Assessing within-woman changes in mammographic density: a comparison of fully versus semi-automated area-based approaches

Development and evaluation of a method to assess breast cancer risk using a longitudinal history of mammographic density: a cohort study

BACKGROUND

Women with dense breasts have an increased risk of breast cancer. However, breast density is measured with variability, which may reduce the reliability and accuracy of its association with breast cancer risk. This is particularly relevant when visually assessing breast density due to variation in inter- and intra-reader assessments. To address this issue, we developed a longitudinal breast density measure which uses an individual woman's entire history of mammographic density, and we evaluated its association with breast cancer risk as well as its predictive ability.

METHODS

In total, 132,439 women, aged 40-73 yr, who were enrolled in Kaiser Permanente Washington and had multiple screening mammograms taken between 1996 and 2013 were followed up for invasive breast cancer through 2014. Breast Imaging Reporting and Data System (BI-RADS) density was assessed at each screen. Continuous and derived categorical longitudinal density measures were developed using a linear mixed model that allowed for longitudinal density to be updated at each screen. Predictive ability was assessed using (1) age and body mass index-adjusted hazard ratios (HR) for breast density (time-varying covariate), (2) likelihood-ratio statistics (ΔLR-χ2) and (3) concordance indices.

RESULTS

In total, 2704 invasive breast cancers were diagnosed during follow-up (median = 5.2 yr; median mammograms per woman = 3). When compared with an age- and body mass index-only model, the gain in statistical information provided by the continuous longitudinal density measure was 23% greater than that provided by BI-RADS density (follow-up after baseline mammogram: ΔLR-χ2 = 379.6 (degrees of freedom (df) = 2) vs. 307.7 (df = 3)), which increased to 35% (ΔLR-χ2 = 251.2 vs. 186.7) for follow-up after three mammograms (n = 76,313, 2169 cancers). There was a sixfold difference in observed risk between densest and fattiest eight-category longitudinal density (HR = 6.3, 95% CI 4.7-8.7), versus a fourfold difference with BI-RADS density (HR = 4.3, 95% CI 3.4-5.5). Discriminatory accuracy was marginally greater for longitudinal versus BI-RADS density (c-index = 0.64 vs. 0.63, mean difference = 0.008, 95% CI 0.003-0.012).

CONCLUSIONS

Estimating mammographic density using a woman's history of breast density is likely to be more reliable than using the most recent observation only, which may lead to more reliable and accurate estimates of individual breast cancer risk. Longitudinal breast density has the potential to improve personal breast cancer risk estimation in women attending mammography screening.

Repeated measures of mammographic density and texture to evaluate prediction and risk of breast cancer: a systematic review of the methods used in the literature

PURPOSE

It may be important for women to have mammograms at different points in time to track changes in breast density, as fluctuations in breast density can affect breast cancer risk. This systematic review aimed to assess methods used to relate repeated mammographic images to breast cancer risk.

METHODS

The databases including Medline (Ovid) 1946-, Embase.com 1947-, CINAHL Plus 1937-, Scopus 1823-, Cochrane Library (including CENTRAL), and Clinicaltrials.gov were searched through October 2021. Eligibility criteria included published articles in English describing the relationship of change in mammographic features with risk of breast cancer. Risk of bias was assessed using the Quality in Prognostic Studies tool.

RESULTS

Twenty articles were included. The Breast Imaging Reporting and Data System and Cumulus were most commonly used for classifying mammographic density and automated assessment was used on more recent digital mammograms. Time between mammograms varied from 1 year to a median of 4.1, and only nine of the studies used more than two mammograms. Several studies showed that adding change of density or mammographic features improved model performance. Variation in risk of bias of studies was highest in prognostic factor measurement and study confounding.

CONCLUSION

This review provided an updated overview and revealed research gaps in assessment of the use of texture features, risk prediction, and AUC. We provide recommendations for future studies using repeated measure methods for mammogram images to improve risk classification and risk prediction for women to tailor screening and prevention strategies to level of risk.

The epidemiologic factors associated with breast density: A review

In recent years, some studies have evaluated the epidemiologic factors associated with breast density. However, the variant and inconsistent results exist. In addition, breast density has been proved to be a significant risk factor associated with breast cancer. Our review summarized the published studies and emphasized the crucial factors including epidemiological factors associated with breast density. In addition, we also discussed the potential reasons for the discrepant results with risk factors. To decrease the incidence and mortality rates for breast cancer, in clinical practice, breast density should be included for clinical risk models in addition to epidemiological factors, and physicians should get more concentrate on those women with risk factors and provide risk-based breast cancer screening regimens.

Breast cancer risk factors by mode of detection among screened women in the Cancer Prevention Study-II

BACKGROUND

Identifying risk factors for women at high risk of symptom-detected breast cancers that were missed by screening would enable targeting of enhanced screening regimens. To this end, we examined associations of breast cancer risk factors by mode of detection in screened women from the Cancer Prevention Study (CPS)-II Nutrition Cohort.

METHODS

Among 77,206 women followed for a median of 14.8 years, 2711 screen-detected and 1281 symptom-detected breast cancer cases were diagnosed. Multivariable-adjusted associations were estimated using joint Cox proportional hazards regression models with person-time calculated contingent on screening.

RESULTS

Factors associated with higher risks of symptom-detected and screen-detected breast cancer included current combined hormone therapy (HT) use (HR 2.07, 95% CI 1.72-2.48 and 1.45, 1.27-1.65, respectively) and history of benign breast disease (1.85, 1.64-2.08 and 1.43, 1.31-1.55, respectively). Current estrogen-only HT use was associated with symptom-detected (1.40, 1.15-1.71) but not screen-detected (0.95, 0.83-1.09) breast cancer. Higher risk of screen-detected but not symptom-detected breast cancer was observed for obese vs. normal body mass index (1.22, 1.01-1.48 and 0.76, 0.56-1.01, respectively), per 3 h/day sitting time (1.10, 1.04-1.16 and 0.97, 0.89-1.06, respectively), and ≥ 2 drinks per day vs. nondrinker (1.40, 1.16-1.69 and 1.27, 0.97-1.66, respectively).

CONCLUSIONS

Differences in risk factors for symptom-detected vs. screen-detected breast cancer were observed and most notably, use of combined and estrogen-only HT and a history of benign breast disease were associated with increased risk of symptomatic detected breast cancer.

IMPACT

If confirmed, these data suggest that such women may benefit from more intensive screening to facilitate early detection.

Longitudinal Changes in Volumetric Breast Density in Healthy Women across the Menopausal Transition

BACKGROUND

Mammographic breast density declines during menopause. We assessed changes in volumetric breast density across the menopausal transition and factors that influence these changes.

METHODS

Women without a history of breast cancer, who had full field digital mammograms during both pre- and postmenopausal periods, at least 2 years apart, were sampled from four facilities within the San Francisco Mammography Registry from 2007 to 2013. Dense breast volume (DV) was assessed using Volpara on mammograms across the time period. Annualized change in DV from pre- to postmenopause was estimated using linear mixed models adjusted for covariates and per-woman random effects. Multiplicative interactions were evaluated between premenopausal risk factors and time to determine whether these covariates modified the annualized changes.

RESULTS

Among the 2,586 eligible women, 1,802 had one premenopausal and one postmenopausal mammogram, 628 had an additional perimenopausal mammogram, and 156 had two perimenopausal mammograms. Women experienced an annualized decrease in DV [-2.2 cm³ (95% confidence interval, -2.7 to -1.7)] over the menopausal transition. Declines were greater among women with a premenopausal DV above the median (54 cm³) versus below (DV, -3.5 cm³ vs. -1.0 cm³; P < 0.0001). Other breast cancer risk factors, including race, body mass index, family history, alcohol, and postmenopausal hormone therapy, had no effect on change in DV over the menopausal transition.

CONCLUSIONS

High premenopausal DV was a strong predictor of greater reductions in DV across the menopausal transition.

IMPACT

We found that few factors other than premenopausal density influence changes in DV across the menopausal transition, limiting targeted prevention efforts.

Mammographic Density Distribution of Healthy Taiwanese Women and its Naturally Decreasing Trend with Age

We analysed typical mammographic density (MD) distributions of healthy Taiwanese women to augment existing knowledge, clarify cancer risks, and focus public health efforts. From January 2011 to December 2015, 88,193 digital mammograms were obtained from 69,330 healthy Taiwanese women (average, 1.27 mammograms each). MD measurements included dense volume (DV) and volumetric density percentage (VPD) and were quantified by fully automated volumetric density estimation and Box-Cox normalization. Prediction of the declining MD trend was estimated using curve fitting and a rational model. Normalized DV and VPD Lowess curves demonstrated similar but non-identical distributions. In high-density grade participants, the VPD increased from 12.45% in the 35-39-year group to 13.29% in the 65-69-year group but only from 5.21% to 8.47% in low-density participants. Regarding the decreased cumulative VPD percentage, the mean MD declined from 12.79% to 19.31% in the 45-50-year group versus the 50-55-year group. The large MD decrease in the fifth decade in this present study was similar to previous observations of Western women. Obtaining an MD distribution model with age improves the understanding of breast density trends and age variations and provides a reference for future studies on associations between MD and cancer risk.

Mammographic density and ageing: A collaborative pooled analysis of cross-sectional data from 22 countries worldwide

BACKGROUND

Mammographic density (MD) is one of the strongest breast cancer risk factors. Its age-related characteristics have been studied in women in western countries, but whether these associations apply to women worldwide is not known.

METHODS AND FINDINGS

We examined cross-sectional differences in MD by age and menopausal status in over 11,000 breast-cancer-free women aged 35-85 years, from 40 ethnicity- and location-specific population groups across 22 countries in the International Consortium on Mammographic Density (ICMD). MD was read centrally using a quantitative method (Cumulus) and its square-root metrics were analysed using meta-analysis of group-level estimates and linear regression models of pooled data, adjusted for body mass index, reproductive factors, mammogram view, image type, and reader. In all, 4,534 women were premenopausal, and 6,481 postmenopausal, at the time of mammography. A large age-adjusted difference in percent MD (PD) between post- and premenopausal women was apparent (-0.46 cm [95% CI: -0.53, -0.39]) and appeared greater in women with lower breast cancer risk profiles; variation across population groups due to heterogeneity (I2) was 16.5%. Among premenopausal women, the √PD difference per 10-year increase in age was -0.24 cm (95% CI: -0.34, -0.14; I2 = 30%), reflecting a compositional change (lower dense area and higher non-dense area, with no difference in breast area). In postmenopausal women, the corresponding difference in √PD (-0.38 cm [95% CI: -0.44, -0.33]; I2 = 30%) was additionally driven by increasing breast area. The study is limited by different mammography systems and its cross-sectional rather than longitudinal nature.

CONCLUSIONS

Declines in MD with increasing age are present premenopausally, continue postmenopausally, and are most pronounced over the menopausal transition. These effects were highly consistent across diverse groups of women worldwide, suggesting that they result from an intrinsic biological, likely hormonal, mechanism common to women. If cumulative breast density is a key determinant of breast cancer risk, younger ages may be the more critical periods for lifestyle modifications aimed at breast density and breast cancer risk reduction.

Qualitative Versus Quantitative Mammographic Breast Density Assessment: Applications for the US and Abroad

Mammographic breast density (MBD) has been proven to be an important risk factor for breast cancer and an important determinant of mammographic screening performance. The measurement of density has changed dramatically since its inception. Initial qualitative measurement methods have been found to have limited consistency between readers, and in regards to breast cancer risk. Following the introduction of full-field digital mammography, more sophisticated measurement methodology is now possible. Automated computer-based density measurements can provide consistent, reproducible, and objective results. In this review paper, we describe various methods currently available to assess MBD, and provide a discussion on the clinical utility of such methods for breast cancer screening.

A Randomized Double-Blind Placebo-Controlled Trial of the Effect of Vitamin D3 Supplementation on Breast Density in Premenopausal Women

Background: This double-blind, placebo-controlled parallel group trial assessed whether oral supplementation with 1,000, 2,000, or 3,000 IU/day vitamin D₃ over one year reduces percent mammographic breast density in premenopausal women.Methods: The trial was conducted between October 2012 and June 2015, among premenopausal female volunteers from Quebec City (Quebec, Canada). Women were randomized with ratio 1:1:1:1 to one of four study arms (1,000, 2,000, or 3,000 IU/day vitamin D₃ or placebo). The primary outcome was mean change in percent mammographic breast density. Participants and research team were blinded to study arm assignment.Results: Participants (n = 405) were randomized to receive 1,000 (n = 101), 2,000 (n = 104), or 3,000 IU/day (n = 101) vitamin D₃, or a placebo (n = 99). The primary analysis included 391 participants (96, 99, 100, and 96, respectively). After the one-year intervention, mean ± SE change in percent breast density in the arms 1,000 IU/day (-5.5% ± 0.5%) and 2,000 IU/day (-5.9% ± 0.5%) vitamin D₃ was similar to that in the placebo arm (-5.7% ± 0.5%) (P values = 1.0). In the 3,000 IU/day vitamin D₃ arm, percent breast density also declined but slightly less (-3.8% ± 0.5%) compared with placebo arm (P = 0.03). Adherence to intervention was excellent (92.8%), and reporting of health problems was comparable among study arms (P ≥ 0.95). All participants had normal serum calcium.Conclusions: In premenopausal women, one-year supplementation with 1,000, 2,000, or 3,000 IU/day vitamin D₃ resulted in a reduction of percent breast density no greater than that seen with the placebo.Impact: At doses of 1,000-3,000 IU/day, vitamin D supplementation will not reduce breast cancer risk through changes in breast density. Cancer Epidemiol Biomarkers Prev; 26(8); 1233-41. ©2017 AACR.

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

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

Influences of race and breast density on related cognitive and emotion outcomes before mandated breast density notification

RATIONALE

Many states have adopted laws mandating breast density (BD) notification for applicable women; however, very little is known about what women knew or felt about BD and related breast cancer (BC) risk before implementation of BD notification laws.

OBJECTIVE

We examined between-race differences in the extent to which having dense breasts was associated with women's related BD cognition and emotion, and with health care providers' communication about BD.

METHODS

We received surveys between May and October of 2015 assessing health care provider (HCP) communication about BD, BD-related knowledge, BD-related anxiety and BC worry from 182 African American (AA) and 113 European American (EA) women in the state of Michigan for whom we had radiologists' assessments of BD.

RESULTS

Whereas having dense breasts was not associated with any BD-related cognition or emotion, there were robust effects of race as follows: EA women were more likely to have been told about BD by a HCP, more likely to know their BD status, had greater knowledge of BD and of BC risk, and had greater perceptions of BC risk and worry; AA women had greater BD-related anxieties. EA women's greater knowledge of their own BD status was directly related to the increased likelihood of HCP communication about BD. However, HCP communication about BD attenuated anxiety for AA women only.

CONCLUSION

We present the only data of which we are aware that examines between-race differences in the associations between actual BD, HCP communication and BD related cognition and emotion before the implementation of BD notification laws. Our findings suggest that the BD notification laws could yield positive benefits for disparities in BD-related knowledge and anxiety when the notifications are followed by discussions with health care providers.