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

Studying the association between longitudinal nondense breast tissue measurements and the risk of breast cancer: a joint modeling approach

Conflicting results have appeared in the literature on whether the amount of nondense, adipose tissue in the breast is a risk factor or a protective factor for breast cancer (BC), and biological hypotheses supporting both have been proposed. We suggest here that limitations in study design and statistical methodology could potentially explain the inconsistent results. Specifically, we exploit recent advances in methodology and software developed for the joint analysis of multiple longitudinal outcomes and time-to-event data to jointly analyze dense and nondense tissue trajectories and the risk of BC in a large Swedish screening cohort. We also perform extensive sensitivity analyses by mimicking analyses/designs of previously published studies-for example, ignoring available longitudinal data. Overall, we do not find strong evidence supporting an association between nondense tissue and the risk of incident BC. We hypothesize that (1) previous studies have not been able to isolate the effect of nondense tissue from dense tissue or adipose tissue elsewhere in the body, that (2) estimates of the effect of nondense tissue on risk are strongly sensitive to modeling assumptions, or that (3) the effect size of nondense tissue on BC risk is likely to be small/not clinically relevant.

Relationship between breast tissue involution and breast cancer

Breast tissue involution is a process in which the epithelial tissue of the mammary gland gradually disappears with age. The relationship between breast tissue involvement and breast cancer (BC) has received increasing amounts of attention in recent years. Many scholars believe that breast tissue involution is a significant risk factor for BC. Breast imaging parameters, particularly mammographic density (MD), may indirectly reflect the degree of breast tissue involution, which may provide a solid basis for classifying priority screening groups for BC. This review explored the relationship between breast tissue involution and BC by providing an overview of breast tissue involution and elaborating on the association between MD and BC. Consistent with the results of other studies, women with complete breast tissue involution had a lower risk of BC, whereas women with a high MD had a relatively greater risk of BC.

A Review of the Clinical and Epidemiologic Evidence Relevant to the Impact of Postdiagnosis Isoflavone Intake on Breast Cancer Outcomes

PURPOSE OF REVIEW

This narrative review aims to determine the impact of postdiagnosis isoflavone intake, via supplements and foods, on breast cancer outcomes. Foods derived from soybeans are uniquely rich sources of isoflavones, naturally occurring compounds that can bind to estrogen receptors although the extent to which they exert estrogen-like effects in humans is unclear. Isoflavones have been rigorously investigated for a wide range of health benefits including breast cancer prevention. However, their classification as phytoestrogens has led to concern that isoflavones and hence, soy food consumption, could worsen the prognosis of women with breast cancer and interfere with the efficacy of endocrine therapy for this disease.

RECENT FINDINGS

Research in athymic ovariectomized mice shows isoflavones stimulate the growth of existing estrogen-sensitive mammary tumors. However, extensive clinical research indicates that neither soy foods nor isolated isoflavones affect markers of breast cancer risk including mammographic density and breast cell proliferation. No effects are observed even when isoflavone exposure greatly exceeds typical intake in Asian countries. Furthermore, the results from epidemiologic studies indicate postdiagnosis isoflavone intake from soy foods reduces recurrence and possibly mortality from breast cancer. Additionally, the limited observational data do not show that isoflavones interfere with the efficacy of tamoxifen or aromatase inhibitors. Regardless of their treatment status, evidence indicates that women with breast cancer can safely consume soy foods. Limiting intake to no more than two servings of traditional Asian soy foods daily, an amount that provides approximately 50 mg isoflavones, is recommended, not because data indicate exceeding this amount is harmful, but because few population-based studies involved participants consuming more than this intake recommendation.

Mammographic Density in Relation to Breast Cancer Risk Factors among Chinese Women

BACKGROUND

Increased mammographic density (MD) is a known breast cancer risk factor, but its influencing factors are unclear in Asian populations. This study examined the links between known breast cancer risk factors and quantitatively measured MD in 7,351 Chinese women with nonmalignant mammographic findings.

METHODS

VolparaDensity software quantified volumetric MD measures: total breast volume (TBV), absolute dense volume (ADV), percent dense volume (PDV = ADV/TBV), and nondense volume (NDV = TBV - ADV). Multivariable linear regression models assessed associations between these MD metrics and breast cancer risk factors.

RESULTS

The mean age of the population was 50.1 (SD = 8.3) years. The mean ADV, NDV, and PDV were 58.4 cm3 (SD = 32.1), 382.8 cm3 (SD = 202.0), and 14.8 % (SD = 7.1), respectively. PDV was inversely associated with age, weight, body mass index (BMI), parity, breastfeeding duration, and postmenopausal status but positively linked to height and age at menopause. NDV showed opposite associations. ADV had similar associations to PDV, except for height, weight, and BMI, which differed for women with the lowest NDV. PDV associations with age at menarche, age at first birth, and breastfeeding duration varied by BMI and menopausal status.

CONCLUSIONS

MD may influence the relationships between reproductive factors and breast cancer risk, depending on MD measure, menopausal status, and BMI.

IMPACT

This study examines how quantitative MD measures relate to known breast cancer risk factors in an East Asian population, factoring in menopausal status and BMI. The results underscore the complex role of MD and confounding factors in breast cancer risk, highlighting the need for tailored insights for future research and screening.

Breast density awareness and cancer risk in the UAE: Enhancing Women's engagement in early detection

INTRODUCTION

Increased breast density (BD) is significantly correlated to higher rates of breast cancer (BC), yet awareness among women remains low. This study assesses women's understanding of BD, its implications for cancer risk, and their engagement in screening practices.

METHODS

A cross-sectional survey of 212 women aged 40 to 74 was conducted using an online questionnaire developed within Google Forms, including open and closed-ended questions. Demographic information was collected, followed by BC awareness and BD knowledge questions. Data were analyzed using IBM SPSS software, with categorical data reported as numbers and percentages, and Chi-square tests employed to explore associations between variables.

RESULTS

Of the 212 participants, those with healthcare involvement were significantly more likely to recognize BD as a BC risk factor, with 25.0 % acknowledging its impact compared to 16.8 % of non-professionals (χ2 = 9.520, p = 0.009). Formal training was associated with increased engagement in breast self-examinations (BSE), with 58.6 % of trained individuals practicing BSE versus 30.1 % without training (χ2 = 9.108, p = 0.003).

CONCLUSION

Findings underscore the need for targeted educational initiatives to improve BD awareness among women in the general public, empowering them to identify as at-risk and to participate in screening programs.

IMPACT ON PRACTICE

This study emphasizes integrating BD awareness into clinical practice. Healthcare providers are encouraged to implement educational strategies that inform women of BD, its associated risks, and the value of regular screening. Enhancing self-awareness among patients may facilitate earlier detection, ultimately improving BC outcomes and public health efforts.

Artificial intelligence and consistency in patient care: a large-scale longitudinal study of mammographic density assessment

Objectives

To assess whether use of an artificial intelligence (AI) model for mammography could result in more longitudinally consistent breast density assessments compared with interpreting radiologists.

Methods

The AI model was evaluated retrospectively on a large mammography dataset including 50 sites across the United States from an outpatient radiology practice. Examinations were acquired on Hologic imaging systems between 2016 and 2021 and were interpreted by 39 radiologists (36% fellowship trained; years of experience: 2-37 years). Longitudinal patterns in 4-category breast density and binary breast density (non-dense vs. dense) were characterized for all women with at least 3 examinations (61 177 women; 214 158 examinations) as constant, descending, ascending, or bi-directional. Differences in longitudinal density patterns were assessed using paired proportion hypothesis testing.

Results

The AI model produced more constant (P < .001) and fewer bi-directional (P < .001) longitudinal density patterns compared to radiologists (AI: constant 81.0%, bi-directional 4.9%; radiologists: constant 56.8%, bi-directional 15.3%). The AI density model also produced more constant (P < .001) and fewer bi-directional (P < .001) longitudinal patterns for binary breast density. These findings held in various subset analyses, which minimize (1) change in breast density (post-menopausal women, women with stable image-based BMI), (2) inter-observer variability (same radiologist), and (3) variability by radiologist's training level (fellowship-trained radiologists).

Conclusions

AI produces more longitudinally consistent breast density assessments compared with interpreting radiologists.

Advances in knowledge

Our results extend the advantages of AI in breast density evaluation beyond automation and reproducibility, showing a potential path to improved longitudinal consistency and more consistent downstream care for screened women.

Trajectories of breast density change over time and subsequent breast cancer risk: longitudinal study

OBJECTIVE

To identify clusters of women with similar trajectories of breast density change over four longitudinal assessments and to examine the association between these trajectories and the subsequent risk of breast cancer.

DESIGN

Retrospective cohort study.

SETTING

Data from the national breast cancer screening programme, which is embedded in the National Health Insurance Service database in Korea. Breast density was assessed using the four category Breast Imaging Reporting and Data System (BI-RADS) classification. Group based trajectory modelling was performed to identify the trajectories of breast density.

PARTICIPANTS

Women aged ≥40 years who underwent four biennial mammographic screenings between 2009 and 2016.

MAIN OUTCOME MEASURES

Breast cancer development was determined to 31 December 2021. Cox proportional hazard models were used to assess the associations between trajectories and breast cancer outcomes after adjusting for covariates.

RESULTS

Among a cohort of 1 747 507 women (mean age 61.4 years), five breast density trajectory groups were identified. Group 1 included women with persistently fatty breast tissue, group 2 included women with fatty breast tissue at baseline but increased breast density over time, and groups 3-5 included women with denser breasts, with a slight decrease in density over time. Women in group 2 had a 1.60-fold (95% confidence interval 1.49 to 1.72) increased risk of breast cancer compared with those in group 1. Women in groups 3-5 had higher risks compared with those in group 1, with adjusted hazard ratios of 1.86 (1.74 to 1.98), 2.49 (2.33 to 2.65), and 3.07 (2.87 to 3.28), respectively. Similar results were observed across different age groups, regardless of changes in menopausal status or body mass index.

CONCLUSIONS

This study identified five distinct groups of women with similar trajectories of breast density change over time. Future risk of breast cancer was found to vary in these groups. Increasingly dense or persistently dense breasts were associated with a higher risk. Changes in breast density over time should be carefully considered during breast cancer risk stratification and incorporated into future risk models.

Permutation Test for Image-on-Scalar Regression With an Application to Breast Cancer

Image based screening is now routinely available for early detection of cancer and other diseases. Quantitative analysis for effects of risk factors on digital images is important to extract biological insights for modifiable factors in prevention studies and understand pathways for targets in preventive drugs. However, current approaches are restricted to summary measures within the image with the assumption that all relevant features needed to characterize an image can be identified and appropriately quantified. Motivated by data challenges in breast cancer, we propose a nonparametric statistical framework for risk factor screening that uses the whole mammogram image as outcome. The proposed permutation test allows assessment of whether a set of scalar risk factors is associated with the whole image in the presence of correlated residuals across the spatial domain. We provide extensive simulation studies and illustrate an application to the Joanne Knight Breast Health Cohort using the mammogram imaging data.

Trends in breast density and other risk factors for breast cancer and associations with trends in the incidence of breast cancer in Korean women

INTRODUCTION

This study investigated the trends in breast density in Korean women and their association with the incidence of breast cancer, incorporating the trends in the known risk factors for breast cancer from an ecological perspective.

METHODS

The prevalence of risk factors for breast cancer from the National Health and Nutrition Survey, breast density from Korea's national breast cancer screening program, and breast cancer incidence from the Korea Central Cancer Registry during 2010-2018 were applied after age-standardization to the population at the middle of the year 2000. The association between the prevalence of risk factors for breast cancer, the prevalence of dense breast, and the incidence rate of breast cancer was estimated using linear regression.

RESULTS

The proportion of age-standardized dense breasts steadily increased from 45.8 % in 2010 to 51.5 % in 2018. The increased prevalence of dense breasts in women was positively related to the prevalence of smoking, drinking, lack of exercise, early menarche age (<15 years old), premenopausal status, nulliparity, and no history of breastfeeding, and negatively related to the prevalence of obesity. The increased prevalence of the dense breast was associated with an increase in the incidence of breast cancer, and 96 % of the variation in breast cancer incidence could be explained by the variation in the prevalence of dense breast. The factors associated with dense breast and breast cancer incidence overlapped.

CONCLUSIONS

Trends in breast cancer risk factors were associated with an increased prevalence of dense breast, which, in turn, was associated with an increased incidence of breast cancer in Korea.

Association between Inflammatory Dietary Pattern and Mammographic Features

BACKGROUND

The empirical dietary inflammation pattern score (EDIP), which measures the ability of the diet to regulate chronic inflammation, is associated with both higher adiposity and breast cancer (BC) risk. Mammographic density (MD) is an important risk factor for BC.

OBJECTIVE

We examined the associations between EDIP and mammographic features overall and stratified by menopausal status, and assessed the extent to which these associations are mediated by adiposity.

METHODS

We included 4145 participants without BC in the Nurses' Health Study (NHS) and NHSII. Cumulative average EDIP was assessed by food frequency questionnaires every 4-6 y. We assessed MD parameters (percent MD, dense area, and nondense area) and V (measure of grayscale variation). MD parameters were square-root transformed. Multivariable-adjusted linear regression models were used to analyze the associations between EDIP score and MD parameters. Baron and Kenny's regression method was used to assess the extent to which the associations of EDIP and mammographic traits were mediated by BMI.

RESULTS

In multivariable-adjusted models, EDIP was significantly inversely associated with percent MD [top compared with bottom quartile, β = -0.57; 95% confidence interval (CI): -0.78, -0.36]. Additional adjustment for BMI attenuated the association (β = -0.15; 95% CI: -0.34, 0.03), with 68% (β = 0.68, 20; 95% CI: 0.54, 0.86) mediation via BMI. In addition, EDIP was positively associated with nondense area after adjusting for BMI and other covariates. No associations were observed for dense area and V measure. Results were similar when stratified by menopausal status.

CONCLUSIONS

EDIP score was inversely associated with percent MD and positively associated with nondense area, and these associations were largely mediated by BMI.

Assessment of the Breast Density Prevalence in Swiss Women with a Deep Convolutional Neural Network: A Cross-Sectional Study

Background/Objectives: High breast density is a risk factor for breast cancer and can reduce the sensitivity of mammography. Given the influence of breast density on patient risk stratification and screening accuracy, it is crucial to monitor the prevalence of extremely dense breasts within local populations. Moreover, there is a lack of comprehensive understanding regarding breast density prevalence in Switzerland. Therefore, this study aimed to determine the prevalence of breast density in a selected Swiss population. Methods: To overcome the potential variability in breast density classifications by human readers, this study utilized commercially available deep convolutional neural network breast classification software. A retrospective analysis of mammographic images of women aged 40 years and older was performed. Results: A total of 4698 mammograms from women (58 ± 11 years) were included in this study. The highest prevalence of breast density was in category C (heterogeneously dense), which was observed in 41.5% of the cases. This was followed by category B (scattered areas of fibroglandular tissue), which accounted for 22.5%. Conclusions: Notably, extremely dense breasts (category D) were significantly more common in younger women, with a prevalence of 34%. However, this rate dropped sharply to less than 10% in women over 55 years of age.

Reproductive factors and mammographic density within the International Consortium of Mammographic Density: A cross-sectional study

BACKGROUND

Elevated mammographic density (MD) for a woman's age and body mass index (BMI) is an established breast cancer risk factor. The relationship of parity, age at first birth, and breastfeeding with MD is less clear. We examined the associations of these factors with MD within the International Consortium of Mammographic Density (ICMD).

METHODS

ICMD is a consortium of 27 studies with pooled individual-level epidemiological and MD data from 11,755 women without breast cancer aged 35-85 years from 22 countries, capturing 40 country-& ethnicity-specific population groups. MD was measured using the area-based tool Cumulus. Meta-analyses across population groups and pooled analyses were used to examine linear regression associations of square-root (√) transformed MD measures (percent MD (PMD), dense area (DA), and non-dense area (NDA)) with parity, age at first birth, ever/never breastfed and lifetime breastfeeding duration. Models were adjusted for age at mammogram, age at menarche, BMI, menopausal status, use of hormone replacement therapy, calibration method, mammogram view and reader, and parity and age at first birth when not the association of interest.

RESULTS

Among 10,988 women included in these analyses, 90.1% (n = 9,895) were parous, of whom 13% (n = 1,286) had ≥ five births. The mean age at first birth was 24.3 years (Standard deviation = 5.1). Increasing parity (per birth) was inversely associated with √PMD (β: - 0.05, 95% confidence interval (CI): - 0.07, - 0.03) and √DA (β: - 0.08, 95% CI: - 0.12, - 0.05) with this trend evident until at least nine births. Women who were older at first birth (per five-year increase) had higher √PMD (β:0.06, 95% CI:0.03, 0.10) and √DA (β:0.06, 95% CI:0.02, 0.10), and lower √NDA (β: - 0.06, 95% CI: - 0.11, - 0.01). In stratified analyses, this association was only evident in women who were post-menopausal at MD assessment. Among parous women, no associations were found between ever/never breastfed or lifetime breastfeeding duration (per six-month increase) and √MD.

CONCLUSIONS

Associations with higher parity and older age at first birth with √MD were consistent with the direction of their respective associations with breast cancer risk. Further research is needed to understand reproductive factor-related differences in the composition of breast tissue and their associations with breast cancer risk.

Percentage mammographic density or absolute breast density for risk stratification in breast screening: Possible implications for socioeconomic health disparity

OBJECTIVES

Obesity levels and mortality from breast cancer are higher in more deprived areas of the UK, despite lower breast cancer incidence. Supplemental imaging for women with dense breasts has been proposed as a potential improvement to screening, but it is not clear how stratification by percentage mammographic density (%MD) would be reflected across socioeconomic groups. This study aims to clarify the associations between breast composition (dense and fatty tissue) and socioeconomic status in a multi-ethnic screening population.

METHODS

Demographic characteristics were collected for 62,913 participants in a UK breast screening programme (age, ethnicity, Index of Multiple Deprivation (IMD)). Automated mammographic measurements were derived: dense volume (DV), non-dense volume (NDV) and percent density (%MD). Correlations between deprivation and mammographic composition were examined before and after adjustment for age, ethnicity and NDV, using non-dense breast volume as a proxy for body mass index (BMI).

RESULTS

There was negligible correlation between deprivation and DV (r = 0.017; P < 0.001 in all cases), but NDV increased with increasing deprivation (Pearson r = 0.101). Correlations were weaker in the Asian and Chinese ethnic groups. %MD decreased with deprivation (r = -0.094) and adjustment for ethnicity did not alter the association between %MD and IMD (relative change, most to least deprived quintile IMD: 1.18; 95% confidence interval: 1.16, 1.21).

CONCLUSIONS

Deprivation-related differences in %MD in the screening population are largely explained by differences in breast fat volume (NDV) which reflects BMI. Women in more deprived areas, where obesity and breast cancer mortality rates are higher, have increased breast adiposity and may miss out on risk-adapted screening if stratification is based solely on %MD or BIRADS classification.

Associations between breast cancer risk factors and mammographic breast density in a large cross-section of Korean women

BACKGROUND

We investigated the association between established risk factors for breast cancer and mammographic breast density in Korean women.

METHODS

This large cross-sectional study included 8 460 928 women aged >40 years, who were screened for breast cancer between 2009 and 2018. Breast density was assessed using the Breast Imaging Reporting and Data System. This study used multiple logistic regression analyses of age, BMI, age at menarche, menopausal status, menopausal age, parity, breastfeeding status, oral contraceptive use, family history of breast cancer, physical activity, smoking, drinking and hormone replacement therapy use to investigate their associations with mammographic breast density. Analyses were performed using SAS software.

RESULTS

Of 8 460 928 women, 4 139 869 (48.9%) had nondense breasts and 4 321 059 (51.1%) had dense breasts. Factors associated with dense breasts were: earlier age at menarche [<15 vs. ≥15; adjusted odds ratio (aOR), 1.18; 95% confidence interval (CI), 1.17-1.18], premenopausal status (aOR, 2.01; 95% CI, 2.00-2.02), later age at menopause (≥52 vs. <52; aOR, 1.23; 95% CI, 1.22-1.23), nulliparity (aOR, 1.64; 95% CI, 1.63-1.65), never breastfed (aOR, 1.23; 95% CI, 1.23-1.24) and use of hormone replacement therapy (aOR, 1.29; 95% CI, 1.28-1.29). Women with a higher BMI and the use of oral contraceptives were more likely to have nondense breasts.

CONCLUSION

Lower BMI, reproductive health and behavioral factors were associated with dense breasts in Korean women. Additional research should investigate the relationship between mammographic breast density, breast cancer risk factors and breast cancer risk.

Background parenchymal enhancement on contrast-enhanced mammography: associations with breast density and patient's characteristics

PURPOSE

To evaluate if background parenchymal enhancement (BPE) on contrast-enhanced mammography (CEM), graded according to the 2022 CEM-dedicated Breast Imaging Reporting and Data System (BI-RADS) lexicon, is associated with breast density, menopausal status, and age.

METHODS

This bicentric retrospective analysis included CEM examinations performed for the work-up of suspicious mammographic findings. Three readers independently and blindly evaluated BPE on recombined CEM images and breast density on low-energy CEM images. Inter-reader reliability was estimated using Fleiss κ. Multivariable binary logistic regression was performed, dichotomising breast density and BPE as low (a/b BI-RADS categories, minimal/mild BPE) and high (c/d BI-RADS categories, moderate/marked BPE).

RESULTS

A total of 200 women (median age 56.8 years, interquartile range 50.5-65.6, 140/200 in menopause) were included. Breast density was classified as a in 27/200 patients (13.5%), as b in 110/200 (55.0%), as c in 52/200 (26.0%), and as d in 11/200 (5.5%), with moderate inter-reader reliability (κ = 0.536; 95% confidence interval [CI] 0.482-0.590). BPE was minimal in 95/200 patients (47.5%), mild in 64/200 (32.0%), moderate in 25/200 (12.5%), marked in 16/200 (8.0%), with substantial inter-reader reliability (κ = 0.634; 95% CI 0.581-0.686). At multivariable logistic regression, premenopausal status and breast density were significant positive predictors of high BPE, with adjusted odds ratios of 6.120 (95% CI 1.847-20.281, p = 0.003) and 2.416 (95% CI 1.095-5.332, p = 0.029) respectively.

CONCLUSION

BPE on CEM is associated with well-established breast cancer risk factors, being higher in women with higher breast density and premenopausal status.

Associations between quantitative measures of mammographic density and terminal ductal lobular unit involution in Chinese breast cancer patients

BACKGROUND

Higher mammographic density (MD), a radiological measure of the proportion of fibroglandular tissue in the breast, and lower terminal duct lobular unit (TDLU) involution, a histological measure of the amount of epithelial tissue in the breast, are independent breast cancer risk factors. Previous studies among predominantly white women have associated reduced TDLU involution with higher MD.

METHODS

In this cohort of 611 invasive breast cancer patients (ages 23-91 years [58.4% ≥ 50 years]) from China, where breast cancer incidence rates are lower and the prevalence of dense breasts is higher compared with Western countries, we examined the associations between TDLU involution assessed in tumor-adjacent normal breast tissue and quantitative MD assessed in the contralateral breast obtained from the VolparaDensity software. Associations were estimated using generalized linear models with MD measures as the outcome variables (log-transformed), TDLU measures as explanatory variables (categorized into quartiles or tertiles), and adjusted for age, body mass index, parity, age at menarche and breast cancer subtype.

RESULTS

We found that, among all women, percent dense volume (PDV) was positively associated with TDLU count (highest tertile vs. zero: Expbeta = 1.28, 95% confidence interval [CI] 1.08-1.51, ptrend =  < .0001), TDLU span (highest vs. lowest tertile: Expbeta = 1.23, 95% CI 1.11-1.37, ptrend =  < .0001) and acini count/TDLU (highest vs. lowest tertile: Expbeta = 1.22, 95% CI 1.09-1.37, ptrend = 0.0005), while non-dense volume (NDV) was inversely associated with these measures. Similar trend was observed for absolute dense volume (ADV) after the adjustment of total breast volume, although the associations for ADV were in general weaker than those for PDV. The MD-TDLU associations were generally more pronounced among breast cancer patients ≥ 50 years and those with luminal A tumors compared with patients < 50 years and with luminal B tumors.

CONCLUSIONS

Our findings based on quantitative MD and TDLU involution measures among Chinese breast cancer patients are largely consistent with those reported in Western populations and may provide additional insights into the complexity of the relationship, which varies by age, and possibly breast cancer subtype.

Breast Density: Where Are We Now?

Breast density refers to the amount of fibroglandular tissue relative to fat on mammography and is determined either qualitatively through visual assessment or quantitatively. It is a heritable and dynamic trait associated with age, race/ethnicity, body mass index, and hormonal factors. Increased breast density has important clinical implications including the potential to mask malignancy and as an independent risk factor for the development of breast cancer. Breast density has been incorporated into breast cancer risk models. Given the impact of dense breasts on the interpretation of mammography, supplemental screening may be indicated.

Mammographic density mediates the protective effect of early-life body size on breast cancer risk

The unexplained protective effect of childhood adiposity on breast cancer risk may be mediated via mammographic density (MD). Here, we investigate a complex relationship between adiposity in childhood and adulthood, puberty onset, MD phenotypes (dense area (DA), non-dense area (NDA), percent density (PD)), and their effects on breast cancer. We use Mendelian randomization (MR) and multivariable MR to estimate the total and direct effects of adiposity and age at menarche on MD phenotypes. Childhood adiposity has a decreasing effect on DA, while adulthood adiposity increases NDA. Later menarche increases DA/PD, but when accounting for childhood adiposity, this effect is attenuated. Next, we examine the effect of MD on breast cancer risk. DA/PD have a risk-increasing effect on breast cancer across all subtypes. The MD SNPs estimates are heterogeneous, and additional analyses suggest that different mechanisms may be linking MD and breast cancer. Finally, we evaluate the role of MD in the protective effect of childhood adiposity on breast cancer. Mediation MR analysis shows that 56% (95% CIs [32%-79%]) of this effect is mediated via DA. Our finding suggests that higher childhood adiposity decreases mammographic DA, subsequently reducing breast cancer risk. Understanding this mechanism is important for identifying potential intervention targets.

Tamoxifen Dose De-Escalation: An Effective Strategy for Reducing Adverse Effects?

Tamoxifen, a cornerstone in the adjuvant treatment of estrogen receptor-positive breast cancer, significantly reduces breast cancer recurrence and breast cancer mortality; however, its standard adjuvant dose of 20 mg daily presents challenges due to a broad spectrum of adverse effects, contributing to high discontinuation rates. Dose reductions of tamoxifen might be an option to reduce treatment-related toxicity, but large randomized controlled trials investigating the tolerability and, more importantly, efficacy of low-dose tamoxifen in the adjuvant setting are lacking. We conducted an extensive literature search to explore evidence on the tolerability and clinical efficacy of reduced doses of tamoxifen. In this review, we discuss two important topics regarding low-dose tamoxifen: (1) the incidence of adverse effects and quality of life among women using low-dose tamoxifen; and (2) the clinical efficacy of low-dose tamoxifen examined in the preventive setting and evaluated through the measurement of several efficacy derivatives. Moreover, practical tools for tamoxifen dose reductions in the adjuvant setting are provided and further research to establish optimal dosing strategies for individual patients are discussed.

The association between mammographic density and breast cancer risk in Chinese women: a systematic review and meta-analysis

PURPOSE

Breast density has consistently been shown to be an independent risk factor for breast cancer in Western populations; however, few studies have evaluated this topic in Chinese women and there is not yet a unified view. This study investigated the association between mammographic density (MD) and breast cancer risk in Chinese women.

METHODS

The PubMed, Cochrane Library, Embase, and Wanfang databases were systematically searched in June 2023 to include all studies on the association between MD and breast cancer risk in Chinese women. A total of 13,977 breast cancer cases from 14 studies were chosen, including 10 case-control/cross-sectional studies, and 4 case-only studies. For case-control/cross-sectional studies, the odds ratios (ORs) of MD were combined using random effects models, and for case-only studies, relative odds ratios (RORs) were combinations of premenopausal versus postmenopausal breast cancer cases.

RESULTS

Women with BI-RADS density category II-IV in case-control/cross-sectional studies had a 0.93-fold (95% confidence interval [CI] 0.55, 1.57), 1.08-fold (95% CI 0.40, 2.94), and 1.24-fold (95% CI 0.42, 3.69) higher risk compared to women with the lowest density category. Combined RORs for premenopausal versus postmenopausal women in case-only studies were 3.84 (95% CI 2.92, 5.05), 22.65 (95% CI 7.21, 71.13), and 42.06 (95% CI 4.22, 419.52), respectively, for BI-RADS density category II-IV versus I.

CONCLUSIONS

For Chinese women, breast cancer risk is weakly associated with MD; however, breast cancer risk is more strongly correlated with mammographic density in premenopausal women than postmenopausal women. Further research on the factors influencing MD in premenopausal women may provide meaningful insights into breast cancer prevention in China.

Breast density reduction as a predictor for prognosis in premenopausal women with estrogen receptor-positive breast cancer: an exploratory analysis of the updated ASTRRA study

BACKGROUND

While the relationship between mammographic breast density reduction (MDR) and endocrine therapy efficacy has been reported in estrogen receptor (ER)-positive breast cancer, it is still unclear in premenopausal women, especially in the case of adding ovarian function suppression (OFS) to antihormone therapy. The authors investigated the impact of MDR on prognosis stratified by treatment based on the updated results of the ASTRRA trial.

MATERIALS AND METHODS

The ASTRRA trial, a randomized phase III study, showed that adding OFS to tamoxifen (TAM) improved survival in premenopausal women with estrogen receptor-positive breast cancer after chemotherapy. The authors updated survival outcomes and assessed mammography before treatment and the annual follow-up mammography for up to 5 years after treatment initiation. Mammographic density (MD) was classified into four categories based on the Breast Imaging-Reporting and Data System. MDR-positivity was defined as a downgrade in MD grade on follow-up mammography up to 2 years after randomization, with pretreatment MD grade as a reference.

RESULTS

The authors evaluated MDR in 944 of the 1282 patients from the trial, and 813 (86.2%) had grade III or IV MD. There was no difference in the MDR-positivity rate between the two treatment groups [TAM-only group (106/476 (22.3%)) vs. TAM+OFS group (89/468 (19.0%)); P =0.217). MDR-positivity was significantly associated with better disease-free survival (DFS) in the TAM+OFS group (estimated 8-year DFS: 93.1% in MDR-positive vs. 82.0% in MDR-negative patients; HR: 0.37; 95% CI: 0.16-0.85; P =0.019), but not in the TAM-only group ( Pinteraction =0.039). MDR-positive patients who received TAM+OFS had a favorable DFS compared to MDR-negative patients who received only TAM (HR: 0.30; 95% CI: 0.13-0.70; P =0.005).

CONCLUSION

Although the proportion of MDR-positive patients was comparable between both treatment groups, MDR-positivity was independently associated with favorable outcomes only in the TAM+OFS group.

Association between Family History of Breast Cancer and Breast Density in Saudi Premenopausal Women Participating in Mammography Screening

BACKGROUND

Mammographic density and family history of breast cancer (FHBC) are well-established independent factors affecting breast cancer risk; however, the association between these two risk factors in premenopausal-screened women remains unclear. The aim of this study is to investigate the relationship between mammographic density and FHBC among Saudi premenopausal-screened women.

METHODS

A total of 446 eligible participants were included in the study. Mammographic density was assessed qualitatively using the Breast Imaging Reporting and Data System (BIRADS 4th edition). Logistic regression models were built to investigate the relationship between mammographic density and FHBC.

RESULTS

Women with a family history of breast cancer demonstrated an 87% greater chance of having dense tissue than women without a family history of breast cancer (95% CI: 1.14-3.08; p = 0.01). Having a positive family history for breast cancer in mothers was significantly associated with dense tissue (adjusted odds ratio (OR): 5.6; 95% CI: 1.3-24.1; p = 0.02).

CONCLUSION

Dense breast tissue in Saudi premenopausal women undergoing screening may be linked to FHBC. If this conclusion is replicated in larger studies, then breast cancer risk prediction models must carefully consider these breast cancer risk factors.

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.

Breast Cancer in Dense Breasts: Detection Challenges and Supplemental Screening Opportunities

Dense breast tissue at mammography is associated with higher breast cancer incidence and mortality rates, which have prompted new considerations for breast cancer screening in women with dense breasts. The authors review the definition and classification of breast density, density assessment methods, breast cancer risk, current legislation, and future efforts and summarize trials and key studies that have affected the existing guidelines for supplemental screening. Cases of breast cancer in dense breasts are presented, highlighting a variety of modalities and specific imaging findings that can aid in cancer detection and staging. Understanding the current state of breast cancer screening in patients with dense breasts and its challenges is important to shape future considerations for care. Shifting the paradigm of breast cancer detection toward early diagnosis for women with dense breasts may be the answer to reducing the number of deaths from this common disease. ©RSNA, 2023 Online supplemental material is available for this article. Quiz questions for this article are available through the Online Learning Center. See the invited commentary by Yeh in this issue.

Predicting mammographic density with linear ultrasound transducers

BACKGROUND

High mammographic density (MD) is a risk factor for the development of breast cancer (BC). Changes in MD are influenced by multiple factors such as age, BMI, number of full-term pregnancies and lactating periods. To learn more about MD, it is important to establish non-radiation-based, alternative examination methods to mammography such as ultrasound assessments.

METHODS

We analyzed data from 168 patients who underwent standard-of-care mammography and performed additional ultrasound assessment of the breast using a high-frequency (12 MHz) linear probe of the VOLUSON® 730 Expert system (GE Medical Systems Kretztechnik GmbH & Co OHG, Austria). Gray level bins were calculated from ultrasound images to characterize mammographic density. Percentage mammographic density (PMD) was predicted by gray level bins using various regression models.

RESULTS

Gray level bins and PMD correlated to a certain extent. Spearman's ρ ranged from - 0.18 to 0.32. The random forest model turned out to be the most accurate prediction model (cross-validated R2, 0.255). Overall, ultrasound images from the VOLUSON® 730 Expert device in this study showed limited predictive power for PMD when correlated with the corresponding mammograms.

CONCLUSIONS

In our present work, no reliable prediction of PMD using ultrasound imaging could be observed. As previous studies showed a reasonable correlation, predictive power seems to be highly dependent on the device used. Identifying feasible non-radiation imaging methods of the breast and their predictive power remains an important topic and warrants further evaluation. Trial registration 325-19 B (Ethics Committee of the medical faculty at Friedrich Alexander University of Erlangen-Nuremberg, Erlangen, Germany).

Epidemiology and Risk Factors for Breast Cancer: 21st Century Advances, Gaps to Address through Interdisciplinary Science

Research methods to study risk factors and prevention of breast cancer have evolved rapidly. We focus on advances from epidemiologic studies reported over the past two decades addressing scientific discoveries, as well as their clinical and public health translation for breast cancer risk reduction. In addition to reviewing methodology advances such as widespread assessment of mammographic density and Mendelian randomization, we summarize the recent evidence with a focus on the timing of exposure and windows of susceptibility. We summarize the implications of the new evidence for application in risk stratification models and clinical translation to focus prevention-maximizing benefits and minimizing harm. We conclude our review identifying research gaps. These include: pathways for the inverse association of vegetable intake and estrogen receptor (ER)-ve tumors, prepubertal and adolescent diet and risk, early life adiposity reducing lifelong risk, and gaps from changes in habits (e.g., vaping, binge drinking), and environmental exposures.

Association and Prediction Utilizing Craniocaudal and Mediolateral Oblique View Digital Mammography and Long-Term Breast Cancer Risk

Mammographic percentage of volumetric density is an important risk factor for breast cancer. Epidemiology studies historically used film images often limited to craniocaudal (CC) views to estimate area-based breast density. More recent studies using digital mammography images typically use the averaged density between craniocaudal (CC) and mediolateral oblique (MLO) view mammography for 5- and 10-year risk prediction. The performance in using either and both mammogram views has not been well-investigated. We use 3,804 full-field digital mammograms from the Joanne Knight Breast Health Cohort (294 incident cases and 657 controls), to quantity the association between volumetric percentage of density extracted from either and both mammography views and to assess the 5 and 10-year breast cancer risk prediction performance. Our results show that the association between percent volumetric density from CC, MLO, and the average between the two, retain essentially the same association with breast cancer risk. The 5- and 10-year risk prediction also shows similar prediction accuracy. Thus, one view is sufficient to assess association and predict future risk of breast cancer over a 5 or 10-year interval.

PREVENTION RELEVANCE

Expanding use of digital mammography and repeated screening provides opportunities for risk assessment. To use these images for risk estimates and guide risk management in real time requires efficient processing. Evaluating the contribution of different views to prediction performance can guide future applications for risk management in routine care.

Survey of knowledge and awareness of breast density amongst Maltese Women undergoing mammography screening

INTRODUCTION

The ratio of breast glandular tissue to fatty tissue is known as breast density. This study assessed the knowledge and awareness of breast density of Maltese women undergoing mammography screening at the National Screening Unit. Increased breast density knowledge may lead to an increase in supplementary imaging attendance. In Europe, there are very limited studies assessing the knowledge and awareness of breast density, providing a solid rationale for this study to be done locally.

METHODS

Women aged 50 to 69 who were eligible for breast cancer screening at the National Screening Unit were given a validated closed-ended questionnaire as part of a quantitative, prospective, cross-sectional, and descriptive study. The questionnaire was designed to achieve the aims of the study. Using IBM-SPSS (v28) software, the data was analysed using the Friedman and Kruskal Wallis tests.

RESULTS

A total of 127 surveys were gathered, with a maximum margin of error of 8.66% based on a 95% confidence range. Breast density and the risks associated with it were not well known or understood (average scores ranging from 2.80 to 3.34 out of 5), but supplemental screening was more widely known (3.65). Participants' knowledge and awareness were correlated with their age, profession, and degree of education. Leaflets (40%) and medical experts (40%) were respondents' favourite sources of information.

CONCLUSION

The population under study lacks knowledge and awareness of breast density and the risks it entails. It's important to provide women more details about breast density. With this information, women will be empowered to seek the finest care.

IMPLICATIONS FOR PRACTICE

Although some socio-demographic parameters were linked to women's knowledge and awareness, it is advised that more research be done using a bigger sample size through interviews and other studies. Moreover, more information regarding breast density must be provided to women undergoing breast cancer screening in Malta to increase their knowledge and awareness.

Mammographic density in relation to breast cancer recurrence and survival in women receiving neoadjuvant chemotherapy

Objective

The association between mammographic density (MD) and breast cancer (BC) recurrence and survival remains unclear. Patients receiving neoadjuvant chemotherapy (NACT) are in a vulnerable situation with the tumor within the breast during treatment. This study evaluated the association between MD and recurrence/survival in BC patients treated with NACT.

Methods

Patients with BC treated with NACT in Sweden (2005-2016) were retrospectively included (N=302). Associations between MD (Breast Imaging-Reporting and Data System (BI-RADS) 5^th Edition) and recurrence-free/BC-specific survival at follow-up (Q1 2022) were addressed. Hazard ratios (HRs) for recurrence/BC-specific survival (BI-RADS a/b/c vs. d) were estimated using Cox regression analysis and adjusted for age, estrogen receptor status, human epidermal growth factor receptor 2 status, axillary lymph node status, tumor size, and complete pathological response.

Results

A total of 86 recurrences and 64 deaths were recorded. The adjusted models showed that patients with BI-RADS d vs. BI-RADS a/b/c had an increased risk of recurrence (HR 1.96 (95% confidence interval (CI) 0.98-3.92)) and an increased risk of BC-specific death (HR 2.94 (95% CI 1.43-6.06)).

Conclusion

These findings raise questions regarding personalized follow-up for BC patients with extremely dense breasts (BI-RADS d) pre-NACT. More extensive studies are required to confirm our findings.

Studying the association between longitudinal mammographic density measurements and breast cancer risk: a joint modelling approach

BACKGROUND

Researchers have suggested that longitudinal trajectories of mammographic breast density (MD) can be used to understand changes in breast cancer (BC) risk over a woman's lifetime. Some have suggested, based on biological arguments, that the cumulative trajectory of MD encapsulates the risk of BC across time. Others have tried to connect changes in MD to the risk of BC.

METHODS

To summarize the MD-BC association, we jointly model longitudinal trajectories of MD and time to diagnosis using data from a large ([Formula: see text]) mammography cohort of Swedish women aged 40-80 years. Five hundred eighteen women were diagnosed with BC during follow-up. We fitted three joint models (JMs) with different association structures; Cumulative, current value and slope, and current value association structures.

RESULTS

All models showed evidence of an association between MD trajectory and BC risk ([Formula: see text] for current value of MD, [Formula: see text] and [Formula: see text] for current value and slope of MD respectively, and [Formula: see text] for cumulative value of MD). Models with cumulative association structure and with current value and slope association structure had better goodness of fit than a model based only on current value. The JM with current value and slope structure suggested that a decrease in MD may be associated with an increased (instantaneous) BC risk. It is possible that this is because of increased screening sensitivity rather than being related to biology.

CONCLUSION

We argue that a JM with a cumulative association structure may be the most appropriate/biologically relevant model in this context.

Longitudinal Analysis of Change in Mammographic Density in Each Breast and Its Association With Breast Cancer Risk

Importance

Although breast density is an established risk factor for breast cancer, longitudinal changes in breast density have not been extensively studied to determine whether this factor is associated with breast cancer risk.

Objective

To prospectively evaluate the association between change in mammographic density in each breast over time and risk of subsequent breast cancer.

Design, Setting, and Participants

This nested case-control cohort study was sampled from the Joanne Knight Breast Health Cohort of 10 481 women free from cancer at entry and observed from November 3, 2008, to October 31, 2020, with routine screening mammograms every 1 to 2 years, providing a measure of breast density. Breast cancer screening was provided for a diverse population of women in the St Louis region. A total of 289 case patients with pathology-confirmed breast cancer were identified, and approximately 2 control participants were sampled for each case according to age at entry and year of enrollment, yielding 658 controls with a total number of 8710 craniocaudal-view mammograms for analysis.

Exposures

Exposures included screening mammograms with volumetric percentage of density, change in volumetric breast density over time, and breast biopsy pathology-confirmed cancer. Breast cancer risk factors were collected via questionnaire at enrollment.

Main Outcomes and Measures

Longitudinal changes over time in each woman's volumetric breast density by case and control status.

Results

The mean (SD) age of the 947 participants was 56.67 (8.71) years at entry; 141 were Black (14.9%), 763 were White (80.6%), 20 were of other race or ethnicity (2.1%), and 23 did not report this information (2.4%). The mean (SD) interval was 2.0 (1.5) years from last mammogram to date of subsequent breast cancer diagnosis (10th percentile, 1.0 year; 90th percentile, 3.9 years). Breast density decreased over time in both cases and controls. However, there was a significantly slower decrease in rate of decline in density in the breast that developed breast cancer compared with the decline in controls (estimate = 0.027; 95% CI, 0.001-0.053; P = .04).

Conclusions and Relevance

This study found that the rate of change in breast density was associated with the risk of subsequent breast cancer. Incorporation of longitudinal changes into existing models could optimize risk stratification and guide more personalized risk management.

Effects of bariatric surgery on breast density in adult obese women: systematic review and meta-analysis

Introduction

Bariatric surgery is one of the most effective methods for treating obesity. It can effectively reduce body weight and reduce the incidence of obesity-related breast cancer. However, there are different conclusions about how bariatric surgery changes breast density. The purpose of this study was to clarify the changes in breast density from before to after bariatric surgery.

Methods

The relevant literature was searched through PubMed and Embase to screen for studies. Meta-analysis was used to clarify the changes in breast density from before to after bariatric surgery.

Results

A total of seven studies were included in this systematic review and meta-analysis, including a total of 535 people. The average body mass index decreased from 45.3 kg/m2 before surgery to 34.4 kg/m2 after surgery. By the Breast Imaging Reporting and Data System score, the proportion of grade A breast density from before to after bariatric surgery decreased by 3.83% (183 vs. 176), grade B (248 vs. 263) increased by 6.05%, grade C (94 vs. 89) decreased by 5.32%, and grade D (1 vs. 4) increased by 300%. There was no significant change in breast density from before to after bariatric surgery (OR=1.27, 95% confidence interval (CI) [0.74, 2.20], P=0.38). By the Volpara density grade score, postoperative volumetric breast density increased (standardized mean difference = -0.68, 95% CI [-1.08, -0.27], P = 0.001).

Discussions

Breast density increased significantly after bariatric surgery, but this depended on the method of detecting breast density. Further randomized controlled studies are needed to validate our conclusions.

Developing a Supplementary Diagnostic Tool for Breast Cancer Risk Estimation Using Ensemble Transfer Learning

Breast cancer is the most prevalent cancer worldwide. Thus, it is necessary to improve the efficiency of the medical workflow of the disease. Therefore, this study aims to develop a supplementary diagnostic tool for radiologists using ensemble transfer learning and digital mammograms. The digital mammograms and their associated information were collected from the department of radiology and pathology at Hospital Universiti Sains Malaysia. Thirteen pre-trained networks were selected and tested in this study. ResNet101V2 and ResNet152 had the highest mean PR-AUC, MobileNetV3Small and ResNet152 had the highest mean precision, ResNet101 had the highest mean F1 score, and ResNet152 and ResNet152V2 had the highest mean Youden J index. Subsequently, three ensemble models were developed using the top three pre-trained networks whose ranking was based on PR-AUC values, precision, and F1 scores. The final ensemble model, which consisted of Resnet101, Resnet152, and ResNet50V2, had a mean precision value, F1 score, and Youden J index of 0.82, 0.68, and 0.12, respectively. Additionally, the final model demonstrated balanced performance across mammographic density. In conclusion, this study demonstrates the good performance of ensemble transfer learning and digital mammograms in breast cancer risk estimation. This model can be utilised as a supplementary diagnostic tool for radiologists, thus reducing their workloads and further improving the medical workflow in the screening and diagnosis of breast cancer.

Changes in mammographic density and risk of breast cancer among a diverse cohort of women undergoing mammography screening

PURPOSE

Mammographic density (MD) is a strong breast cancer risk factor. MD may change over time, with potential implications for breast cancer risk. Few studies have assessed associations between MD change and breast cancer in racially diverse populations. We investigated the relationships between MD and MD change over time and breast cancer risk in a large, diverse screening cohort.

MATERIALS AND METHODS

We retrospectively analyzed data from 8462 women who underwent ≥ 2 screening mammograms from Sept. 2010 to Jan. 2015 (N = 20,766 exams); 185 breast cancers were diagnosed 1-7 years after screening. Breast percent density (PD) and dense area (DA) were estimated from raw digital mammograms (Hologic Inc.) using LIBRA (v1.0.4). For each MD measure, we modeled breast density change between two sequential visits as a function of demographic and risk covariates. We used Cox regression to examine whether varying degrees of breast density change were associated with breast cancer risk, accounting for multiple exams per woman.

RESULTS

PD at any screen was significantly associated with breast cancer risk (hazard ratio (HR) for PD = 1.03 (95% CI [1.01, 1.05], p < 0.0005), but neither change in breast density nor more extreme than expected changes in breast density were associated with breast cancer risk. We found no evidence of differences in density change or breast cancer risk due to density change by race. Results using DA were essentially identical.

CONCLUSIONS

Using a large racially diverse cohort, we found no evidence of association between short-term change in MD and risk of breast cancer, suggesting that short-term MD change is not a strong predictor for risk.

Association of Breast Cancer Family History With Breast Density Over Time in Korean Women

IMPORTANCE

Evidence suggests that women with a family history of breast cancer (FHBC) in first-degree relatives have a higher level of breast density; however, studies of premenopausal women remain limited.

OBJECTIVE

To investigate the association between FHBC and mammographic breast density and breast density changes among premenopausal women.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cohort study used population-based data obtained from the National Health Insurance Service-National Health Information Database of Korea. We included premenopausal women aged 40 to 55 years who underwent mammography for breast cancer screening once between January 1, 2015, and December 31, 2016 (n = 1 174 214), and women who underwent mammography twice (first in 2015-2016 and again between January 1, 2017 and December 31, 2018) (n = 838 855).

EXPOSURES

Family history of breast cancer was assessed using a self-reported questionnaire, which included information on FHBC in the mother and/or sister.

MAIN OUTCOMES AND MEASURES

Breast density, based on the Breast Imaging Reporting and Data System, was categorized as dense (heterogeneously or extremely dense) and nondense (almost entirely fat or scattered fibroglandular areas). Multivariate logistic regression was used to assess the association among FHBC, breast density, and changes in breast density from the first to second screening. Data analysis was performed from June 1 to September 31, 2022.

RESULTS

Of the 1 174 214 premenopausal women, 34 003 (2.4%; mean [SD] age, 46.3 [3.2] years) reported having FHBC among their first-degree relatives, and 1 140 211 (97.1%; mean [SD] age, 46.3 [3.2] years) reported no FHBC. Odds of having dense breasts was 22% higher (adjusted odds ratio [aOR], 1.22; 95% CI, 1.19-1.26) in women with FHBC than in women without FHBC, and the association varied by affected relatives: mother alone (aOR, 1.15; 95% CI, 1.10-1.21), sister alone (aOR, 1.26; 95% CI, 1.22-1.31), and both mother and sister (aOR, 1.64; 95% CI, 1.20-2.25). Among women with fatty breasts at baseline, the odds of developing dense breasts was higher in women with FHBC than in those without FHBC (aOR, 1.19; 95% CI, 1.11-1.26), whereas among women with dense breasts, higher odds of having persistently dense breasts were observed in women with FHBC (aOR, 1.11; 95% CI, 1.05-1.16) than in those without FHBC.

CONCLUSIONS AND RELEVANCE

In this cohort study of premenopausal Korean women, FHBC was positively associated with an increased incidence of having increased or persistently dense breasts over time. These findings suggest the need for a tailored breast cancer risk assessment for women with FHBC.

Alternative methods to measure breast density in younger women

BACKGROUND

Breast density is a strong and potentially modifiable breast cancer risk factor. Almost everything we know about breast density has been derived from mammography, and therefore, very little is known about breast density in younger women aged <40. This study examines the acceptability and performance of two alternative breast density measures, Optical Breast Spectroscopy (OBS) and Dual X-ray Absorptiometry (DXA), in women aged 18-40.

METHODS

Breast tissue composition (percent water, collagen, and lipid content) was measured in 539 women aged 18-40 using OBS. For a subset of 169 women, breast density was also measured via DXA (percent fibroglandular dense volume (%FGV), absolute dense volume (FGV), and non-dense volume (NFGV)). Acceptability of the measurement procedures was assessed using an adapted validated questionnaire. Performance was assessed by examining the correlation and agreement between the measures and their associations with known determinants of mammographic breast density.

RESULTS

Over 93% of participants deemed OBS and DXA to be acceptable. The correlation between OBS-%water + collagen and %FGV was 0.48. Age and BMI were inversely associated with OBS-%water + collagen and %FGV and positively associated with OBS-%lipid and NFGV.

CONCLUSIONS

OBS and DXA provide acceptable and viable alternative methods to measure breast density in younger women aged 18-40 years.

Age-related change in mammographic breast density of women without history of breast cancer over a 10-year retrospective study

Background

Women with higher breast density are at higher risk of developing breast cancer. Breast density is known to affect sensitivity to mammography and to decrease with age. However, the age change and associated factors involved are still unknown. This study aimed to investigate changes in breast density and the associated factors over a 10-year period.

Materials and Methods

The study included 221 women who had undergone eight or more mammograms for 10 years (2011-2020), were between 25 and 65 years of age, and had no abnormalities as of 2011. Breast density on mammographic images was classified into four categories: fatty, scattered, heterogeneously dense, and extremely dense. Breast density was determined using an image classification program with a Microsoft Lobe's machine-learning model. The temporal changes in breast density over a 10-year period were classified into three categories: no change, decrease, and increase. An ordinal logistic analysis was performed with the three groups of temporal changes in breast density categories as the objective variable and the four items of breast density at the start, BMI, age, and changes in BMI as explanatory variables.

Results

As of 2011, the mean age of the 221 patients was 47 ± 7.3 years, and breast density category 3 scattered was the most common (67.0%). The 10-year change in breast density was 64.7% unchanged, 25.3% decreased, and 10% increased. BMI was increased by 64.7% of women. Breast density decreased in 76.6% of the category at the start: extremely dense breast density at the start was correlated with body mass index (BMI). The results of the ordinal logistic analysis indicated that contributing factors to breast density classification were higher breast density at the start (odds ratio = 0.044; 95% CI [0.025-0.076]), higher BMI at the start (odds ratio = 0.76; 95% CI [0.70-0.83]), increased BMI (odds ratio = 0.57; 95% CI [0.36-0.92]), and age in the 40s at the start (odds ratio = 0.49; 95% CI [0.24-0.99]). No statistically significant differences were found for medical history.

Conclusion

Breast density decreased in approximately 25% of women over a 10-year period. Women with decreased breast density tended to have higher breast density or higher BMI at the start. This effect was more pronounced among women in their 40s at the start. Women with these conditions may experience changes in breast density over time. The present study would be useful to consider effective screening mammography based on breast density.

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.

Estimating Age-Specific Mean Sojourn Time of Breast Cancer and Sensitivity of Mammographic Screening by Breast Density among Korean Women

PURPOSE

High breast cancer incidence and dense breast prevalence among women in forties are specific to Asian. This study examined the natural history of breast cancer among Korean women.

MATERIALS AND METHODS

We applied a three-state Markov model (i.e., healthy, preclinical, and clinical state) to fit the natural history of breast cancer to data in the Korean National Cancer Screening Program. Breast cancer was ascertained by linkage to the Korean Central Cancer Registry. Disease-progression rates (i.e., transition rates between three states), mean sojourn time (MST) and mammographic sensitivity were estimated across 10-year age groups and levels of breast density determined by the Breast Imaging, Reporting and Data System.

RESULTS

Overall prevalence of dense breast was 53.9%. Transition rate from healthy to preclinical state, indicating the preclinical incidence of breast cancer, was higher among women in forties (0.0019; 95% confidence interval [CI], 0.0017 to 0.0021) and fifties (0.0020; 95% CI, 0.0017 to 0.0022), than women in sixties (0.0014; 95% CI, 0.0012 to 0.0017). The MSTs, in which the tumor is asymptomatic but detectable by screening, were also fastest among younger age groups, estimated as 1.98 years (95% CI, 1.67 to 2.33), 2.49 years (95% CI, 1.92 to 3.22), and 3.07 years (95% CI, 2.11 to 4.46) for women in forties, fifties, and sixties, respectively. Having dense breasts increased the likelihood of the preclinical cancer risk (1.96 to 2.35 times) and decreased the duration of MST (1.53 to 2.02 times).

CONCLUSION

This study estimated Korean-specific natural history parameters of breast cancer that would be utilized for establishing optimal screening strategies in countries with higher dense breast prevalence.

Mammography biomarkers of cardiovascular and musculoskeletal health: A review

Breast density (BD) and breast arterial calcifications (BAC) can expand the role of mammography. In premenopause, BD is related to body fat composition: breast adipose tissue and total volume are potential indicators of fat storage in visceral depots, associated with higher risk of cardiovascular disease (CVD). Women with fatty breast have an increased likelihood of hypercholesterolemia. Women without cardiometabolic diseases with higher BD have a lower risk of diabetes mellitus, hypertension, chest pain, and peripheral vascular disease, while those with lower BD are at increased risk of cardiometabolic diseases. BAC, the expression of Monckeberg sclerosis, are associated with CVD risk. Their prevalence, 13 % overall, rises after menopause and is reduced in women aged over 65 receiving hormonal replacement therapy. Due to their distinct pathogenesis, BAC are associated with hypertension but not with other cardiovascular risk factors. Women with BAC have an increased risk of acute myocardial infarction, ischemic stroke, and CVD death; furthermore, moderate to severe BAC load is associated with coronary artery disease. The clinical use of BAC assessment is limited by their time-consuming manual/visual quantification, an issue possibly solved by artificial intelligence-based approaches addressing BAC complex topology as well as their large spectrum of extent and x-ray attenuations. A link between BD, BAC, and osteoporosis has been reported, but data are still inconclusive. Systematic, standardised reporting of BD and BAC should be encouraged.

Studies of parenchymal texture added to mammographic breast density and risk of breast cancer: a systematic review of the methods used in the literature

This systematic review aimed to assess the methods used to classify mammographic breast parenchymal features in relation to the prediction of future breast cancer. 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 to extract published articles in English describing the relationship of parenchymal texture features with the risk of breast cancer. Twenty-eight articles published since 2016 were included in the final review. The identification of parenchymal texture features varied from using a predefined list to machine-driven identification. A reduction in the number of features chosen for subsequent analysis in relation to cancer incidence then varied across statistical approaches and machine learning methods. The variation in approach and number of features identified for inclusion in analysis precluded generating a quantitative summary or meta-analysis of the value of these features to improve predicting risk of future breast cancers. This updated overview of the state of the art revealed research gaps; based on these, we provide recommendations for future studies using parenchymal features for mammogram images to make use of accumulating image data, and external validation of prediction models that extend to 5 and 10 years to guide clinical risk management. Following these recommendations could enhance the applicability of models, helping improve risk classification and risk prediction for women to tailor screening and prevention strategies to the level of risk.

Breast Cancer Screening in Women With Dense Breasts: Current Status and Future Directions for Appropriate Risk Stratification and Imaging Utilization

Breast density continues to be a prevailing topic in the field of breast imaging, with continued complexities contributing to overall confusion and controversy among patients and the medical community. In this article, we explore the current status of breast cancer screening in women with dense breasts including breast density legislation. Risk-based approaches to supplemental screening may be more financially cost-effective. While all advanced imaging modalities detect additional primarily invasive, node-negative cancers, the degree to which this occurs can vary by density category. Future directions include expanding the use of density-inclusive risk models with appropriate risk stratification and imaging utilization. Further research is needed, however, to better understand how to optimize population-based screening programs with knowledge of patients' individualized risk, including breast density assessment, to improve the benefit-to-harm ratio of breast cancer screening.

Over-the-Counter Breast Cancer Classification Using Machine Learning and Patient Registration Records

This study aims to determine the feasibility of machine learning (ML) and patient registration record to be utilised to develop an over-the-counter (OTC) screening model for breast cancer risk estimation. Data were retrospectively collected from women who came to the Hospital Universiti Sains Malaysia, Malaysia for breast-related problems. Eight ML models were used: k-nearest neighbour (kNN), elastic-net logistic regression, multivariate adaptive regression splines, artificial neural network, partial least square, random forest, support vector machine (SVM), and extreme gradient boosting. Features utilised for the development of the screening models were limited to information in the patient registration form. The final model was evaluated in terms of performance across a mammographic density. Additionally, the feature importance of the final model was assessed using the model agnostic approach. kNN had the highest Youden J index, precision, and PR-AUC, while SVM had the highest F2 score. The kNN model was selected as the final model. The model had a balanced performance in terms of sensitivity, specificity, and PR-AUC across the mammographic density groups. The most important feature was the age at examination. In conclusion, this study showed that ML and patient registration information are feasible to be used as the OTC screening model for breast cancer.

Ionizing radiation and volumetric mammographic density

OBJECTIVES

Mammographic density (MD) refers to the percentage of dense tissue of an entire breast and was proposed to be used as a surrogate marker for breast cancer. High-dose ionizing radiation (IR) has been recognized as a breast cancer risk factor. The aim of our study was to investigate association between lifetime low dose ionizing radiation (LDIR) and MD.

MATERIAL AND METHODS

A cross-sectional study included 467 women aged 40-60 years who underwent screening mammography in Łódź, Poland. The digital mammography examination of the breasts included both craniocaudal and mediolateral oblique views. The volumetric breast density (VBD) (%) and fibrograndular tissue volume (FG) (cm3) were determined based on the analysis of mammographic image ("for processing") using Volpara Imaging Software. The exposure to IR was estimated for each individual, based on the data from interviews about diagnostic or therapeutic medical procedures performed in the area of the neck, chest, abdomen and spine, which involved X-rays and γ rays and the data about the doses derived from literature. Linear and logistic regression were fitted with VBD and FG as the outcomes and organ breast dose, effective dose and number of mammographies as the determinants, adjusted for major confounders.

RESULTS

The analyses showed no association between VBD or FG and the breast organ dose or the effective dose. The only significant finding observed concerned the association between the number of mammographies and the FG volume with β coefficient: 0.028 (95% CI: 0.012-0.043), and predicted mean FG volume >13.4 cm3 among the women with >3 mammographies when compared to those with none.

CONCLUSIONS

This study does not, in general, provide support for the positive association between LDIR and MD. The weak association of the FG volume with the number of mammographies warrants further verification in larger independent studies. Int J Occup Med Environ Health. 2022;35(5):635-49.

Mouse Mammary Gland Whole Mount Density Assessment across Different Morphologies Using a Bifurcated Program for Image Processing

Mammographic density is associated with increased breast cancer risk. Conventional visual assessment of murine mouse models does not include quantified total density analysis. A bifurcated method was sufficient to obtain relative density scores on a broad range of two-dimensional whole mount images that contained both normal and abnormal findings. Image processing techniques, including a ridge operator and a gaussian denoising method, were used to isolate background away from mammary epithelium and use mean pixel intensity to represent mammary density on genetically engineered mouse models for breast cancer in mice 4 to 29 months of age. The bifurcated method allowed for application of an optimal image processing approach for the structural elements present in the whole mount images. Gaussian denoising was the optimal approach when more dense lobular growth and tertiary branching dominate and a ridge operator when epithelial growth was more sparse and secondary branching was the more dominant structural feature. The two processing approaches were combined in a single experimental flow program using an initial image density measurement as the decision point between the two approaches. Higher density was associated with lobular growth, tertiary branching, fibrotic stroma, and presence of cancer. The significance of the study is development of a readily accessible program for digital assessment of mammary gland whole mount density across a range of mammary gland morphologies.

In Vivo Measurements of the Bulk Ultrasonic Attenuation Coefficient of Breast Tissue Using a Novel Phase-Insensitive Receiver

This study describes the first in vivo acoustic attenuation measurements of breast tissue undertaken using a novel phase-insensitive detection technique employing a differential pyroelectric sensor. The operation of the sensor is thermal in nature, with its output signal being dictated by the acoustic power integrated over its surface. The particularly novel feature of the sensor lies in its differential principle of operation, which significantly enhances its immunity to background acoustic and vibration noise. A large area variant of the sensor was used to detect ultrasonic energy generated by an array of 14 discrete 3.2-MHz plane piston transducers, transmitted through pendent breasts in water. The transduction and reception capability represent key parts of a prototype Quantitative Ultrasound Computed Tomography Test Facility developed at the National Physical Laboratory to study the efficacy of phase-insensitive ultrasound computed tomography of breast phantoms containing a range of appropriate inclusions, in particular, the measurement uncertainties associated with quantitative reconstructions of the acoustic attenuation coefficient. For this study, attenuation coefficient measurements were made using 1-D projections on 12 nominally healthy study volunteers, whose age ranged from 19 to 65 years. Averaged or bulk attenuation coefficient values were generated in the range 1.7-4.6 dBcm ^-1 at 3.2 MHz and have been compared with existing literature, derived from in vivo and ex vivo studies. Results are encouraging and indicate that the relatively simple technique could be applied as a robust method for assessing the properties of breast tissue, particularly the balance of fatty (adipose) and fibroglandular components.

Evaluation of physician performance using a concurrent-read artificial intelligence system to support breast ultrasound interpretation

Purpose

The purpose of this study was to compare the diagnostic performance and the interpretation time of breast ultrasound examination between reading without and with the artificial intelligence (AI) system as a concurrent reading aid.

Material and methods

A fully crossed multi-reader and multi-case (MRMC) reader study was conducted. Sixteen participating physicians were recruited and retrospectively interpreted 172 breast ultrasound cases in two reading scenarios, once without and once with the AI system (BU-CAD™, TaiHao Medical Inc.) assistance for concurrent reading. Interpretations of any given case set with and without the AI system were separated by at least 5 weeks. These reading results were compared to the reference standard and the area under the LROC curve (AUCLROC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for performance evaluations. The interpretation time was also compared between the unaided and aided scenarios.

Results

With the help of the AI system, the readers had higher diagnostic performance with an increase in the average AUCLROC from 0.7582 to 0.8294 with statistically significant. The sensitivity, specificity, PPV, and NPV were also improved from 95.77%, 24.07%, 44.18%, and 93.50%–98.17%, 30.67%, 46.91%, and 96.10%, respectively. Of these, the improvement in specificity reached statistical significance. The average interpretation time was significantly reduced by approximately 40% when the readers were assisted by the AI system.

Conclusion

The concurrent-read AI system improves the diagnostic performance in detecting and diagnosing breast lesions on breast ultrasound images. In addition, the interpretation time is effectively reduced for the interpreting physicians.

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A reader study was conducted to compare the breast ultrasound interpreting performance without and with the aid of AI system.

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The performance of breast ultrasound interpretation was improved by the AI system as a concurrent reading aid.

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The breast ultrasound interpretation time is significantly reduced by the AI system as a concurrent reading aid.

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The reproducibility experiments of the same lesion cropped by different rectangular proved the robustness of the AI system.