Comparison of percent density from raw and processed full-field digital mammography data

Mammographic breast density and the risk of breast cancer: A systematic review and meta-analysis

OBJECTIVES

Mammographic density is a well-defined risk factor for breast cancer and having extremely dense breast tissue is associated with a one-to six-fold increased risk of breast cancer. However, it is questioned whether this increased risk estimate is applicable to current breast density classification methods. Therefore, the aim of this study was to further investigate and clarify the association between mammographic density and breast cancer risk based on current literature.

METHODS

Medline, Embase and Web of Science were systematically searched for articles published since 2013, that used BI-RADS lexicon 5th edition and incorporated data on digital mammography. Crude and maximally confounder-adjusted data were pooled in odds ratios (ORs) using random-effects models. Heterogeneity regarding breast cancer risks were investigated using I² statistic, stratified and sensitivity analyses.

RESULTS

Nine observational studies were included. Having extremely dense breast tissue (BI-RADS density D) resulted in a 2.11-fold (95% CI 1.84-2.42) increased breast cancer risk compared to having scattered dense breast tissue (BI-RADS density B). Sensitivity analysis showed that when only using data that had adjusted for age and BMI, the breast cancer risk was 1.83-fold (95% CI 1.52-2.21) increased. Both results were statistically significant and homogenous.

CONCLUSIONS

Mammographic breast density BI-RADS D is associated with an approximately two-fold increased risk of breast cancer compared to having BI-RADS density B in general population women. This is a novel and lower risk estimate compared to previously reported and might be explained due to the use of digital mammography and BI-RADS lexicon 5th edition.

Automated percent mammographic density, mammographic texture variation, and risk of breast cancer: a nested case-control study

Percent mammographic density (PMD) is a strong breast cancer risk factor, however, other mammographic features, such as V, the standard deviation (SD) of pixel intensity, may be associated with risk. We assessed whether PMD, automated PMD (APD), and V, yielded independent associations with breast cancer risk. We included 1900 breast cancer cases and 3921 matched controls from the Nurses' Health Study (NHS) and the NHSII. Using digitized film mammograms, we estimated PMD using a computer-assisted thresholding technique. APD and V were determined using an automated computer algorithm. We used logistic regression to generate odds ratios (ORs) and 95% confidence intervals (CIs). Median time from mammogram to diagnosis was 4.1 years (interquartile range: 1.6-6.8 years). PMD (OR per SD:1.52, 95% CI: 1.42, 1.63), APD (OR per SD:1.32, 95% CI: 1.24, 1.41), and V (OR per SD:1.32, 95% CI: 1.24, 1.40) were positively associated with breast cancer risk. Associations for APD were attenuated but remained statistically significant after mutual adjustment for PMD or V. Women in the highest quartile of both APD and V (OR vs Q1/Q1: 2.49, 95% CI: 2.02, 3.06), or PMD and V (OR vs Q1/Q1: 3.57, 95% CI: 2.79, 4.58) had increased breast cancer risk. An automated method of PMD assessment is feasible and yields similar, but somewhat weaker, estimates to a manual measure. PMD, APD and V are each independently, positively associated with breast cancer risk. Women with dense breasts and greater texture variation are at the highest relative risk of breast cancer.

Evaluation of LIBRA Software for Fully Automated Mammographic Density Assessment in Breast Cancer Risk Prediction

Background The associations of density measures from the publicly available Laboratory for Individualized Breast Radiodensity Assessment (LIBRA) software with breast cancer have primarily focused on estimates from the contralateral breast at the time of diagnosis. Purpose To evaluate LIBRA measures on mammograms obtained before breast cancer diagnosis and compare their performance to established density measures. Materials and Methods For this retrospective case-control study, full-field digital mammograms in for-processing (raw) and for-presentation (processed) formats were obtained (March 2008 to December 2011) in women who developed breast cancer an average of 2 years later and in age-matched control patients. LIBRA measures included absolute dense area and area percent density (PD) from both image formats. For comparison, dense area and PD were assessed by using the research software (Cumulus), and volumetric PD (VPD) and absolute dense volume were estimated with a commercially available software (Volpara). Density measures were compared by using Spearman correlation coefficients (r), and conditional logistic regression (odds ratios [ORs] and 95% confidence intervals [CIs]) was performed to examine the associations of density measures with breast cancer by adjusting for age and body mass index. Results Evaluated were 437 women diagnosed with breast cancer (median age, 62 years ± 17 [standard deviation]) and 1225 matched control patients (median age, 61 years ± 16). LIBRA PD showed strong correlations with Cumulus PD (r = 0.77-0.84) and Volpara VPD (r = 0.85-0.90) (P < .001 for both). For LIBRA, the strongest breast cancer association was observed for PD from processed images (OR, 1.3; 95% CI: 1.1, 1.5), although the PD association from raw images was not significantly different (OR, 1.2; 95% CI: 1.1, 1.4; P = .25). Slightly stronger breast cancer associations were seen for Cumulus PD (OR, 1.5; 95% CI: 1.3, 1.8; processed images; P = .01) and Volpara VPD (OR, 1.4; 95% CI: 1.2, 1.7; raw images; P = .004) compared with LIBRA measures. Conclusion Automated density measures provided by the Laboratory for Individualized Breast Radiodensity Assessment from raw and processed mammograms correlated with established area and volumetric density measures and showed comparable breast cancer associations. © RSNA, 2020 Online supplemental material is available for this article.

Calibrated Breast Density Measurements

RATIONALE AND OBJECTIVES

Mammographic density is an important risk factor for breast cancer, but translation to the clinic requires assurance that prior work based on mammography is applicable to current technologies. The purpose of this work is to evaluate whether a calibration methodology developed previously produces breast density metrics predictive of breast cancer risk when applied to a case-control study.

MATERIALS AND METHODS

A matched case control study (n = 319 pairs) was used to evaluate two calibrated measures of breast density. Two-dimensional mammograms were acquired from six Hologic mammography units: three conventional Selenia two-dimensional full-field digital mammography systems and three Dimensions digital breast tomosynthesis systems. We evaluated the capability of two calibrated breast density measures to quantify breast cancer risk: the mean (PGm) and standard deviation (PGsd) of the calibrated pixels. Matching variables included age, hormone replacement therapy usage/duration, screening history, and mammography unit. Calibrated measures were compared to the percentage of breast density (PD) determined with the operator-assisted Cumulus method. Conditional logistic regression was used to generate odds ratios (ORs) from continuous and quartile (Q) models with 95% confidence intervals. The area under the receiver operating characteristic curve (Az) was also used as a comparison metric. Both univariate models and models adjusted for body mass index and ethnicity were evaluated.

RESULTS

In adjusted models, both PGsd and PD were statistically significantly associated with breast cancer with similar Az of 0.61-0.62. The corresponding ORs and confidence intervals were also similar. For PGsd, the OR was 1.34 (1.09, 1.66) for the continuous measure and 1.83 (1.11, 3.02), 2.19 (1.28, 3.73), and 2.20 (1.26, 3.85) for Q2-Q4. For PD, the OR was 1.43 (1.16, 1.76) for the continuous measure and 0.84 (0.52, 1.38), 1.96 (1.19, 3.23), and 2.27 (1.29, 4.00) for Q2-Q4. The results for PGm were slightly attenuated and not statistically significant. The OR was 1.22 (0.99, 1.51) with Az = 0.60 for the continuous measure and 1.24 (0.78, 1.97), 0.98 (0.60, 1.61), and 1.26, (0.77, 2.07) for Q2-Q4 with Az = 0.60.

CONCLUSION

The calibrated PGsd measure provided significant associations with breast cancer comparable to those given by PD. The calibrated PGm performed slightly worse. These findings indicate that the calibration approach developed previously replicates under more general conditions.

Reproductive Factors and Mammographic Density: Associations Among 24,840 Women and Comparison of Studies Using Digitized Film-Screen Mammography and Full-Field Digital Mammography

Breast density is a modifiable factor that is strongly associated with breast cancer risk. We sought to understand the influence of newer technologies of full-field digital mammography (FFDM) on breast density research and to determine whether results are comparable across studies using FFDM and previous studies using traditional film-screen mammography. We studied 24,840 screening-age (40-74 years) non-Hispanic white women who were participants in the Research Program on Genes, Environment and Health of Kaiser Permanente Northern California and underwent screening mammography with either Hologic (Hologic, Inc., Marlborough, Massachusetts) or General Electric (General Electric Company, Boston, Massachusetts) FFDM machines between 2003 and 2013. We estimated the associations of parity, age at first birth, age at menarche, and menopausal status with percent density and dense area as measured by a single radiological technologist using Cumulus software (Canto Software, Inc., San Francisco, California). We found that associations between reproductive factors and mammographic density measured using processed FFDM images were generally similar in magnitude and direction to those from prior studies using film mammography. Estimated associations for both types of FFDM machines were in the same direction. There was some evidence of heterogeneity in the magnitude of the effect sizes by machine type, which we accounted for using random-effects meta-analysis when combining results. Our findings demonstrate the robustness of quantitative mammographic density measurements across FFDM and film mammography platforms.

Generalized breast density metrics

Mammograms represent data that can inform future risk of breast cancer. Data from two case-control study populations were analyzed. Population 1 included women (N  =  180 age matched case-control pairs) with mammograms acquired with one indirect x-ray conversion mammography unit. Population 2 included women (N  =  319 age matched case-control pairs) with mammograms acquired from 6 direct x-ray conversion units. The Fourier domain was decomposed into n concentric rings (radial spatial frequency bands). The power in each ring was summarized giving a set of measures. We investigated images in raw, for presentation (processed) and calibrated representations and made comparison with the percentage of breast density (BD) determined with the operator assisted Cumulus method. Breast cancer associations were evaluated with conditional logistic regression, adjusted for body mass index and ethnicity. Odds ratios (ORs), per standard deviation increase derived from the respective breast density distributions and 95% confidence intervals (CIs) were estimated. A measure from a lower radial frequency ring, corresponding 0.083-0.166 cycles mm^-1 and BD had significant associations with risk in both populations. In Population 1, the Fourier measure produced significant associations in each representation: OR  =  1.76 (1.33, 2.32) for raw; OR  =  1.43 (1.09, 1.87) for processed; and OR  =  1.68 (1.26, 2.25) for calibrated. BD also provided significant associations in Population 1: OR  =  1.72 (1.27, 2.33). In Population 2, the Fourier measure produced significant associations for each representation as well: OR  =  1.47 (1.19, 1.80) for raw; OR  =  1.38 (1.15, 1.67) for processed; and OR  =  1.42 (1.15, 1.75) for calibrated. BD provided significant associations in Population 2: OR  =  1.43 (1.17, 1.76). Other coincident spectral regions were also predictive of case-control status. In sum, generalized breast density measures were significantly associated with breast cancer in both FFDM technologies.

Comparison of subjective and fully automated methods for measuring mammographic density

Background Breast radiologists of the Norwegian Breast Cancer Screening Program subjectively classified mammographic density using a three-point scale between 1996 and 2012 and changed into the fourth edition of the BI-RADS classification since 2013. In 2015, an automated volumetric breast density assessment software was installed at two screening units. Purpose To compare volumetric breast density measurements from the automated method with two subjective methods: the three-point scale and the BI-RADS density classification. Material and Methods Information on subjective and automated density assessment was obtained from screening examinations of 3635 women recalled for further assessment due to positive screening mammography between 2007 and 2015. The score of the three-point scale (I = fatty; II = medium dense; III = dense) was available for 2310 women. The BI-RADS density score was provided for 1325 women. Mean volumetric breast density was estimated for each category of the subjective classifications. The automated software assigned volumetric breast density to four categories. The agreement between BI-RADS and volumetric breast density categories was assessed using weighted kappa (k_w). Results Mean volumetric breast density was 4.5%, 7.5%, and 13.4% for categories I, II, and III of the three-point scale, respectively, and 4.4%, 7.5%, 9.9%, and 13.9% for the BI-RADS density categories, respectively ( P for trend < 0.001 for both subjective classifications). The agreement between BI-RADS and volumetric breast density categories was k_w = 0.5 (95% CI = 0.47-0.53; P < 0.001). Conclusion Mean values of volumetric breast density increased with increasing density category of the subjective classifications. The agreement between BI-RADS and volumetric breast density categories was moderate.

Age at Menarche and Late Adolescent Adiposity Associated with Mammographic Density on Processed Digital Mammograms in 24,840 Women

Background: High mammographic density is strongly associated with increased breast cancer risk. Some, but not all, risk factors for breast cancer are also associated with higher mammographic density.Methods: The study cohort (N = 24,840) was drawn from the Research Program in Genes, Environment and Health of Kaiser Permanente Northern California and included non-Hispanic white females ages 40 to 74 years with a full-field digital mammogram (FFDM). Percent density (PD) and dense area (DA) were measured by a radiological technologist using Cumulus. The association of age at menarche and late adolescent body mass index (BMI) with PD and DA were modeled using linear regression adjusted for confounders.Results: Age at menarche and late adolescent BMI were negatively correlated. Age at menarche was positively associated with PD (P value for trend <0.0001) and DA (P value for trend <0.0001) in fully adjusted models. Compared with the reference category of ages 12 to 13 years at menarche, menarche at age >16 years was associated with an increase in PD of 1.47% (95% CI, 0.69-2.25) and an increase in DA of 1.59 cm² (95% CI, 0.48-2.70). Late adolescent BMI was inversely associated with PD (P < 0.0001) and DA (P < 0.0001) in fully adjusted models.Conclusions: Age at menarche and late adolescent BMI are both associated with Cumulus measures of mammographic density on processed FFDM images.Impact: Age at menarche and late adolescent BMI may act through different pathways. The long-term effects of age at menarche on cancer risk may be mediated through factors besides mammographic density. Cancer Epidemiol Biomarkers Prev; 26(9); 1450-8. ©2017 AACR.

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 (r_G) and breast PD (r_PD), 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, p_w < 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 ≤ r_G ≤ 0.2), weakly to moderately associated with breast PD (-0.6 ≤ r_PD ≤ 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.

Mammographic density assessed on paired raw and processed digital images and on paired screen-film and digital images across three mammography systems

BACKGROUND

Inter-women and intra-women comparisons of mammographic density (MD) are needed in research, clinical and screening applications; however, MD measurements are influenced by mammography modality (screen film/digital) and digital image format (raw/processed). We aimed to examine differences in MD assessed on these image types.

METHODS

We obtained 1294 pairs of images saved in both raw and processed formats from Hologic and General Electric (GE) direct digital systems and a Fuji computed radiography (CR) system, and 128 screen-film and processed CR-digital pairs from consecutive screening rounds. Four readers performed Cumulus-based MD measurements (n = 3441), with each image pair read by the same reader. Multi-level models of square-root percent MD were fitted, with a random intercept for woman, to estimate processed-raw MD differences.

RESULTS

Breast area did not differ in processed images compared with that in raw images, but the percent MD was higher, due to a larger dense area (median 28.5 and 25.4 cm2 respectively, mean √dense area difference 0.44 cm (95% CI: 0.36, 0.52)). This difference in √dense area was significant for direct digital systems (Hologic 0.50 cm (95% CI: 0.39, 0.61), GE 0.56 cm (95% CI: 0.42, 0.69)) but not for Fuji CR (0.06 cm (95% CI: -0.10, 0.23)). Additionally, within each system, reader-specific differences varied in magnitude and direction (p < 0.001). Conversion equations revealed differences converged to zero with increasing dense area. MD differences between screen-film and processed digital on the subsequent screening round were consistent with expected time-related MD declines.

CONCLUSIONS

MD was slightly higher when measured on processed than on raw direct digital mammograms. Comparisons of MD on these image formats should ideally control for this non-constant and reader-specific difference.

Pre-natal exposures and breast tissue composition: findings from a British pre-birth cohort of young women and a systematic review

BACKGROUND

Breast density, the amount of fibroglandular tissue in the adult breast for a women's age and body mass index, is a strong biomarker of susceptibility to breast cancer, which may, like breast cancer risk itself, be influenced by events early in life. In the present study, we investigated the association between pre-natal exposures and breast tissue composition.

METHODS

A sample of 500 young, nulliparous women (aged approximately 21 years) from a U.K. pre-birth cohort underwent a magnetic resonance imaging examination of their breasts to estimate percent water, a measure of the relative amount of fibroglandular tissue equivalent to mammographic percent density. Information on pre-natal exposures was collected throughout the mothers' pregnancy and shortly after delivery. Regression models were used to investigate associations between percent water and pre-natal exposures. Mediation analysis, and a systematic review and meta-analysis of the published literature, were also conducted.

RESULTS

Adjusted percent water in young women was positively associated with maternal height (p for linear trend [p t] = 0.005), maternal mammographic density in middle age (p t = 0.018) and the participant's birth size (p t < 0.001 for birthweight). A 1-SD increment in weight (473 g), length (2.3 cm), head circumference (1.2 cm) and Ponderal Index (4.1 g/cm3) at birth were associated with 3 % (95 % CI 2-5 %), 2 % (95 % CI 0-3 %), 3 % (95 % CI 1-4 %) and 1 % (95 % CI 0-3 %), respectively, increases in mean adjusted percent water. The effect of maternal height on the participants' percent water was partly mediated through birth size, but there was little evidence that the effect of birthweight was primarily mediated via adult body size. The meta-analysis supported the study findings, with breast density being positively associated with birth size.

CONCLUSIONS

These findings provide strong evidence of pre-natal influences on breast tissue composition. The positive association between birth size and relative amount of fibroglandular tissue indicates that breast density and breast cancer risk may share a common pre-natal origin.

Impact of type of full-field digital image on mammographic density assessment and breast cancer risk estimation: a case-control study

Background

Full-field digital mammography, which is gradually being introduced in most clinical and screening settings, produces two types of images: raw and processed. However, the extent to which mammographic density measurements, and their ability to predict breast cancer risk, vary according to type of image is not fully known.

Methods

We compared the performance of the semi-automated Cumulus method on digital raw, “analogue-like” raw and processed images, and the performance of a recently developed method - Laboratory for Breast Radiodensity Assessment (LIBRA) - on digital raw and processed images, in a case-control study (414 patients (cases) and 684 controls) by evaluating the extent to which their measurements were associated with breast cancer risk factors, and by comparing their ability to predict breast cancer risk.

Results

Valid Cumulus and LIBRA measurements were obtained from all available images, but the resulting distributions differed according to the method and type of image used. Both Cumulus and LIBRA percent density were inversely associated with age, body mass index (BMI), parity and postmenopausal status, regardless of type of image used. Cumulus percent density was strongly associated with breast cancer risk, but with the magnitude of the association slightly stronger for processed (risk increase per one SD increase in percent density (95 % CI): 1.55 (1.29, 1.85)) and “analogue-like” raw (1.52 (1.28, 1.80)) than for raw (1.35 (1.14, 1.60)) images. LIBRA percent density produced weaker associations with risk, albeit stronger for processed (1.32 (1.08, 1.61)) than raw images (1.17 (0.99, 1.37)). The percent density values yielded by the various density assessment/type of image combinations had similar ability to discriminate between patients and controls (area under the receiving operating curve values for percent density, age, BMI, parity and menopausal status combined ranged from 0.61 and 0.64).

Conclusions

The findings showed that Cumulus can be used to measure density on all types of digital images. They also indicate that LIBRA may provide a valid fully automated alternative to the more labour-intensive Cumulus. However, the same digital image type and assessment method should be used when examining mammographic density across populations, or longitudinal changes in density within a single population.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-016-0756-7) contains supplementary material, which is available to authorized users.

Case-control study of mammographic density and breast cancer risk using processed digital mammograms

Background

Full-field digital mammography (FFDM) has largely replaced film-screen mammography in the US. Breast density assessed from film mammograms is strongly associated with breast cancer risk, but data are limited for processed FFDM images used for clinical care.

Methods

We conducted a case-control study nested among non-Hispanic white female participants of the Research Program in Genes, Environment and Health of Kaiser Permanente Northern California who were aged 40 to 74 years and had screening mammograms acquired on Hologic FFDM machines. Cases (n = 297) were women with a first invasive breast cancer diagnosed after a screening FFDM. For each case, up to five controls (n = 1149) were selected, matched on age and year of FFDM and image batch number, and who were still under follow-up and without a history of breast cancer at the age of diagnosis of the matched case. Percent density (PD) and dense area (DA) were assessed by a radiological technologist using Cumulus. Conditional logistic regression was used to estimate odds ratios (ORs) for breast cancer associated with PD and DA, modeled continuously in standard deviation (SD) increments and categorically in quintiles, after adjusting for body mass index, parity, first-degree family history of breast cancer, breast area, and menopausal hormone use.

Results

Median intra-reader reproducibility was high with a Pearson’s r of 0.956 (range 0.902 to 0.983) for replicate PD measurements across 23 image batches. The overall mean was 20.02 (SD, 14.61) for PD and 27.63 cm² (18.22 cm²) for DA. The adjusted ORs for breast cancer associated with each SD increment were 1.70 (95 % confidence interval, 1.41–2.04) for PD, and 1.54 (1.34–1.77) for DA. The adjusted ORs for each quintile were: 1.00 (ref.), 1.49 (0.91–2.45), 2.57 (1.54–4.30), 3.22 (1.91–5.43), 4.88 (2.78–8.55) for PD, and 1.00 (ref.), 1.43 (0.85–2.40), 2.53 (1.53–4.19), 2.85 (1.73–4.69), 3.48 (2.14–5.65) for DA.

Conclusions

PD and DA measured using Cumulus on processed FFDM images are positively associated with breast cancer risk, with similar magnitudes of association as previously reported for film-screen mammograms. Processed digital mammograms acquired for routine clinical care in a general practice setting are suitable for breast density and cancer research.

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.

Consistency of visual assessments of mammographic breast density from vendor-specific "for presentation" images

Discussions of percent breast density (PD) and breast cancer risk implicitly assume that visual assessments of PD are comparable between vendors despite differences in technology and display algorithms. This study examines the extent to which visual assessments of PD differ between mammograms acquired from two vendors. Pairs of "for presentation" digital mammography images were obtained from two mammography units for 146 women who had a screening mammogram on one vendor unit followed by a diagnostic mammogram on a different vendor unit. Four radiologists independently visually assessed PD from single left mediolateral oblique view images from the two vendors. Analysis of variance, intra-class correlation coefficients (ICC), scatter plots, and Bland-Altman plots were used to evaluate PD assessments between vendors. The mean radiologist PD for each image was used as a consensus PD measure. Overall agreement of the PD assessments was excellent between the two vendors with an ICC of 0.95 (95% confidence interval: 0.93 to 0.97). Bland-Altman plots demonstrated narrow upper and lower limits of agreement between the vendors with only a small bias (2.3 percentage points). The results of this study support the assumption that visual assessment of PD is consistent across mammography vendors despite vendor-specific appearances of "for presentation" images.

International Consortium on Mammographic Density: Methodology and population diversity captured across 22 countries

Mammographic density (MD) is a quantitative trait, measurable in all women, and is among the strongest markers of breast cancer risk. The population-based epidemiology of MD has revealed genetic, lifestyle and societal/environmental determinants, but studies have largely been conducted in women with similar westernized lifestyles living in countries with high breast cancer incidence rates. To benefit from the heterogeneity in risk factors and their combinations worldwide, we created an International Consortium on Mammographic Density (ICMD) to pool individual-level epidemiological and MD data from general population studies worldwide. ICMD aims to characterize determinants of MD more precisely, and to evaluate whether they are consistent across populations worldwide. We included 11755 women, from 27 studies in 22 countries, on whom individual-level risk factor data were pooled and original mammographic images were re-read for ICMD to obtain standardized comparable MD data. In the present article, we present (i) the rationale for this consortium; (ii) characteristics of the studies and women included; and (iii) study methodology to obtain comparable MD data from original re-read films. We also highlight the risk factor heterogeneity captured by such an effort and, thus, the unique insight the pooled study promises to offer through wider exposure ranges, different confounding structures and enhanced power for sub-group analyses.

Background parenchymal uptake during molecular breast imaging and associated clinical factors

OBJECTIVE. The purposes of this study were to describe the prevalence of background parenchymal uptake categories observed at screening molecular breast imaging (MBI) and to examine the association of background parenchymal uptake with mammographic density and other clinical factors. MATERIALS AND METHODS. Adjunct MBI screening was performed for women with dense breasts on previous mammograms. Two radiologists reviewed images from the MBI examinations and subjectively categorized background parenchymal uptake into four groups: photopenic, minimal-mild, moderate, or marked. Women with breast implants or a personal history of breast cancer were excluded. The association between background parenchymal uptake categories and patient characteristics was examined with Kruskal-Wallis and chi-square tests as appropriate. RESULTS. In 1149 eligible participants, background parenchymal uptake was photopenic in 252 (22%), minimal-mild in 728 (63%), and moderate or marked in 169 (15%). The distribution of categories differed across BI-RADS density categories (p < 0.0001). In 164 participants with extremely dense breasts, background parenchymal uptake was photopenic in 72 (44%), minimal-mild in 55 (34%), and moderate or marked in 37 (22%). The moderate-marked group was younger on average, more likely to be premenopausal or perimenopausal, and more likely to be using postmenopausal hormone therapy than the photopenic or minimal-mild groups (p < 0.0001). CONCLUSION. Among women with similar-appearing mammographic density, background parenchymal uptake ranged from photopenic to marked. Background parenchymal uptake was associated with menopausal status and postmenopausal hormone therapy but not with premenopausal hormonal contraceptives, phase of menstrual cycle, or Gail model 5-year risk of breast cancer. Additional work is necessary to fully characterize the underlying cause of background parenchymal uptake and determine its utility in predicting subsequent risk of breast cancer.

Area and volumetric density estimation in processed full-field digital mammograms for risk assessment of breast cancer

Introduction

Mammographic density, the white radiolucent part of a mammogram, is a marker of breast cancer risk and mammographic sensitivity. There are several means of measuring mammographic density, among which are area-based and volumetric-based approaches. Current volumetric methods use only unprocessed, raw mammograms, which is a problematic restriction since such raw mammograms are normally not stored. We describe fully automated methods for measuring both area and volumetric mammographic density from processed images.

Methods

The data set used in this study comprises raw and processed images of the same view from 1462 women. We developed two algorithms for processed images, an automated area-based approach (CASAM-Area) and a volumetric-based approach (CASAM-Vol). The latter method was based on training a random forest prediction model with image statistical features as predictors, against a volumetric measure, Volpara, for corresponding raw images. We contrast the three methods, CASAM-Area, CASAM-Vol and Volpara directly and in terms of association with breast cancer risk and a known genetic variant for mammographic density and breast cancer, rs10995190 in the gene ZNF365. Associations with breast cancer risk were evaluated using images from 47 breast cancer cases and 1011 control subjects. The genetic association analysis was based on 1011 control subjects.

Results

All three measures of mammographic density were associated with breast cancer risk and rs10995190 (p<0.025 for breast cancer risk and p<1×10⁻⁶ for rs10995190). After adjusting for one of the measures there remained little or no evidence of residual association with the remaining density measures (p>0.10 for risk, p>0.03 for rs10995190).

Conclusions

Our results show that it is possible to obtain reliable automated measures of volumetric and area mammographic density from processed digital images. Area and volumetric measures of density on processed digital images performed similar in terms of risk and genetic association.

Risk determination and prevention of breast cancer

Breast cancer is an increasing public health problem. Substantial advances have been made in the treatment of breast cancer, but the introduction of methods to predict women at elevated risk and prevent the disease has been less successful. Here, we summarize recent data on newer approaches to risk prediction, available approaches to prevention, how new approaches may be made, and the difficult problem of using what we already know to prevent breast cancer in populations. During 2012, the Breast Cancer Campaign facilitated a series of workshops, each covering a specialty area of breast cancer to identify gaps in our knowledge. The risk-and-prevention panel involved in this exercise was asked to expand and update its report and review recent relevant peer-reviewed literature. The enlarged position paper presented here highlights the key gaps in risk-and-prevention research that were identified, together with recommendations for action. The panel estimated from the relevant literature that potentially 50% of breast cancer could be prevented in the subgroup of women at high and moderate risk of breast cancer by using current chemoprevention (tamoxifen, raloxifene, exemestane, and anastrozole) and that, in all women, lifestyle measures, including weight control, exercise, and moderating alcohol intake, could reduce breast cancer risk by about 30%. Risk may be estimated by standard models potentially with the addition of, for example, mammographic density and appropriate single-nucleotide polymorphisms. This review expands on four areas: (a) the prediction of breast cancer risk, (b) the evidence for the effectiveness of preventive therapy and lifestyle approaches to prevention, (c) how understanding the biology of the breast may lead to new targets for prevention, and (d) a summary of published guidelines for preventive approaches and measures required for their implementation. We hope that efforts to fill these and other gaps will lead to considerable advances in our efforts to predict risk and prevent breast cancer over the next 10 years.

Breast Imaging Reporting and Data System (BI-RADS) breast composition descriptors: automated measurement development for full field digital mammography

PURPOSE

The Breast Imaging Reporting and Data System (BI-RADS) breast composition descriptors are used for standardized mammographic reporting and are assessed visually. This reporting is clinically relevant because breast composition can impact mammographic sensitivity and is a breast cancer risk factor. New techniques are presented and evaluated for generating automated BI-RADS breast composition descriptors using both raw and calibrated full field digital mammography (FFDM) image data.

METHODS

A matched case-control dataset with FFDM images was used to develop three automated measures for the BI-RADS breast composition descriptors. Histograms of each calibrated mammogram in the percent glandular (pg) representation were processed to create the new BR(pg) measure. Two previously validated measures of breast density derived from calibrated and raw mammograms were converted to the new BR(vc) and BR(vr) measures, respectively. These three measures were compared with the radiologist-reported BI-RADS compositions assessments from the patient records. The authors used two optimization strategies with differential evolution to create these measures: method-1 used breast cancer status; and method-2 matched the reported BI-RADS descriptors. Weighted kappa (κ) analysis was used to assess the agreement between the new measures and the reported measures. Each measure's association with breast cancer was evaluated with odds ratios (ORs) adjusted for body mass index, breast area, and menopausal status. ORs were estimated as per unit increase with 95% confidence intervals.

RESULTS

The three BI-RADS measures generated by method-1 had κ between 0.25-0.34. These measures were significantly associated with breast cancer status in the adjusted models: (a) OR = 1.87 (1.34, 2.59) for BR(pg); (b) OR = 1.93 (1.36, 2.74) for BR(vc); and (c) OR = 1.37 (1.05, 1.80) for BR(vr). The measures generated by method-2 had κ between 0.42-0.45. Two of these measures were significantly associated with breast cancer status in the adjusted models: (a) OR = 1.95 (1.24, 3.09) for BR(pg); (b) OR = 1.42 (0.87, 2.32) for BR(vc); and (c) OR = 2.13 (1.22, 3.72) for BR(vr). The radiologist-reported measures from the patient records showed a similar association, OR = 1.49 (0.99, 2.24), although only borderline statistically significant.

CONCLUSIONS

A general framework was developed and validated for converting calibrated mammograms and continuous measures of breast density to fully automated approximations for the BI-RADS breast composition descriptors. The techniques are general and suitable for a broad range of clinical and research applications.

Automated Percentage of Breast Density Measurements for Full-field Digital Mammography Applications

RATIONALE AND OBJECTIVES

Increased mammographic breast density is a significant risk factor for breast cancer. A reproducible, accurate, and automated breast density measurement is required for full-field digital mammography (FFDM) to support clinical applications. We evaluated a novel automated percentage of breast density measure (PDa) and made comparisons with the standard operator-assisted measure (PD) using FFDM data.

METHODS

We used a nested breast cancer case-control study matched on age, year of mammogram and diagnosis with images acquired from a specific direct x-ray conversion FFDM technology. PDa was applied to the raw and clinical display (or processed) representation images. We evaluated the transformation (pixel mapping) of the raw image, giving a third representation (raw-transformed), to improve the PDa performance using differential evolution optimization. We applied PD to the raw and clinical display images as a standard for measurement comparison. Conditional logistic regression was used to estimate the odd ratios (ORs) for breast cancer with 95% confidence intervals (CI) for all measurements; analyses were adjusted for body mass index. PDa operates by evaluating signal-dependent noise (SDN), captured as local signal variation. Therefore, we characterized the SDN relationship to understand the PDa performance as a function of data representation and investigated a variation analysis of the transformation.

RESULTS

The associations of the quartiles of operator-assisted PD with breast cancer were similar for the raw (OR: 1.00 [ref.]; 1.59 [95% CI, 0.93-2.70]; 1.70 [95% CI, 0.95-3.04]; 2.04 [95% CI, 1.13-3.67]) and clinical display (OR: 1.00 [ref.]; 1.31 [95% CI, 0.79-2.18]; 1.14 [95% CI, 0.65-1.98]; 1.95 [95% CI, 1.09-3.47]) images. PDa could not be assessed on the raw images without preprocessing. However, PDa had similar associations with breast cancer when assessed on 1) raw-transformed (OR: 1.00 [ref.]; 1.27 [95% CI, 0.74-2.19]; 1.86 [95% CI, 1.05-3.28]; 3.00 [95% CI, 1.67-5.38]) and 2) clinical display (OR: 1.00 [ref.]; 1.79 [95% CI, 1.04-3.11]; 1.61 [95% CI, 0.90-2.88]; 2.94 [95% CI, 1.66-5.19]) images. The SDN analysis showed that a nonlinear relationship between the mammographic signal and its variation (ie, the biomarker for the breast density) is required for PDa. Although variability in the transform influenced the respective PDa distribution, it did not affect the measurement's association with breast cancer.

CONCLUSIONS

PDa assessed on either raw-transformed or clinical display images is a valid automated breast density measurement for a specific FFDM technology and compares well against PD. Further work is required for measurement generalization.

Changes in mammographic density over time in breast cancer cases and women at high risk for breast cancer

High mammographic breast density is one of the strongest intermediate markers of breast cancer risk, and decreases in density over time have been associated with decreases in breast cancer risk. Using repeated measures of mammographic density in a cohort of high-risk women, the Women at Risk (WAR) cohort at Columbia University Medical Center (N = 2670), we examined whether changes in prediagnostic mammographic density differed among 85 prospectively-ascertained breast cancer cases and 85 age-matched controls, using a nested case–control design. Median age at first mammogram was 51 years (range, 29–77 years), with a median of 4 years between first and second prediagnostic mammogram (range, 1–15 years). Using linear regression with change in percent density as the outcome, we found that in women who did not go on to be diagnosed with breast cancer, change in percent density decreased as time between first and second mammogram increased (β = −1.62% per year, p = 0.004). However, in women who did go on to be diagnosed with breast cancer, there was no overall change in percent density associated with time between first and second mammogram (β = 0.29% per year, p = 0.61); the change over time was statistically significantly different between cases versus controls (p <0.009). If replicated in larger cohorts, these results suggest that within-individual changes in mammographic density as measured by percent density may be a useful biomarker of breast cancer risk.

Estimation of percentage breast tissue density: comparison between digital mammography (2D full field digital mammography) and digital breast tomosynthesis according to different BI-RADS categories

OBJECTIVE

To compare breast density estimated from two-dimensional full-field digital mammography (2D FFDM) and from digital breast tomosynthesis (DBT) according to different Breast Imaging-Reporting and Data System (BI-RADS) categories, using automated software.

METHODS

Institutional review board approval and written informed patient consent were obtained. DBT and 2D FFDM were performed in the same patients to allow within-patient comparison. A total of 160 consecutive patients (mean age: 50±14 years; mean body mass index: 22±3) were included to create paired data sets of 40 patients for each BI-RADS category. Automatic software (MedDensity(©), developed by Giulio Tagliafico) was used to compare the percentage breast density between DBT and 2D FFDM. The estimated breast percentage density obtained using DBT and 2D FFDM was examined for correlation with the radiologists' visual BI-RADS density classification.

RESULTS

The 2D FFDM differed from DBT by 16.0% in BI-RADS Category 1, by 11.9% in Category 2, by 3.5% in Category 3 and by 18.1% in Category 4. These differences were highly significant (p<0.0001). There was a good correlation between the BI-RADS categories and the density evaluated using 2D FFDM and DBT (r=0.56, p<0.01 and r=0.48, p<0.01, respectively).

CONCLUSION

Using DBT, breast density values were lower than those obtained using 2D FFDM, with a non-linear relationship across the BI-RADS categories. These data are relevant for clinical practice and research studies using density in determining the risk.

ADVANCES IN KNOWLEDGE

On DBT, breast density values were lower than with 2D FFDM, with a non-linear relationship across the classical BI-RADS categories.

Equol-producing status, isoflavone intake, and breast density in a sample of U.S. Chinese women

BACKGROUND

Differences in ability to metabolize daidzein to equol might help explain inconsistent findings about isoflavones and breast cancer. We examined equol-producing status in relation to breast density, a marker of breast cancer risk, and evaluated whether an association of isoflavone intake with breast density differs by equol-producing status in a sample of Chinese immigrant women.

METHODS

Participants were 224 women, ages 36 to 58 years, enrolled in a study on diet and breast density. All women completed dietary recall interviews, underwent a soy challenge to assess equol-producing status, and received a mammogram assessed for breast density using a computer-assisted method.

RESULTS

In our sample, 30% were classified as equol producers. In adjusted linear regression models, equol producers had significantly lower mean dense tissue area (32.8 vs. 37.7 cm(2), P = 0.03) and lower mean percent breast density (32% vs. 35%, P = 0.03) than nonproducers. Significant inverse associations of isoflavone intake with dense area and percent density were apparent, but only in equol producers (interaction P = 0.05 for both).

CONCLUSIONS

These results support the possibility that equol-producing status affects breast density and that effects of isoflavones on breast density depend on ability to metabolize daidzein to equol.

IMPACT

Although these findings warrant confirmation in a larger sample, they offer a possible explanation for the inconsistent findings about soy intake and breast density and possibly breast cancer risk as well. The findings further suggest the importance of identifying factors that influence equol-producing status and exploring appropriate targeting of interventions.

Validation of DM-Scan, a computer-assisted tool to assess mammographic density in full-field digital mammograms

We developed a semi-automated tool to assess mammographic density (MD), a phenotype risk marker for breast cancer (BC), in full-field digital images and evaluated its performance testing its reproducibility, comparing our MD estimates with those obtained by visual inspection and using Cumulus, verifying their association with factors that influence MD, and studying the association between MD measures and subsequent BC risk.

Three radiologists assessed MD using DM-Scan, the new tool, on 655 processed images (craniocaudal view) obtained in two screening centers. Reproducibility was explored computing pair-wise concordance correlation coefficients (CCC). The agreement between DM-Scan estimates and visual assessment (semi-quantitative scale, 6 categories) was quantified computing weighted kappa statistics (quadratic weights). DM-Scan and Cumulus readings were compared using CCC. Variation of DM-Scan measures by age, body mass index (BMI) and other MD modifiers was tested in regression mixed models with mammographic device as a random-effect term.

The association between DM-Scan measures and subsequent BC was estimated in a case–control study. All BC cases in screening attendants (2007–2010) at a center with full-field digital mammography were matched by age and screening year with healthy controls (127 pairs). DM-Scan was used to blindly assess MD in available mammograms (112 cases/119 controls). Unconditional logistic models were fitted, including age, menopausal status and BMI as confounders.

DM-Scan estimates were very reliable (pairwise CCC: 0.921, 0.928 and 0.916). They showed a reasonable agreement with visual MD assessment (weighted kappa ranging 0.79-0.81). DM-Scan and Cumulus measures were highly concordant (CCC ranging 0.80-0.84), but ours tended to be higher (4%-5% on average). As expected, DM-Scan estimates varied with age, BMI, parity and family history of BC. Finally, DM-Scan measures were significantly associated with BC (p-trend=0.005). Taking MD<7% as reference, OR per categories of MD were: OR_7%-17%=1.32 (95% CI=0.59-2.99), OR_17%-28%=2.28 (95% CI=1.03-5.04) and OR_>=29%=3.10 (95% CI=1.35-7.14). Our results confirm that DM-Scan is a reliable tool to assess MD in full-field digital mammograms.