Reported mammographic density: film-screen versus digital acquisition

Association of Longitudinal Mammographic Breast Density Changes with Subsequent Breast Cancer Risk

Background Although Breast Imaging Reporting and Data System (BI-RADS) density classification has been used to assess future breast cancer risk, its reliability and validity are still debated in literature. Purpose To determine the association between overall longitudinal changes in mammographic breast density and breast cancer risk stratified by menopausal status. Materials and Methods In a retrospective cohort study using the Korean National Health Insurance Service database, women aged at least 40 years without a history of cancer who underwent three consecutive biennial mammographic screenings in 2009-2014 were followed up through December 2020. Participants were divided according to baseline breast density: fatty (BI-RADS categories a, b) versus dense (BI-RADS categories c, d) and then into subgroups on the basis of changes from the first to second and from second to third screenings. Women without change in breast density were used as the reference group. Main outcomes were incident breast cancer events, both invasive breast cancer and ductal carcinoma in situ. Cox proportion hazard regression was used to calculate the hazard ratio (HR) with adjustment for other covariables. Results Among 2 253 963 women (mean age, 59 years ± 9) there were 22 439 detected breast cancers. Premenopausal women with fatty breasts at the first screening had a higher risk of breast cancer as density increased in the second and third screenings (fatty-to-dense HR, 1.45 [95% CI: 1.27, 1.65]; dense-to-fatty HR, 1.53 [95% CI: 1.34, 1.74]; dense-to-dense HR, 1.93 [95% CI: 1.75, 2.13]). In premenopausal women with dense breasts at baseline, those in whom density continuously decreased had a 0.62-fold lower risk (95% CI: 0.56, 0.69). Similar results were observed in postmenopausal women, remaining significant after adjustment for baseline breast density or changes in body mass index (fatty-to-dense HR, 1.50 [95% CI: 1.39, 1.62]; dense-to-fatty HR, 1.42 [95% CI: 1.31, 1.53]; dense-to-dense HR, 1.62 [95% CI: 1.51, 1.75]). Conclusion In both premenopausal and postmenopausal women undergoing three consecutive biennial mammographic screenings, a consecutive increase in breast density augmented the future breast cancer risk whereas a continuous decrease was associated with a lower risk. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Kataoka et al in this issue.

Automatic mammographic breast density classification in Chinese women: clinical validation of a deep learning model

BACKGROUND

High breast density is a strong risk factor for breast cancer. As such, high consistency and accuracy in breast density assessment is necessary.

PURPOSE

To validate our proposed deep learning (DL) model and explore its impact on radiologists on density assessments.

MATERIAL AND METHODS

A total of 3732 mammographic cases were collected as a validated set: 1686 cases before the implementation of the DL model and 2046 cases after the DL model. Five radiologists were divided into two groups (junior and senior groups) to assess all mammograms using either two- or four-category evaluation. Linear-weighted kappa (K) and intraclass correlation coefficient (ICC) statistics were used to analyze the consistency between radiologists before and after implementation of the DL model.

RESULTS

The accuracy and clinical acceptance of the DL model for the junior group were 96.3% and 96.8% for two-category evaluation, and 85.6% and 89.6% for four-category evaluation, respectively. For the senior group, the accuracy and clinical acceptance were 95.5% and 98.0% for two-category evaluation, and 84.3% and 95.3% for four-category evaluation, respectively. The consistency within the junior group, the senior group, and among all radiologists improved with the help of the DL model. For two-category, their K and ICC values improved to 0.81, 0.81, and 0.80 from 0.73, 0.75, and 0.76. And for four-category, their K and ICC values improved to 0.81, 0.82, and 0.82 from 0.73, 0.79, and 0.78, respectively.

CONCLUSION

The DL model showed high accuracy and clinical acceptance in breast density categories. It is helpful to improve radiologists' consistency.

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.

Long-Term Risk of Breast Cancer after Diagnosis of Benign Breast Disease by Screening Mammography

Assessing the long-term risk of breast cancer after diagnosis of benign breast disease by mammography is of utmost importance to design personalised screening strategies. We analysed individual-level data from 778,306 women aged 50–69 years with at least one mammographic screening participation in any of ten breast cancer screening centers in Spain from 1996 to 2015, and followed-up until 2017. We used Poisson regression to compare the rates of incident breast cancer among women with and without benign breast disease. During a median follow-up of 7.6 years, 11,708 (1.5%) women had an incident of breast cancer and 17,827 (2.3%) had a benign breast disease. The risk of breast cancer was 1.77 times higher among women with benign breast disease than among those without (95% CI: 1.61 to 1.95). The relative risk increased to 1.99 among women followed for less than four years, and remained elevated for two decades, with relative risk 1.96 (95% CI: 1.32 to 2.92) for those followed from 12 to 20 years. Benign breast disease is a long-term risk factor for breast cancer. Women with benign breast disease could benefit from closer surveillance and personalized screening strategies.

Comparing Mammographic Density Assessed by Digital Breast Tomosynthesis or Digital Mammography: The Breast Cancer Surveillance Consortium

Background Consistency in reporting Breast Imaging Reporting and Data System (BI-RADS) breast density on mammograms is important because breast density is used for breast cancer risk assessment and is reported directly to women and clinicians to inform decisions about supplemental screening. Purpose To assess the consistency of BI-RADS density reporting between digital breast tomosynthesis (DBT) and digital mammography (DM) and evaluate density as a breast cancer risk factor when assessed using DM versus DBT. Materials and Methods The Breast Cancer Surveillance Consortium is a prospective cohort study of women undergoing mammography with DM or DBT. This secondary analysis included women aged 40-79 years who underwent at least two screening mammography examinations less than 36 months apart. Percentage agreement and κ statistic were estimated for pairs of BI-RADS density assessments. Cox proportional hazards regression was used to calculate hazard ratios (HRs) of breast density as a risk factor for invasive breast cancer. Results A total of 403 326 pairs of mammograms from 342 149 women were evaluated. There were no significant differences in breast density assessment in pairs consisting of one DM and one DBT examination (57 516 of 74 729 [77%]; κ = 0.64), two DM examinations (238 678 of 301 743 [79%]; κ = 0.67), and two DBT examinations (20 763 of 26 854 [77%]; κ = 0.65). Results were similar when restricting the analyses to pairs read by the same radiologist. The breast cancer HRs for breast density were similar for DM and DBT (P = .45 for interaction). The HRs for density acquired using DM and DBT, respectively, were 0.55 (95% CI: 0.49, 0.63) and 0.37 (95% CI: 0.21, 0.66) for almost entirely fat, 1.47 (95% CI: 1.37, 1.58) and 1.36 (95% CI: 1.02, 1.82) for heterogeneously dense, and 1.72 (95% CI: 1.54, 1.93) and 2.05 (95% CI: 1.25, 3.36) for extremely dense breasts. Conclusion Radiologist reporting of Breast Imaging Reporting and Data System density obtained with digital breast tomosynthesis did not differ from that obtained with digital mammography. © RSNA, 2021 Online supplemental material is available for this article.

Mammographic Breast Density and Acculturation: Longitudinal Analysis in Chinese Immigrants

Breast cancer is the most common cancer in women. Asian American women have experienced steadily increasing breast cancer incidence rates over the past several decades. The increased rate might be in part due to acculturation. We tested the hypothesis that higher level of acculturation was associated with higher mammographic breast density (MBD), an indicator of breast cancer risk, in a cohort of 425 premenopausal Chinese immigrant women in Philadelphia. Generalized estimating equations accounted for repeated observations and adjusted for age, type of mammographic image, body mass index, months of breastfeeding, number of live births, age at first birth, and menopausal stage (pre, early peri, late peri, post). Results indicated that acculturation level was not associated with any of the MBD measures. Findings were contrary to our hypothesis and previous, cross-sectional studies. In this study population, reproductive factors had a greater effect on MBD than acculturation-related behaviors in adulthood.

Deep-Learning-Driven Full-Waveform Inversion for Ultrasound Breast Imaging

Ultrasound breast imaging is a promising alternative to conventional mammography because it does not expose women to harmful ionising radiation and it can successfully image dense breast tissue. However, conventional ultrasound imaging only provides morphological information with limited diagnostic value. Ultrasound computed tomography (USCT) uses energy in both transmission and reflection when imaging the breast to provide more diagnostically relevant quantitative tissue properties, but it is often based on time-of-flight tomography or similar ray approximations of the wave equation, resulting in reconstructed images with low resolution. Full-waveform inversion (FWI) is based on a more accurate approximation of wave-propagation phenomena and can consequently produce very high resolution images using frequencies below 1 megahertz. These low frequencies, however, are not available in most USCT acquisition systems, as they use transducers with central frequencies well above those required in FWI. To circumvent this problem, we designed, trained, and implemented a two-dimensional convolutional neural network to artificially generate missing low frequencies in USCT data. Our results show that FWI reconstructions using experiment data after the application of the proposed method successfully converged, showing good agreement with X-ray CT and reflection ultrasound-tomography images.

Deep-Learning-Driven Full-Waveform Inversion for Ultrasound Breast Imaging

Ultrasound breast imaging is a promising alternative to conventional mammography because it does not expose women to harmful ionising radiation and it can successfully image dense breast tissue. However, conventional ultrasound imaging only provides morphological information with limited diagnostic value. Ultrasound computed tomography (USCT) uses energy in both transmission and reflection when imaging the breast to provide more diagnostically relevant quantitative tissue properties, but it is often based on time-of-flight tomography or similar ray approximations of the wave equation, resulting in reconstructed images with low resolution. Full-waveform inversion (FWI) is based on a more accurate approximation of wave-propagation phenomena and can consequently produce very high resolution images using frequencies below 1 megahertz. These low frequencies, however, are not available in most USCT acquisition systems, as they use transducers with central frequencies well above those required in FWI. To circumvent this problem, we designed, trained, and implemented a two-dimensional convolutional neural network to artificially generate missing low frequencies in USCT data. Our results show that FWI reconstructions using experiment data after the application of the proposed method successfully converged, showing good agreement with X-ray CT and reflection ultrasound-tomography images.

Breast density, benign breast disease, and risk of breast cancer over time

OBJECTIVES

Assessing the combined effect of mammographic density and benign breast disease is of utmost importance to design personalized screening strategies.

METHODS

We analyzed individual-level data from 294,943 women aged 50-69 years with at least one mammographic screening participation in any of four areas of the Spanish Breast Cancer Screening Program from 1995 to 2015, and followed up until 2017. We used partly conditional Cox models to assess the association between benign breast disease, breast density, and the risk of breast cancer.

RESULTS

During a median follow-up of 8.0 years, 3697 (1.25%) women had a breast cancer diagnosis and 5941 (2.01%) had a benign breast disease. More than half of screened women had scattered fibroglandular density (55.0%). The risk of breast cancer independently increased with the presence of benign breast disease and with the increase in breast density (p for interaction = 0.84). Women with benign breast disease and extremely dense breasts had a threefold elevated risk of breast cancer compared with those with scattered fibroglandular density and without benign breast disease (hazard ratio [HR] = 3.07; 95%CI = 2.01-4.68). Heterogeneous density and benign breast disease was associated with nearly a 2.5 elevated risk (HR = 2.48; 95%CI = 1.66-3.70). Those with extremely dense breast without a benign breast disease had a 2.27 increased risk (95%CI = 2.07-2.49).

CONCLUSIONS

Women with benign breast disease had an elevated risk for over 15 years independently of their breast density category. Women with benign breast disease and dense breasts are at high risk for future breast cancer.

KEY POINTS

• Benign breast disease and breast density were independently associated with breast cancer. • Women with benign breast disease had an elevated risk for up to 15 years independently of their mammographic density category.

A review of the influence of mammographic density on breast cancer clinical and pathological phenotype

PURPOSE

It is well established that high mammographic density (MD), when adjusted for age and body mass index, is one of the strongest known risk factors for breast cancer (BC), and also associates with higher incidence of interval cancers in screening due to the masking of early mammographic abnormalities. Increasing research is being undertaken to determine the underlying histological and biochemical determinants of MD and their consequences for BC pathogenesis, anticipating that improved mechanistic insights may lead to novel preventative or treatment interventions. At the same time, technological advances in digital and contrast mammography are such that the validity of well-established relationships needs to be re-examined in this context.

METHODS

With attention to old versus new technologies, we conducted a literature review to summarise the relationships between clinicopathologic features of BC and the density of the surrounding breast tissue on mammography, including the associations with BC biological features inclusive of subtype, and implications for the clinical disease course encompassing relapse, progression, treatment response and survival.

RESULTS AND CONCLUSIONS

There is reasonable evidence to support positive relationships between high MD (HMD) and tumour size, lymph node positivity and local relapse in the absence of radiotherapy, but not between HMD and LVI, regional relapse or distant metastasis. Conflicting data exist for associations of HMD with tumour location, grade, intrinsic subtype, receptor status, second primary incidence and survival, which need further confirmatory studies. We did not identify any relationships that did not hold up when data involving newer imaging techniques were employed in analysis.

Trends in Clinical Breast Density Assessment From the Breast Cancer Surveillance Consortium

Changes to mammography practice, including revised Breast Imaging Reporting and Data System (BI-RADS) density classification guidelines and implementation of digital breast tomosynthesis (DBT), may impact clinical breast density assessment. We investigated temporal trends in clinical breast density assessment among 2 990 291 digital mammography (DM) screens and 221 063 DBT screens interpreted by 722 radiologists from 144 facilities in the Breast Cancer Surveillance Consortium. After age-standardization, 46.3% (95% CI = 44.1% to 48.6%) of DM screens were assessed as dense (heterogeneously/extremely dense) during the BI-RADS 4th edition era (2005-2013), compared to 46.5% (95% CI = 43.8% to 49.1%) during the 5th edition era (2014-2016) (P = .93 from two-sided generalized score test). Among DBT screens in the BI-RADS 5th edition era, 45.8% (95% CI = 42.0% to 49.7%) were assessed as dense (P = .77 from two-sided generalized score test) compared to 46.5% (95% CI = 43.8% to 49.1%) dense on DM in BI-RADS 5th edition era. Results were similar when examining all four density categories and age subgroups. Clinicians, researchers, and policymakers may reasonably expect stable density distributions across screened populations despite changes to the BI-RADS guidelines and implementation of DBT.

Changes in mammographic density over time and the risk of breast cancer: An observational cohort study

BACKGROUND

The effect of changes in mammographic density over time on the risk of breast cancer remains inconclusive.

METHODS

We used information from four centres of the Breast Cancer Screening Program in Spain in the period 1996-2015. We analysed individual level data from 117,388 women first screened age 50-54, with at least two screening examinations. Breast density was determined using the BI-RADS classification (A to D in increasing order) at earliest and latest screening examination. Adjusted Poisson regression models were used to estimate the relative risk (RR) and 95% confidence intervals (95%CI) of the association between changes in mammographic density and breast cancer risk over time.

RESULTS

During an average 5.8 years of follow-up, 1592 (1.36%) women had a breast cancer diagnosis. An increase in density category increased breast cancer risk, and a decrease in density decreased the risk, compared with women who remained in the same BI-RADS category. Women whose density category increased from B to C or B to D had a RR of 1.55 (95%CI = 1.24-1.94) and 2.32 (95%CI = 1.48-3.63), respectively. The RR for women whose density increased from C to D was 1.51 (95%CI = 1.03-2.22). Changes in BI-RADS density were similarly associated with the risk for invasive cancer than for ductal carcinoma in situ.

CONCLUSIONS

Although a modest proportion of women changed BI-RADS density category, mammographic density changes modulated the risk of breast cancer and identified women at a differential risk. Using two longitudinal measures of BI-RADS density could help target women for risk-based screening strategies.

A Case-Control Study to Add Volumetric or Clinical Mammographic Density into the Tyrer-Cuzick Breast Cancer Risk Model

Background

Accurate breast cancer risk assessment for women attending routine screening is needed to guide screening and preventive interventions. We evaluated the accuracy of risk predictions from both visual and volumetric mammographic density combined with the Tyrer-Cuzick breast cancer risk model.

Methods

A case-control study (474 patient participants and 2243 healthy control participants) of women aged 40–79 years was performed using self-reported classical risk factors. Breast density was measured by using automated volumetric software and Breast Imaging and Reporting Data System (BI-RADS) density categories. Odds ratios (95% CI) were estimated by using logistic regression, adjusted for age, demographic factors, and 10-year risk from the Tyrer-Cuzick model, for a change from the 25^th to 75^th percentile of the adjusted percent density distribution in control participants (IQ-OR).

Results

After adjustment for classical risk factors in the Tyrer-Cuzick model, age, and body mass index (BMI), BI-RADS density had an IQ-OR of 1.55 (95% CI = 1.33 to 1.80) compared with 1.40 (95% CI = 1.21 to 1.60) for volumetric percent density. Fibroglandular volume (IQ-OR = 1.28, 95% CI = 1.12 to 1.47) was a weaker predictor than was BI-RADS density (P_diff = 0.014) or volumetric percent density (P_diff = 0.065). In this setting, 4.8% of women were at high risk (8% + 10-year risk), using the Tyrer-Cuzick model without density, and 7.1% (BI-RADS) compared with 6.8% (volumetric) when combined with density.

Conclusion

The addition of volumetric and visual mammographic density measures to classical risk factors improves risk stratification. A combined risk could be used to guide precision medicine, through risk-adapted screening and prevention strategies.

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

RATIONALE AND OBJECTIVES

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.

MATERIALS AND METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Breast Density and Breast Cancer Incidence in the Lebanese Population: Results from a Retrospective Multicenter Study

Purpose

To study the distribution of breast mammogram density in Lebanese women and correlate it with breast cancer (BC) incidence.

Methods

Data from 1,049 women who had screening or diagnostic mammography were retrospectively reviewed. Age, menopausal status, contraceptives or hormonal replacement therapy (HRT), parity, breastfeeding, history of BC, breast mammogram density, and final BI-RADS assessment were collected. Breast density was analyzed in each age category and compared according to factors that could influence breast density and BC incidence.

Results

120 (11.4%) patients had BC personal history with radiation and/or chemotherapy; 66 patients were postmenopausal under HRT. Mean age was 52.58 ± 11.90 years. 76.4% of the patients (30–39 years) had dense breasts. Parity, age, and menopausal status were correlated to breast density whereas breastfeeding and personal/family history of BC and HRT were not. In multivariate analysis, it was shown that the risk of breast cancer significantly increases 3.3% with age (P = 0.005), 2.5 times in case of menopause (P = 0.004), and 1.4 times when breast density increases (P = 0.014).

Conclusion

Breast density distribution in Lebanon is similar to the western society. Similarly to other studies, it was shown that high breast density was statistically related to breast cancer, especially in older and menopausal women.

Accuracy of fully automated, quantitative, volumetric measurement of the amount of fibroglandular breast tissue using MRI: correlation with anthropomorphic breast phantoms

To demonstrate the accuracy of fully automated, quantitative, volumetric measurement of the amount of fibroglandular breast tissue (FGT), using MRI, and to investigate the impact of different MRI sequences using anthropomorphic breast phantoms as the ground truth. In this study, 10 anthropomorphic breast phantoms that consisted of different known fractions of adipose and protein tissue, which closely resembled normal breast parenchyma, were developed. Anthropomorphic breast phantoms were imaged with a 1.5 T unit (Siemens, Avantofit) using an 18-channel breast coil. The sequence protocol consisted of an isotropic Dixon sequence (Di), an anisotropic Dixon sequence (Da), and T₁ 3D FLASH sequences with and without fat saturation (T1). Fully automated, quantitative, volumetric measurement of FGT for all anthropomorphic phantoms and sequences was performed and correlated with the amounts of fatty and protein components in the phantoms as the ground truth. Fully automated, quantitative, volumetric measurements of FGT with MRI for all sequences ranged from 5.86 to 61.05% (mean 33.36%). The isotropic Dixon sequence yielded the highest accuracy (median 0.51%-0.78%) and precision (median range 0.19%) compared with anisotropic Dixon (median 1.92%-2.09%; median range 0.55%) and T₁ -weighted sequences (median 2.54%-2.46%; median range 0.82%). All sequences yielded good correlation with the FGT content of the anthropomorphic phantoms. The best correlation of FGT measurements was identified for Dixon sequences (Di, R²  = 0.999; Da, R²  = 0.998) compared with conventional T₁ -weighted sequences (R²  = 0.971). MRI yields accurate, fully automated, quantitative, volumetric measurements of FGT, an increasingly important and sensitive imaging biomarker for breast cancer risk. Compared with conventional T₁ sequences, Dixon-type sequences show the highest correlation and reproducibility for automated, quantitative, volumetric FGT measurements using anthropomorphic breast phantoms as the ground truth.

Comparison Between Digital and Synthetic 2D Mammograms in Breast Density Interpretation

OBJECTIVE

The purpose of this study was to compare assessments of breast density on synthetic 2D images as compared with digital 2D mammograms.

MATERIALS AND METHODS

This retrospective study included consecutive women undergoing screening with digital 2D mammography and tomosynthesis during May 2015 with a negative or benign outcome. In separate reading sessions, three radiologists with 5-25 years of clinical experience and 1 year of experience with synthetic 2D mammography read digital 2D and synthetic 2D images and assigned breast density categories according to the 5th edition of BI-RADS. Inter- and intrareader agreement was assessed for each BI-RADS density assessment and combined dense and nondense categories using percent agreement and Cohen kappa coefficient for consensus and all reads.

RESULTS

A total of 309 patients met study inclusion criteria. Agreement between consensus BI-RADS density categories assigned for digital and synthetic 2D mammography was 80.3% (95% CI, 75.4-84.5%) with κ = 0.73 (95% CI, 0.66-0.79). For combined dense and nondense categories, agreement reached 91.9% (95% CI, 88.2-94.7%). For consensus readings, similar numbers of patients were shifted between nondense and dense categories (11 and 14, respectively) with the synthetic 2D compared with digital 2D mammography. Interreader differences were apparent; assignment to dense categories was greater with digital 2D mammography for reader 1 (odds ratio [OR], 1.26; p = 0.002), the same for reader 2 (OR, 0.91; p = 0.262), and greater with synthetic 2D mammography for reader 3 (OR, 0.86; p = 0.033).

CONCLUSION

Overall, synthetic 2D mammography is comparable with digital 2D mammography in assessment of breast density, though there is some variability by reader. Practices can readily adopt synthetic 2D mammography without concern that it will affect density assessment and subsequent recommendations for supplemental screening.

Association between air pollution and mammographic breast density in the Breast Cancer Surveilance Consortium

Background

Mammographic breast density is a well-established strong risk factor for breast cancer. The environmental contributors to geographic variation in breast density in urban and rural areas are poorly understood. We examined the association between breast density and exposure to ambient air pollutants (particulate matter <2.5 μm in diameter (PM_2.5) and ozone (O₃)) in a large population-based screening registry.

Methods

Participants included women undergoing mammography screening at imaging facilities within the Breast Cancer Surveillance Consortium (2001–2009). We included women aged ≥40 years with known residential zip codes before the index mammogram (n = 279,967). Breast density was assessed using the American College of Radiology’s Breast Imaging-Reporting and Data System (BI-RADS) four-category breast density classification. PM_2.5 and O₃ estimates for grids across the USA (2001–2008) were obtained from the US Environmental Protection Agency Hierarchical Bayesian Model (HBM). For the majority of women (94%), these estimates were available for the year preceding the mammogram date. Association between exposure to air pollutants and density was estimated using polytomous logistic regression, adjusting for potential confounders.

Results

Women with extremely dense breasts had higher mean PM_2.5 and lower O₃ exposures than women with fatty breasts (8.97 vs. 8.66 ug/m³ and 33.70 vs. 35.82 parts per billion (ppb), respectively). In regression analysis, women with heterogeneously dense vs. scattered fibroglandular breasts were more likely to have higher exposure to PM_2.5 (fourth vs. first quartile odds ratio (OR) = 1.19, 95% confidence interval (CI) 1.16 − 1.23). Women with extremely dense vs. scattered fibroglandular breasts were less likely to have higher levels of ozone exposure (fourth vs. first quartile OR = 0.80, 95% CI 0.73–0.87).

Conclusion

Exposure to PM_2.5 and O₃ may in part explain geographical variation in mammographic density. Further studies are warranted to determine the causal nature of these associations.

Variation in Mammographic Breast Density Assessments Among Radiologists in Clinical Practice: A Multicenter Observational Study

BACKGROUND

About half of the United States has legislation requiring radiology facilities to disclose mammographic breast density information to women, often with language recommending discussion of supplemental screening options for women with dense breasts.

OBJECTIVE

To examine variation in breast density assessment across radiologists in clinical practice.

DESIGN

Cross-sectional and longitudinal analyses of prospectively collected observational data.

SETTING

30 radiology facilities within the 3 breast cancer screening research centers of the Population-based Research Optimizing Screening through Personalized Regimens (PROSPR) consortium.

PARTICIPANTS

Radiologists who interpreted at least 500 screening mammograms during 2011 to 2013 (n = 83). Data on 216 783 screening mammograms from 145 123 women aged 40 to 89 years were included.

MEASUREMENTS

Mammographic breast density, as clinically recorded using the 4 Breast Imaging Reporting and Data System categories (heterogeneously dense and extremely dense categories were considered "dense" for analyses), and patient age, race, and body mass index (BMI).

RESULTS

Overall, 36.9% of mammograms were rated as showing dense breasts. Across radiologists, this percentage ranged from 6.3% to 84.5% (median, 38.7% [interquartile range, 28.9% to 50.9%]), with multivariable adjustment for patient characteristics having little effect (interquartile range, 29.9% to 50.8%). Examination of patient subgroups revealed that variation in density assessment across radiologists was pervasive in all but the most extreme patient age and BMI combinations. Among women with consecutive mammograms interpreted by different radiologists, 17.2% (5909 of 34 271) had discordant assessments of dense versus nondense status.

LIMITATION

Quantitative measures of mammographic breast density were not available for comparison.

CONCLUSION

There is wide variation in density assessment across radiologists that should be carefully considered by providers and policymakers when considering supplemental screening strategies. The likelihood of a woman being told she has dense breasts varies substantially according to which radiologist interprets her mammogram.

PRIMARY FUNDING SOURCE

National Institutes of Health.

Effects of Changes in BI-RADS Density Assessment Guidelines (Fourth Versus Fifth Edition) on Breast Density Assessment: Intra- and Interreader Agreements and Density Distribution

OBJECTIVE

The objective of our study was to determine intra- and interreader agreements for density assessment using the fifth edition of the BI-RADS guidelines and to compare with those for density assessment using the fourth edition of the BI-RADS guidelines.

MATERIALS AND METHODS

Five radiologists assessed breast density four times in 104 mammographic examinations: twice using the fourth edition of the BI-RADS guidelines and twice using the fifth edition. The intra- and interreader agreements for density assessment based on each guideline were determined and compared. The density distribution pattern under each of the four BI-RADS density categories using each guideline was also noted and compared.

RESULTS

The intrareader agreement for density assessment using the fifth-edition criteria was lower than that using the fourth-edition criteria (p = 0.0179). The overall intrareader agreement (weighted kappa) using the old criteria was 0.84 (95% CI, 0.80-0.87), and the individual intrareader agreement values in five readers ranged from 0.78 (95% CI, 0.69-0.88) to 0.92 (95% CI, 0.87-0.97). The overall intrareader agreement using the new BI-RADS criteria was 0.77 (95% CI, 0.73-0.81), and the individual intrareader agreement values in five readers ranged from 0.74 (95% CI, 0.64-0.84) to 0.99 (95% CI, 0.98-1.00). The interreader agreement values obtained using the fifth-edition criteria were also lower than those obtained using the fourth-edition criteria (p = 0.006). The overall interreader agreement using the old BI-RADS criteria was 0.65 (95% CI, 0.61-0.69), whereas the overall interreader agreement using the new BI-RADS criteria was 0.57 (95% CI, 0.53-0.61). Overall a higher number of dense assessments were given when the fifth-edition guidelines were used (p < 0.0001).

CONCLUSION

Compared with the intra- and interreader agreements obtained using the fourth edition of the BI-RADS guidelines, the intra- and interreader agreements were lower using the fifth-edition guidelines. An increased number of dense assessments were given when the fifth-edition guidelines were used.

Consistency of breast density categories in serial screening mammograms: A comparison between automated and human assessment

Reliable breast density measurement is needed to personalize screening by using density as a risk factor and offering supplemental screening to women with dense breasts. We investigated the categorization of pairs of subsequent screening mammograms into density classes by human readers and by an automated system. With software (VDG) and by four readers, including three specialized breast radiologists, 1000 mammograms belonging to 500 pairs of subsequent screening exams were categorized into either two or four density classes. We calculated percent agreement and the percentage of women that changed from dense to non-dense and vice versa. Inter-exam agreement (IEA) was calculated with kappa statistics. Results were computed for each reader individually and for the case that each mammogram was classified by one of the four readers by random assignment (group reading). Higher percent agreement was found with VDG (90.4%, CI 87.9-92.9%) than with readers (86.2-89.2%), while less plausible changes from non-dense to dense occur less often with VDG (2.8%, CI 1.4-4.2%) than with group reading (4.2%, CI 2.4-6.0%). We found an IEA of 0.68-0.77 for the readers using two classes and an IEA of 0.76-0.82 using four classes. IEA is significantly higher with VDG compared to group reading. The categorization of serial mammograms in density classes is more consistent with automated software than with a mixed group of human readers. When using breast density to personalize screening protocols, assessment with software may be preferred over assessment by radiologists.

Inter- and intra-observer agreement of BI-RADS-based subjective visual estimation of amount of fibroglandular breast tissue with magnetic resonance imaging: comparison to automated quantitative assessment

Purpose

To evaluate the inter-/intra-observer agreement of BI-RADS-based subjective visual estimation of the amount of fibroglandular tissue (FGT) with magnetic resonance imaging (MRI), and to investigate whether FGT assessment benefits from an automated, observer-independent, quantitative MRI measurement by comparing both approaches.

Materials and methods

Eighty women with no imaging abnormalities (BI-RADS 1 and 2) were included in this institutional review board (IRB)-approved prospective study. All women underwent un-enhanced breast MRI. Four radiologists independently assessed FGT with MRI by subjective visual estimation according to BI-RADS. Automated observer-independent quantitative measurement of FGT with MRI was performed using a previously described measurement system. Inter-/intra-observer agreements of qualitative and quantitative FGT measurements were assessed using Cohen’s kappa (k).

Results

Inexperienced readers achieved moderate inter-/intra-observer agreement and experienced readers a substantial inter- and perfect intra-observer agreement for subjective visual estimation of FGT. Practice and experience reduced observer-dependency. Automated observer-independent quantitative measurement of FGT was successfully performed and revealed only fair to moderate agreement (k = 0.209–0.497) with subjective visual estimations of FGT.

Conclusion

Subjective visual estimation of FGT with MRI shows moderate intra-/inter-observer agreement, which can be improved by practice and experience. Automated observer-independent quantitative measurements of FGT are necessary to allow a standardized risk evaluation.

Key Points

• Subjective FGT estimation with MRI shows moderate intra-/inter-observer agreement in inexperienced readers.

• Inter-observer agreement can be improved by practice and experience.

• Automated observer-independent quantitative measurements can provide reliable and standardized assessment of FGT with MRI.

Supplemental Screening for Breast Cancer in Women With Dense Breasts: A Systematic Review for the U.S. Preventive Services Task Force

BACKGROUND

Screening mammography has lower sensitivity and specificity in women with dense breasts, who experience higher breast cancer risk.

PURPOSE

To perform a systematic review of reproducibility of Breast Imaging Reporting and Data System (BI-RADS) density categorization and test performance and clinical outcomes of supplemental screening with breast ultrasonography, magnetic resonance imaging (MRI), and digital breast tomosynthesis (DBT) in women with dense breasts and negative mammography results.

DATA SOURCES

MEDLINE, PubMed, EMBASE, and Cochrane database from January 2000 to July 2015.

STUDY SELECTION

Studies reporting BI-RADS density reproducibility or supplemental screening results for women with dense breasts.

DATA EXTRACTION

Quality assessment and abstraction of 24 studies from 7 countries; 6 studies were good-quality.

DATA SYNTHESIS

Three good-quality studies reported reproducibility of BI-RADS density; 13% to 19% of women were recategorized between "dense" and "nondense" at subsequent screening. Two good-quality studies reported that sensitivity of ultrasonography for women with negative mammography results ranged from 80% to 83%; specificity, from 86% to 94%; and positive predictive value (PPV), from 3% to 8%. The sensitivity of MRI ranged from 75% to 100%; specificity, from 78% to 94%; and PPV, from 3% to 33% (3 studies). Rates of additional cancer detection with ultrasonography were 4.4 per 1000 examinations (89% to 93% invasive); recall rates were 14%. Use of MRI detected 3.5 to 28.6 additional cancer cases per 1000 examinations (34% to 86% invasive); recall rates were 12% to 24%. Rates of cancer detection with DBT increased by 1.4 to 2.5 per 1000 examinations compared with mammography alone (3 studies). Recall rates ranged from 7% to 11%, compared with 7% to 17% with mammography alone. No studies examined breast cancer outcomes.

LIMITATIONS

Good-quality evidence was sparse. Studies were small and CIs were wide. Definitions of recall were absent or inconsistent.

CONCLUSION

Density ratings may be recategorized on serial screening mammography. Supplemental screening of women with dense breasts finds additional breast cancer but increases false-positive results. Use of DBT may reduce recall rates. Effects of supplemental screening on breast cancer outcomes remain unclear.

PRIMARY FUNDING SOURCE

Agency for Healthcare Research and Quality.

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.

Misclassification of Breast Imaging Reporting and Data System (BI-RADS) Mammographic Density and Implications for Breast Density Reporting Legislation

USA states have begun legislating mammographic breast density reporting to women, requiring that women undergoing screening mammography who have dense breast tissue (Breast Imaging Reporting and Data System [BI-RADS] density c or d) receive written notification of their breast density; however, the impact that misclassification of breast density will have on this reporting remains unclear. The aim of this study was to assess reproducibility of the four-category BI-RADS density measure and examine its relationship with a continuous measure of percent density. We enrolled 19 radiologists, experienced in breast imaging, from a single integrated health care system. Radiologists interpreted 341 screening mammograms at two points in time 6 months apart. We assessed intra- and interobserver agreement in radiologists'; interpretations of BI-RADS density and explored whether agreement depended upon radiologist characteristics. We examined the relationship between BI-RADS density and percent density in a subset of 282 examinations. Intraradiologist agreement was moderate to substantial, with kappa varying across radiologists from 0.50 to 0.81 (mean = 0.69, 95% CI [0.63, 0.73]). Intraradiologist agreement was higher for radiologists with ≥10 years experience interpreting mammograms (difference in mean kappa = 0.10, 95% CI [0.01, 0.24]). Interradiologist agreement varied widely across radiologist pairs from slight to substantial, with kappa ranging from 0.02 to 0.72 (mean = 0.46, 95% CI [0.36, 0.55]). Of 145 examinations interpreted as "nondense" (BI-RADS density a or b) by the majority of radiologists, 82.8% were interpreted as "dense" (BI-RADS density c or d) by at least one radiologist. Of 187 examinations interpreted as "dense" by the majority of radiologists, 47.1% were interpreted as "nondense" by at least one radiologist. While the examinations of almost half of the women in our study were interpreted clinically as having BI-RADS density c or d, only about 10% of examinations had percent density >50%. Our results suggest that breast density reporting based on a single BI-RADS density interpretation may be misleading due to high interradiologist variability and a lack of correspondence between BI-RADS density and percent density.

Do mammographic tumor features in breast cancer relate to breast density and invasiveness, tumor size, and axillary lymph node involvement?

BACKGROUND

Breast density and mammographic tumor features of breast cancer may carry prognostic information. The potential benefit of using the combined information obtained from breast density, mammographic tumor features, and pathological tumor characteristics has not been extensively studied.

PURPOSE

To investigate how mammographic tumor features relate to breast density and pathological tumor characteristics.

MATERIAL AND METHODS

This retrospective study was carried out within the Malmö Diet and Cancer Study: a population-based cohort study recruiting 17,035 women during 1991-1996. A total of 826 incident breast cancers were identified during follow-up. Mammography images were collected and analyzed according to breast density and tumor features at diagnosis. Pathological data were retrieved from medical reports. Mammographic tumor features in relation to invasiveness, tumor size, and axillary lymph node involvement were analyzed using logistic regression yielding odds ratios (OR) with 95% confidence intervals (CI) and adjusted for age at diagnosis, mode of detection, and breast density.

RESULTS

Tumors presenting as an ill-defined mass or calcifications were more common in dense breasts than tumors presenting as a distinct mass or with spiculated appearance. Invasive cancer was more common in tumors with spiculated appearance than tumors presenting as a distinct mass (adjusted OR, 5.68 [1.81-17.84]). Among invasive tumors, an ill-defined mass was more often large (>20 mm) compared with a distinct mass, (adjusted OR, 3.16 [1.80-5.55]).

CONCLUSION

Tumors presenting as an ill-defined mass or calcifications were more common in dense breasts. Spiculated appearance was related to invasiveness, and ill-defined mass to larger tumor size, regardless of mode of detection and breast density. The potential role of mammographic tumor features in clinical decision-making warrants further investigation.

Mammographic density in relation to tumor biomarkers, molecular subtypes, and mode of detection in breast cancer

PURPOSE

Mammographic density is an established risk factor for breast cancer; however, the relation to tumor pathological parameters including the androgen receptor and molecular subtypes has not been extensively studied.

METHODS

In the Malmö Diet and Cancer Study, 733 invasive breast cancers were diagnosed from 1991 to 2007. Mammographic density was defined qualitatively. Tumor biomarker information including estrogen receptor (ER), progesterone receptor, androgen receptor (AR), human epidermal growth factor 2 (HER2), and Ki67 was collected. Surrogate molecular subtypes were defined as luminal A, luminal B, HER2 positive and triple-negative breast cancer (TNBC).

RESULTS

Among the 632 tumors with mammographic and pathological information, 352 tumors were screening-detected and 280 clinically detected. Higher mammographic density was associated with ER-negative tumors [ORadj 1.93 (1.04-3.59)] and TNBC [ORadj 2.44 (1.01-5.89), luminal A reference], in clinically detected breast cancer. Similarly, higher mammographic density was associated with AR-negative tumors [ORadj 1.77 (0.80-3.93)] in clinically detected breast cancer, though the evidence for this association was weak.

CONCLUSIONS

In clinically detected breast cancer, but not in screening-detected, higher mammographic density was associated with ER-negative tumors including TNBC. This study highlights the need for taking mode of detection into consideration when addressing mammographic density and tumor biomarkers.

One versus Two Breast Density Measures to Predict 5- and 10-Year Breast Cancer Risk

BACKGROUND

One measure of Breast Imaging Reporting and Data System (BI-RADS) breast density improves 5-year breast cancer risk prediction, but the value of sequential measures is unknown. We determined whether two BI-RADS density measures improve the predictive accuracy of the Breast Cancer Surveillance Consortium 5-year risk model compared with one measure.

METHODS

We included 722,654 women of ages 35 to 74 years with two mammograms with BI-RADS density measures on average 1.8 years apart; 13,715 developed invasive breast cancer. We used Cox regression to estimate the relative hazards of breast cancer for age, race/ethnicity, family history of breast cancer, history of breast biopsy, and one or two density measures. We developed a risk prediction model by combining these estimates with 2000-2010 Surveillance, Epidemiology, and End Results incidence and 2010 vital statistics for competing risk of death.

RESULTS

The two-measure density model had marginally greater discriminatory accuracy than the one-measure model (AUC, 0.640 vs. 0.635). Of 18.6% of women (134,404 of 722,654) who decreased density categories, 15.4% (20,741 of 134,404) of women whose density decreased from heterogeneously or extremely dense to a lower density category with one other risk factor had a clinically meaningful increase in 5-year risk from <1.67% with the one-density model to ≥1.67% with the two-density model.

CONCLUSION

The two-density model has similar overall discrimination to the one-density model for predicting 5-year breast cancer risk and improves risk classification for women with risk factors and a decrease in density.

IMPACT

A two-density model should be considered for women whose density decreases when calculating breast cancer risk.

Reliability of automated breast density measurements

PURPOSE

To estimate the reliability of a reference standard two-dimensional area-based method and three automated volumetric breast density measurements by using repeated measures.

MATERIALS AND METHODS

Thirty women undergoing screening mammography consented to undergo a repeated left craniocaudal examination performed by a second technologist in this prospective institutional review board-approved HIPAA-compliant study. Breast density was measured by using an area-based method (Cumulus ABD) and three automated volumetric methods (CumulusV [University of Toronto], Volpara [version 1.4.5; Volpara Solutions, Wellington, New Zealand), and Quantra [version 2.0; Hologic, Danbury, Conn]). Discrepancy between the first and second breast density measurements (Δ1-2) was obtained for each algorithm by subtracting the second measurement from the first. The Δ1-2 values of each algorithm were then analyzed with a random-effects model to derive Bland-Altman-type limits of measurement agreement.

RESULTS

Variability was higher for Cumulus ABD and CumulusV than for Volpara or Quantra. The within-breast density measurement standard deviations were 3.32% (95% confidence interval [CI]: 2.65, 4.44), 3.59% (95% CI: 2.86, 4.48), 0.99% (95% CI: 0.79, 1.33), and 1.64% (95% CI: 1.31, 1.39) for Cumulus ABD, CumulusV, Volpara, and Quantra, respectively. Although the mean discrepancy between repeat breast density measurements was not significantly different from zero for any of the algorithms, larger absolute breast density discrepancy (Δ1-2) values were associated with larger breast density values for Cumulus ABD and CumulusV but not for Volpara and Quantra.

CONCLUSION

Variability in a repeated measurement of breast density is lowest for Volpara and Quantra; these algorithms may be more suited to incorporation into a risk model.

Prevalence of mammographically dense breasts in the United States

BACKGROUND

National legislation is under consideration that would require women with mammographically dense breasts to be informed of their breast density and encouraged to discuss supplemental breast cancer screening with their health care providers. The number of US women potentially affected by this legislation is unknown.

METHODS

We determined the mammographic breast density distribution by age and body mass index (BMI) using data from 1518 599 mammograms conducted from 2007 through 2010 at mammography facilities in the Breast Cancer Surveillance Consortium (BCSC). We applied these breast density distributions to age- and BMI-specific counts of the US female population derived from the 2010 US Census and the National Health and Nutrition Examination Survey (NHANES) to estimate the number of US women with dense breasts.

RESULTS

Overall, 43.3% (95% confidence interval [CI] = 43.1% to 43.4%) of women 40 to 74 years of age had heterogeneously or extremely dense breasts, and this proportion was inversely associated with age and BMI. Based on the age and BMI distribution of US women, we estimated that 27.6 million women (95% CI = 27.5 to 27.7 million) aged 40 to 74 years in the United States have heterogeneously or extremely dense breasts. Women aged 40 to 49 years (N = 12.3 million) accounted for 44.3% of this group.

CONCLUSION

The prevalence of dense breasts among US women of common breast cancer screening ages exceeds 25 million. Policymakers and healthcare providers should consider this large prevalence when debating breast density notification legislation and designing strategies to ensure that women who are notified have opportunities to evaluate breast cancer risk and discuss and pursue supplemental screening options if deemed appropriate.

Breast density and mode of detection in relation to breast cancer specific survival: a cohort study

BACKGROUND

The aim of this study was to examine breast density in relation to breast cancer specific survival and to assess if this potential association was modified by mode of detection. An additional aim was to study whether the established association between mode of detection and survival is modified by breast density.

METHODS

The study included 619 cases from a prospective cohort, The Malmö Diet and Cancer Study. Breast density estimated qualitatively, was analyzed in relation to breast cancer death, in non-symptomatic and symptomatic women, using Cox regression calculating hazard ratios (HR) with 95% confidence intervals. Adjustments were made in several steps for; diagnostic age, tumour size, axillary lymph node involvement, grade, hormone receptor status, body mass index (baseline), diagnostic period, use of hormone replacement therapy at diagnosis and mode of detection. Detection mode in relation to survival was analyzed stratified for breast density. Differences in HR following different adjustments were analyzed by Freedmans%.

RESULTS

After adjustment for age and other prognostic factors, women with dense, as compared to fatty breasts, had an increased risk of breast cancer death, HR 2.56:1.07-6.11, with a statistically significant trend over density categories, p = 0.04. In the stratified analysis, the effect was less pronounced in non-symptomatic women, HR 2.04:0.49-8.49 as compared to symptomatic, HR 3.40:1.06-10.90. In the unadjusted model, symptomatic women had a higher risk of breast cancer death, regardless of breast density. Analyzed by Freedmans%, age, tumour size, lymph nodes, grade, diagnostic period, ER and PgR explained 55.5% of the observed differences in mortality between non-symptomatic and symptomatic cases. Additional adjustment for breast density caused only a minor change.

CONCLUSIONS

High breast density at diagnosis may be associated with decreased breast cancer survival. This association appears to be stronger in women with symptomatic cancers but breast density could not explain differences in survival according to detection mode.

Benign breast disease, mammographic breast density, and the risk of breast cancer

BACKGROUND

Benign breast disease and high breast density are prevalent, strong risk factors for breast cancer. Women with both risk factors may be at very high risk.

METHODS

We included 42818 women participating in the Breast Cancer Surveillance Consortium who had no prior diagnosis of breast cancer and had undergone at least one benign breast biopsy and mammogram; 1359 women developed incident breast cancer in 6.1 years of follow-up (78.1% invasive, 21.9% ductal carcinoma in situ). We calculated hazard ratios (HRs) using Cox regression analysis. The referent group was women with nonproliferative changes and average density. All P values are two-sided.

RESULTS

Benign breast disease and breast density were independently associated with breast cancer. The combination of atypical hyperplasia and very high density was uncommon (0.6% of biopsies) but was associated with the highest risk for breast cancer (HR = 5.34; 95% confidence interval [CI] = 3.52 to 8.09, P < .001). Proliferative disease without atypia (25.6% of biopsies) was associated with elevated risk that varied little across levels of density: average (HR = 1.37; 95% CI = 1.11 to 1.69, P = .003), high (HR = 2.02; 95% CI = 1.68 to 2.44, P < .001), or very high (HR = 2.05; 95% CI = 1.54 to 2.72, P < .001). Low breast density (4.5% of biopsies) was associated with low risk (HRs <1) for all benign pathology diagnoses.

CONCLUSIONS

Women with high breast density and proliferative benign breast disease are at very high risk for future breast cancer. Women with low breast density are at low risk, regardless of their benign pathologic diagnosis.