclusionWe conclude that the use of automated breast density quantification (i.e. CABD) is a valuable tool for the reduction of variability in breast density ratings.

Medical breast ultrasound image segmentation by machine learning

Breast cancer is the most commonly diagnosed cancer, which alone accounts for 30% all new cancer diagnoses for women, posing a threat to women’s health. Segmentation of breast ultrasound images into functional tissues can aid tumor localization, breast density measurement, and assessment of treatment response, which is important to the clinical diagnosis of breast cancer. However, manually segmenting the ultrasound images, which is skill and experience dependent, would lead to a subjective diagnosis; in addition, it is time-consuming for radiologists to review hundreds of clinical images. Therefore, automatic segmentation of breast ultrasound images into functional tissues has received attention in recent years, amidst the more numerous studies of detection and segmentation of masses. In this paper, we propose to use convolutional neural networks (CNNs) for segmenting breast ultrasound images into four major tissues: skin, fibroglandular tissue, mass, and fatty tissue, on three-dimensional (3D) breast ultrasound images. Quantitative metrics for evaluation of segmentation results including Accuracy, Precision, Recall, and F1measure, all reached over 80%, which indicates that the method proposed has the capacity to distinguish functional tissues in breast ultrasound images. Another metric called the Jaccard similarity index (JSI) yields an 85.1% value, outperforming our previous study using the watershed algorithm with 74.54% JSI value. Thus, our proposed method might have the potential to provide the segmentations necessary to assist the clinical diagnosis of breast cancer and improve imaging in other modes in medical ultrasound.

Interactions of alcohol and postmenopausal hormone use in regards to mammographic breast density

We investigated the association of alcohol intake with mammographic breast density in postmenopausal women by their hormone therapy (HT) status. This study included 2,100 cancer-free postmenopausal women within the Nurses' Health Study and Nurses' Health Study II cohorts. Percent breast density (PD), absolute dense (DA), and non-dense areas (NDA) were measured from digitized film mammograms using a computer-assisted thresholding technique; all measures were square root transformed. Alcohol consumption was assessed with a food frequency questionnaire (0, < 5, and ≥ 5g/day). Information regarding breast cancer risk factors was obtained from baseline or biennial questionnaires closest to the mammogram date. We used generalized linear regression to examine associations between alcohol and breast density measures in women with no HT history, current, and past HT users. In multivariable analyses, we found no associations of alcohol consumption with PD (p trend = 0.32) and DA (p trend = 0.53) and an inverse association with NDA (β = - 0.41, 95% CI - 0.73, - 0.09 for ≥ 5g/day, p trend < 0.01). In the stratified analysis by HT status, alcohol was not associated with PD in any of the strata. We found a significant inverse association of alcohol with NDA among past HT users (β = - 0.79, 95% CI - 1.51, - 0.07 for ≥ 5g/day, p trend = 0.02). There were no significant interactions between alcohol and HT in relation to PD, DA, and NDA (p interaction = 0.19, 0.42, and 0.43, respectively). Our findings suggest that associations of alcohol with breast density do not vary by HT status.

The combined effect of mammographic texture and density on breast cancer risk: a cohort study

BackgroundTexture patterns have been shown to improve breast cancer risk segregation in addition to area-based mammographic density. The additional value of texture pattern scores on top of volumetric mammographic density measures in a large screening cohort has never been studied.MethodsVolumetric mammographic density and texture pattern scores were assessed automatically for the first available digital mammography (DM) screening examination of 51,400 women (50–75 years of age) participating in the Dutch biennial breast cancer screening program between 2003 and 2011. The texture assessment method was developed in a previous study and validated in the current study. Breast cancer information was obtained from the screening registration system and through linkage with the Netherlands Cancer Registry. All screen-detected breast cancers diagnosed at the first available digital screening examination were excluded. During a median follow-up period of 4.2 (interquartile range (IQR) 2.0–6.2) years, 301 women were diagnosed with breast cancer. The associations between texture pattern scores, volumetric breast density measures and breast cancer risk were determined using Cox proportional hazard analyses. Discriminatory performance was assessed using c-indices.ResultsThe median age of the women at the time of the first available digital mammography examination was 56 years (IQR 51–63). Texture pattern scores were positively associated with breast cancer risk (hazard ratio (HR) 3.16 (95% CI 2.16–4.62) (p value for trend <0.001), for quartile (Q) 4 compared to Q1). The c-index of texture was 0.61 (95% CI 0.57–0.64). Dense volume and percentage dense volume showed positive associations with breast cancer risk (HR 1.85 (95% CI 1.32–2.59) (p value for trend <0.001) and HR 2.17 (95% CI 1.51–3.12) (p value for trend <0.001), respectively, for Q4 compared to Q1). When adding texture measures to models with dense volume or percentage dense volume, c-indices increased from 0.56 (95% CI 0.53–0.59) to 0.62 (95% CI 0.58–0.65) (p < 0.001) and from 0.58 (95% CI 0.54–0.61) to 0.60 (95% CI 0.57–0.63) (p = 0.054), respectively.ConclusionsDeep-learning-based texture pattern scores, measured automatically on digital mammograms, are associated with breast cancer risk, independently of volumetric mammographic density, and augment the capacity to discriminate between future breast cancer and non-breast cancer cases.

Reproducible automated breast density measure with no ionizing radiation using fat-water decomposition MRI.

Increased breast density is a significant independent risk factor for breast cancer, and recent studies show that this risk is modifiable. Hence, breast density measures sensitive to small changes are desired. Utilizing fat-water decomposition MRI, we propose an automated, reproducible breast density measurement, which is nonionizing and directly comparable to mammographic density (MD). Retrospective study. The study included two sample sets of breast cancer patients enrolled in a clinical trial, for concordance analysis with MD (40 patients) and reproducibility analysis (10 patients). The majority of MRI scans (59 scans) were performed with a 1.5T GE Signa scanner using radial IDEAL-GRASE sequence, while the remaining (seven scans) were performed with a 3T Siemens Skyra using 3D Cartesian 6-echo GRE sequence with a similar fat-water separation technique. After automated breast segmentation, breast density was calculated using FraGW, a new measure developed to reliably reflect the amount of fibroglandular tissue and total water content in the entire breast. Based on its concordance with MD, FraGW was calibrated to MR-based breast density (MRD) to be comparable to MD. A previous breast density measurement, Fra80-the ratio of breast voxels with <80% fat fraction-was also calculated for comparison with FraGW. Pearson correlation was performed between MD (reference standard) and FraGW (and Fra80). Test-retest reproducibility of MRD was evaluated using the difference between test-retest measures (Δ1-2 ) and intraclass correlation coefficient (ICC). Both FraGW and Fra80 were strongly correlated with MD (Pearson ρ: 0.96 vs. 0.90, both P < 0.0001). MRD converted from FraGW showed higher test-retest reproducibility (Δ1-2 variation: 1.1% ± 1.2%; ICC: 0.99) compared to MD itself (literature intrareader ICC ≤0.96) and Fra80. The proposed MRD is directly comparable with MD and highly reproducible, which enables the early detection of small breast density changes and treatment response. 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;48:971-981.

MRI background parenchymal enhancement, breast density and serum hormones in postmenopausal women.

Background parenchymal enhancement (BPE) is the degree to which normal breast tissue enhances on contrast‐enhanced magnetic resonance imaging (MRI). MRI‐density is a volumetric measure of breast density that is highly correlated with mammographic density, an established breast cancer risk factor. Endogenous estrogen concentrations are positively associated with postmenopausal breast cancer risk and BPE has been shown to be sensitive to hormonal exposures. The objective of our study was to examine the relationship between BPE and MRI‐density and serum hormone concentrations in postmenopausal women. This was a study of cancer‐free postmenopausal women undergoing contrast‐enhanced breast MRI (N = 118). At the time of MRI all women completed a self‐administered questionnaire and blood samples were collected for hormone analyses. Serum concentrations of estrone (E1), estradiol (E2) and bioavailable E2 were examined by category of BPE and MRI‐density. Compared to women with “minimal” BPE, those who had “marked” BPE had significantly higher serum concentrations of E1, E2 and bioavailable E2 (90% increase, p trend across all categories = 0.001; 150% increase, p trend = 0.001; and 158% increase, p trend = 0.001, respectively). These associations were only affected to a minor extent by adjustment for BMI and other variables. After adjustment for BMI, no significant associations between MRI‐density and serum E1, E2 and bioavailable E2 were observed. Serum estrogen concentrations were significantly positively associated with BPE. Our study provides further evidence of the hormone‐sensitive nature of BPE, indicating a potential role for BPE as an imaging marker of endogenous and exogenous hormonal exposures in the breast.

Beyond BI-RADS Density: A Call for Quantification in the Breast Imaging Clinic.

Ultimately, the incorporation of automated quantitative measures of breast density will lead to more effective clinical care and more robust outcomes research than the current, subjective assignment of Breast Imaging and Reporting Data System density categories, by providing reproducible estimates of both the risk of masking a cancer as well as the risk of developing breast cancer—two important factors in determining personalized breast cancer screening algorithms.

Average glandular dose in paired digital mammography and digital breast tomosynthesis acquisitions in a population based screening program: effects of measuring breast density, air kerma and beam quality

The main purpose was to compare average glandular dose (AGD) for same-compression digital mammography (DM) and digital breast tomosynthesis (DBT) acquisitions in a population based screening program, with and without breast density stratification, as determined by automatically calculated breast density (Quantra™). Secondary, to compare AGD estimates based on measured breast density, air kerma and half value layer (HVL) to DICOM metadata based estimates. AGD was estimated for 3819 women participating in the screening trial. All received craniocaudal and mediolateral oblique views of each breasts with paired DM and DBT acquisitions. Exposure parameters were extracted from DICOM metadata. Air kerma and HVL were measured for all beam qualities used to acquire the mammograms. Volumetric breast density was estimated using Quantra™. AGD was estimated using the Dance model. AGD reported directly from the DICOM metadata was also assessed. Mean AGD was 1.74 and 2.10 mGy for DM and DBT, respectively. Mean DBT/DM AGD ratio was 1.24. For fatty breasts: mean AGD was 1.74 and 2.27 mGy for DM and DBT, respectively. For dense breasts: mean AGD was 1.73 and 1.79 mGy, for DM and DBT, respectively. For breasts of similar thickness, dense breasts had higher AGD for DM and similar AGD for DBT. The DBT/DM dose ratio was substantially lower for dense compared to fatty breasts (1.08 versus 1.33). The average c-factor was 1.16. Using previously published polynomials to estimate glandularity from thickness underestimated the c-factor by 5.9% on average. Mean AGD error between estimates based on measurements (air kerma and HVL) versus DICOM header data was 3.8%, but for one mammography unit as high as 7.9%. Mean error of using the AGD value reported in the DICOM header was 10.7 and 13.3%, respectively. Thus, measurement of breast density, radiation dose and beam quality can substantially affect AGD estimates.

Correlation Between Mammographic Breast Density, Breast Tissue Type in Ultrasonography, Fibroglandular Tissue, and Background Parenchymal Enhancement in Magnetic Resonance Imaging.

Objective:Breast cancer is the most common malignancy in the female population, and imaging studies play a critical role for its early detection. Mammographic breast density (MBD) is one of the markers used to predict the risk stratification of breast cancer in patients. We aimed to assess the correlations among MBD, ultrasound breast composition (USBC), fibroglandular tissue (FGT), and the amount of background parenchymal enhancement (BPE) in magnetic resonance imaging, after considering the subjects’ menopausal status.Methods:In this retrospective cross-sectional study, the medical records’ archives in a tertiary referral hospital were reviewed. Data including age, menopausal status, their mammograms, and ultrasound assessments were extracted from their records. All of their imaging studies were reviewed, and MBD, USBC, FGT, and BPE were determined, recorded, and entered into SPSS software for analysis.Results:A total of 121 women (mean age = 42.7 ± 11.0 years) were included, of which 35 out of 115 (30.4%) had reached menopause. Using the Jonckheere-Terpstra test for evaluating the trends among above mentioned 4 radiologic characteristics in the total sample population, a significant positive relation was found between each of these paired variables: (1) USBC-MBD (P = .006), (2) FGT-MBD (P = .001), (3) USBC-BPE (P = .046), (4) USBC-FGT (P = .036), and (5) BPE-FGT (P < .001). These trends were not found to be significant among premenopausal subjects.Conclusions:Considering the trends between different measures of breast density in the 3 radiologic modalities, these factors can be used interchangeably in certain settings.

Bridging the Data Gap in Breast Cancer Risk Assessment to Enable Widespread Clinical Implementation across the Multiethnic Landscape of the US.

Breast cancer remains the second leading cause of cancer death among women and is the most commonly diagnosed cancer in women. Breast cancer risk assessment has been clinically available for nearly 30 years yet is under-utilized in practice for multiple reasons. Incorporation of polygenic risk as well as breast density measurements, promise to increase the accuracy of risk assessment. With that comes the hope that both prevention and screening become more personalized and thus more effective. Incidence rates have been static over the past 15 years and have even increased slightly in African American and Asian/Pacific Islander populations despite the robust data on breast cancer risk reduction measures that exist. Current challenges in reducing breast cancer incidence begin with robust data curation that allows for appropriate risk stratification across our multiethnic population and conclude with the implementation of prevention strategies within our fractured healthcare system.

Evaluation of Quantra Hologic Volumetric Computerized Breast Density Software in Comparison With Manual Interpretation in a Diverse Population.

Objective:Increased mammographic breast density is a well-established risk factor for breast cancer development, regardless of age or ethnic background. The current gold standard for categorizing breast density consists of a radiologist estimation of percent density according to the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) criteria. This study compares paired qualitative interpretations of breast density on digital mammograms with quantitative measurement of density using Hologic’s Food and Drug Administration–approved R2 Quantra volumetric breast density assessment tool. Our goal was to find the best cutoff value of Quantra-calculated breast density for stratifying patients accurately into high-risk and low-risk breast density categories.Methods:Screening digital mammograms from 385 subjects, aged 18 to 64 years, were evaluated. These mammograms were interpreted by a radiologist using the ACR’s BI-RADS density method, and had quantitative density measured using the R2 Quantra breast density assessment tool. The appropriate cutoff for breast density–based risk stratification using Quantra software was calculated using manually determined BI-RADS scores as a gold standard, in which scores of D3/D4 denoted high-risk densities and D1/D2 denoted low-risk densities.Results:The best cutoff value for risk stratification using Quantra-calculated breast density was found to be 14.0%, yielding a sensitivity of 65%, specificity of 77%, and positive and negative predictive values of 75% and 69%, respectively. Under bootstrap analysis, the best cutoff value had a mean ± SD of 13.70% ± 0.89%.Conclusions:Our study is the first to publish on a North American population that assesses the accuracy of the R2 Quantra system at breast density stratification. Quantitative breast density measures will improve accuracy and reliability of density determination, assisting future researchers to accurately calculate breast cancer risks associated with density increase.

The ANDROMEDA prospective cohort study: predictive value of combined criteria to tailor breast cancer screening and new opportunities from circulating markers: study protocol

BackgroundIn recent years growing interest has been posed on alternative ways to screen women for breast cancer involving different imaging techniques or adjusting screening interval by breast cancer risk estimates. A new research area is studying circulating microRNAs as molecular biomarkers potentially useful for non invasive early detection together with the analysis of single-nucleotide polymorphisms (SNPs).The Andromeda study is a prospective cohort study on women attending breast cancer screening in a northern Italian area. The aims of the study are: 1) to define appropriate women risk-based stratifications for personalized screening considering different factors (reproductive, family and biopsy history, breast density, lifestyle habits); 2) to evaluate the diagnostic accuracy of selected circulating microRNAs in a case-control study nested within the above mentioned cohort.MethodsAbout 21,000 women aged 46–67 years compliant to screening mammography are expected to be enrolled. At enrolment, information on well-known breast cancer risk factors and life-styles habits are collected through self-admistered questionnaires. Information on breast density and anthropometric measurements (height, weight, body composition, and waist circumference) are recorded. In addition, women are requested to provide a blood sample for serum, plasma and buffy-coat storing for subsequent molecular analyses within the nested case-control study. This investigation will be performed on approximately 233 cases (screen-detected) and 699 matched controls to evaluate SNPs and circulating microRNAs. The whole study will last three years and the cohort will be followed up for ten years to observe the onset of new breast cancer cases.DiscussionNowadays women undergo the same screening protocol, independently of their breast density and their individual risk to develop breast cancer. New criteria to better stratify women in risk groups could enable the screening strategies to target high-risk women while reducing interventions in those at low-risk. In this frame the present study will contribute in identifying the feasibility and impact of implementing personalized breast cancer screening.Trial registrationNCT02618538 (retrospectively registered on 27–11-2015.)

Breast density quantification using structured-light-based diffuse optical tomography simulations.

We present the feasibility of structured-light-based diffuse optical tomography (DOT) to quantify the breast density with an extensive simulation study. This study is performed on multiple numerical breast phantoms built from magnetic resonance imaging (MRI) images. These phantoms represent realistic tissue morphologies and are given typical breast optical properties. First, synthetic data are simulated at five wavelengths using our structured-light-based DOT forward problem. Afterwards, the inverse problem is solved to obtain the absorption images and subsequently the chromophore concentration maps. Parameters, such as segmented volumes and mean concentrations, are extracted from these maps and used in a regression model to estimate the percent breast densities. These estimations are correlated with the true values from MRI, r=0.97, showing that our new technique is promising in measuring breast density.

Combining quantitative and qualitative breast density measures to assess breast cancer risk

Combining quantitative and qualitative breast density measures to assess breast cancer risk

Combining quantitative and qualitative breast density measures to assess breast cancer risk

BackgroundAccurately identifying women with dense breasts (Breast Imaging Reporting and Data System [BI-RADS] heterogeneously or extremely dense) who are at high breast cancer risk will facilitate discussions of supplemental imaging and primary prevention. We examined the independent contribution of dense breast volume and BI-RADS breast density to predict invasive breast cancer and whether dense breast volume combined with Breast Cancer Surveillance Consortium (BCSC) risk model factors (age, race/ethnicity, family history of breast cancer, history of breast biopsy, and BI-RADS breast density) improves identifying women with dense breasts at high breast cancer risk.MethodsWe conducted a case-control study of 1720 women with invasive cancer and 3686 control subjects. We calculated ORs and 95% CIs for the effect of BI-RADS breast density and Volpara™ automated dense breast volume on invasive cancer risk, adjusting for other BCSC risk model factors plus body mass index (BMI), and we compared C-statistics between models. We calculated BCSC 5-year breast cancer risk, incorporating the adjusted ORs associated with dense breast volume.ResultsCompared with women with BI-RADS scattered fibroglandular densities and second-quartile dense breast volume, women with BI-RADS extremely dense breasts and third- or fourth-quartile dense breast volume (75% of women with extremely dense breasts) had high breast cancer risk (OR 2.87, 95% CI 1.84–4.47, and OR 2.56, 95% CI 1.87–3.52, respectively), whereas women with extremely dense breasts and first- or second-quartile dense breast volume were not at significantly increased breast cancer risk (OR 1.53, 95% CI 0.75–3.09, and OR 1.50, 95% CI 0.82–2.73, respectively). Adding continuous dense breast volume to a model with BCSC risk model factors and BMI increased discriminatory accuracy compared with a model with only BCSC risk model factors (C-statistic 0.639, 95% CI 0.623–0.654, vs. C-statistic 0.614, 95% CI 0.598–0.630, respectively; P < 0.001). Women with dense breasts and fourth-quartile dense breast volume had a BCSC 5-year risk of 2.5%, whereas women with dense breasts and first-quartile dense breast volume had a 5-year risk ≤ 1.8%.ConclusionsRisk models with automated dense breast volume combined with BI-RADS breast density may better identify women with dense breasts at high breast cancer risk than risk models with either measure alone.

Automated Volumetric Mammographic Breast Density Measurements May Underestimate Percent Breast Density for High-density Breasts

The purpose of this study was to evaluate discrepancy in breast composition measurements obtained from mammograms using two commercially available software methods for systematic trends in overestimation or underestimation compared to magnetic resonance-derived measurements. An institutional review board-approved, Health Insurance Portability and Accountability Act-compliant retrospective study was performed to calculate percent breast density (PBD) by quantifying fibroglandular volume and total breast volume derived from magnetic resonance imaging (MRI) segmentation and mammograms using two commercially available software programs (Volpara and Quantra). Consecutive screening MRI exams from a 6-month period with negative or benign findings were used. The most recent mammogram within 9 months was used to derive mean density values from "for processing" images at the per breast level. Bland-Altman statistical analyses were performed to determine the mean discrepancy and the limits of agreement. A total of 110 women with 220 breasts met the study criteria. Overall, PBD was not different between MRI (mean 10%, range 1%-41%) and Volpara (mean 10%, range 3%-29%); a small but significant difference was present in the discrepancy between MRI and Quantra (4.0%, 95% CI: 2.9 to 5.0, P < 0.001). Discrepancy was highest at higher breast densities, with Volpara slightly underestimating and Quantra slightly overestimating PBD compared to MRI. The mean discrepancy for both Volpara and Quantra for total breast volume was not significantly different from MRI (p = 0.89, 0.35, respectively). Volpara tended to underestimate, whereas Quantra tended to overestimate fibroglandular volume, with the highest discrepancy at higher breast volumes. Both Volpara and Quantra tend to underestimate PBD, which is most pronounced at higher densities. PBD can be accurately measured using automated volumetric software programs, but values should not be used interchangeably between vendors.

Research in Digital Mammography and Tomosynthesis at The University of Toronto

There have been major advances in the field of breast cancer imaging since the early 1970s, both in technological improvements and in the use of the methods of medical physics and image analysis to optimize image quality. The introduction of digital mammography in 2000 provided a marked improvement in imaging of dense breasts. In addition, it became possible to produce tomographic and functional images on modified digital mammography systems. Digital imaging also greatly facilitated the extraction of quantitative information from images. My laboratory has been fortunate in being able to participate in some of these exciting developments. I will highlight some of the areas of our research interest which include modeling of the image formation process, development of high-resolution X-ray detectors for digital mammography and investigating new methods for analyzing image quality. I will also describe our more recent work on developing new applications of digital mammography including tomosynthesis, contrast-enhanced mammography, and measurement of breast density. Finally, I will point to a new area for our research—the application of the techniques of medical imaging to making pathology more quantitative to contribute to use of biomarkers for better characterizing breast cancer and directing therapeutic decisions.

Assessing tumor extent on contrast-enhanced spectral mammography versus full-field digital mammography and ultrasound

ObjectivesTo compare breast cancer size measurements on full-field digital mammography (FFDM), contrast-enhanced spectral mammography (CEDM), and ultrasound (US), with histologic tumor size used as the reference standard.Material and methodsThe HIPAA complaint, IRB approved study comprised 88 women with newly diagnosed breast cancer who underwent FFDM and CEDM;74 also had US. Breast density, histologic subtype, and maximum tumor measurements were recorded. ResultsPearson correlation coefficients for FFDM, US, and CEDM vs histopathology were 0.598, 0.639, and 0.859, respectively (P<0.001). The following correlation coefficients were calculated for dense breasts (n=48): histopathology vs FFDM (0.555), US (0.633), and CEDM (0.843) (P<0.001); for nondense breasts (n=40), they were FFDM (0.618), US (0.512), and CEDM (0.885) (P<0.001). For size difference, the mean (SD) for histopathology vs FFDM, US, and CEDM was −1.3 (11.9) mm, −2.8 (11.1) mm, and 2.9 (9.5) mm, respectively. Limits of agreement were −24.8 to 22.0mm, −24.5 to 18.8mm, and −15.6 to 21.4mm, respectively. ConclusionsIn patients with biopsy-proven malignancy, size measurements correlated well with histopathologic size, and were higher on CEDM than those for FFDM and US in patients with dense or nondense breasts. The added value of CEDM as a supplement to FFDM in determining tumor size, however, was greater in patients with dense breasts. CEDM may be a promising alternative preoperative measurement tool for breast cancer patients with dense breasts and/or limited access or contraindications to MRI.

Abstract 4255: Associations of coffee consumption and caffeine intake with mammographic breast density

Abstract Purpose: Previous studies suggest that coffee consumption and caffeine intake may be associated with reduced breast cancer risk which may result from stimulating effect of coffee on estrogen metabolism enzymes and subsequent changes in bioavailable estrogen. We investigated the association of coffee consumption and caffeine intake with mammographic density by woman’s menopausal status and, in postmenopausal women, by hormone therapy (HT). Methods: This study included 4,130 cancer-free women within the Nurses’ Health Study and Nurses’ Health Study II cohorts. Percent breast density (PD), absolute dense and non-dense areas were measured from digitized film mammograms using a computer-assisted thresholding technique; all measures were square-root transformed for the analysis to improve normality. Diet was assessed with a food frequency questionnaire; average cumulative coffee/caffeine consumption was calculated using data available from all the questionnaires preceding the mammogram date. Information regarding breast cancer risk factors was obtained from baseline or biennial questionnaires closest to the mammogram date. We used generalized linear regression to quantify associations between exposures and breast density measures, adjusted for potential confounders. Associations were examined separately for regular coffee, decaffeinated coffee, total coffee, and energy-adjusted caffeine intake. Results: In multivariable analyses, decaffeinated coffee was positively associated with PD in premenopausal women (2+ cups/day: β=0.23, p-trend=0.03). In postmenopausal women, decaffeinated and total coffee were inversely associated with PD (decaffeinated coffee 2+ cups/day: β=-0.24, p-trend=0.04; total coffee 4+ cups/day: β=-0.17). These associations appeared to be driven by the inverse associations of these exposures with absolute dense area. Among current HT users (n=980), regular coffee and caffeine intake were inversely associated with PD (regular coffee 4+ cups/day: β=-0.29, p-trend&amp;lt;0.01; caffeine 4th vs. 1st quartile: β=-0.32, p-trend&amp;lt;0.01). Among past users (n=430), decaffeinated coffee was inversely associated with PD (2+ cups/day β=-0.70, p-trend=0.02). Conclusions: Our findings suggest that among current HT users, regular coffee and caffeine are inversely associated with mammographic percent breast density. Citation Format: Lusine Yaghjyan, Graham Colditz, Bernard Rosner, Aleksandra Gasparova, Rulla Tamimi. Associations of coffee consumption and caffeine intake with mammographic breast density [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4255. doi:10.1158/1538-7445.AM2017-4255