Mammographic density phenotypes and risk of breast cancer: a meta-analysis

What do women want to see in a personalized breast cancer risk report? A qualitative study of Asian women of two countries

A breast cancer risk assessment tool for Asian populations, incorporating Polygenic Risk Score and Gail Model algorithm, has been established and validated. However, effective methods for delivering personalized risk information remain underexplored. This study aims to identify and develop effective methods for conveying breast cancer risk information to Asian women. Through ten focus group discussions with 32 women in Indonesia and Singapore, we explored preferences for the presentation of risk information. Participants favored comprehensive reports featuring actionable steps, simplified language, non-intimidating visuals, and personalized risk reduction recommendations. Singaporean participants, more aware of breast cancer prevention, showed a lower likelihood of seeking follow-ups upon receiving low-risk results compared to Indonesians. Overall, participants found the reports useful and advocated for similar approaches in other disease assessments. Balancing content and complexity in reports is crucial, highlighting the need for improved patient understanding and engagement with healthcare providers. Future studies could explore physicians' roles in delivering personalized risk assessments for breast cancer prevention.

The unique risk factor profile of triple-negative breast cancer: a comprehensive meta-analysis

BACKGROUND

Triple-negative breast cancer (TNBC) has a poor prognosis compared with other breast cancer subtypes. This systematic review and meta-analysis examines whether known risk factors for breast cancer are also associated with TNBC in adult women.

METHODS

EMBASE, Medline, SCOPUS, and gray literature were queried with no limit on the date or language of publication. The exposures of interest included parity, breastfeeding, duration of breastfeeding, age at menarche, age at first live birth, oral contraceptive (OC) use, duration of OC use, use of menopausal hormone therapy (MHT), family history, body mass index (BMI), alcohol use, smoking, and breast density. The main outcome of interest was TNBC. Study quality was determined using the Newcastle-Ottawa scale for case control studies and cohort studies. We estimated weighted odds ratios from random effects models to study the exposure-outcome associations. Protocol was registered under the number: PROSPERO 2021 CRD42021254594.

RESULTS

Thirty-three studies were included. Family history, longer duration of OC use, and higher breast density were significantly associated with increased risk for TNBC, whereas later age at menarche, later age at first birth, and breastfeeding were protective against TNBC. Parity, MHT, alcohol, smoking, and BMI were not significantly associated with TNBC overall, but higher parity was associated with higher risk among Black women.

CONCLUSION

Our findings highlight that TNBC has a distinct risk factor profile compared with overall breast cancer. This can be the foundational work in identification of actionable TNBC risk factors to improve prevention and early detection of these poor prognosis breast tumors.

The obese inflammatory microenvironment may promote breast DCIS progression

Introduction

Ductal carcinoma in situ (DCIS), characterized by a proliferation of neoplastic cells confined within the mammary ducts, is distinctly isolated from the surrounding stroma by an almost uninterrupted layer of myoepithelial cells (MECs) and by the basement membrane. Heightened interactions within the adipose microenvironment, particularly in obese patients, may play a key role in the transition from DCIS to invasive ductal carcinoma (IDC), which is attracting growing interest in scientific research. Adipose tissue undergoes metabolic changes in obesity, impacting adipokine secretion and promoting chronic inflammation. This study aimed to assess the interactions between DCIS, including in situ cancer cells and MECs, and the various components of its inflammatory adipose microenvironment (adipocytes and macrophages).

Methods

To this end, a 3D co-culture model was developed using bicellular bi-fluorescent DCIS-like tumoroids, adipose cells, and macrophages to investigate the influence of the inflammatory adipose microenvironment on DCIS progression.

Results

The 3D co-culture model demonstrated an inhibition of the expression of genes involved in apoptosis (BAX, BAG1, BCL2, CASP3, CASP8, and CASP9), and an increase in genes related to cell survival (TP53, JUN, and TGFB1), inflammation (TNF-α, PTGS2, IL-6R), invasion and metastasis (TIMP1 and MMP-9) in cancer cells of the tumoroids under inflammatory conditions versus a non-inflammatory microenvironment. On the contrary, it confirmed the compromised functionality of MECs, resulting in the loss of their protective effects against cancer cells. Adipocytes from obese women showed a significant increase in the expression of all studied myofibroblast-associated genes (myoCAFs), such as FAP and α-SMA. In contrast, adipocytes from normal-weight women expressed markers of inflammatory fibroblast phenotypes (iCAF) characterized by a significant increase in the expression of LIF and inflammatory cytokines such as TNF-α, IL-1β, IL-8, and CXCL-10. These changes also influenced macrophage polarization, leading to a pro-inflammatory M1 phenotype. In contrast, myoCAF-associated adipocytes, and the cancer-promoting microenvironment polarized macrophages towards an M2 phenotype, characterized by high CD163 receptor expression and IL-10 and TGF-β secretion.

Discussion

Reciprocal interactions between the tumoroid and its microenvironment, particularly in obesity, led to transcriptomic changes in adipocytes and macrophages, may participate in breast cancer progression while disrupting the integrity of the MEC layer. These results underlined the importance of adipose tissue in cancer progression.

Disentangling the relationships of body mass index and circulating sex hormone concentrations in mammographic density using Mendelian randomization

PURPOSE

Mammographic density phenotypes, adjusted for age and body mass index (BMI), are strong predictors of breast cancer risk. BMI is associated with mammographic density measures, but the role of circulating sex hormone concentrations is less clear. We investigated the relationship between BMI, circulating sex hormone concentrations, and mammographic density phenotypes using Mendelian randomization (MR).

METHODS

We applied two-sample MR approaches to assess the association between genetically predicted circulating concentrations of sex hormones [estradiol, testosterone, sex hormone-binding globulin (SHBG)], BMI, and mammographic density phenotypes (dense and non-dense area). We created instrumental variables from large European ancestry-based genome-wide association studies and applied estimates to mammographic density phenotypes in up to 14,000 women of European ancestry. We performed analyses overall and by menopausal status.

RESULTS

Genetically predicted BMI was positively associated with non-dense area (IVW: β = 1.79; 95% CI = 1.58, 2.00; p = 9.57 × 10-63) and inversely associated with dense area (IVW: β = - 0.37; 95% CI = - 0.51,- 0.23; p = 4.7 × 10-7). We observed weak evidence for an association of circulating sex hormone concentrations with mammographic density phenotypes, specifically inverse associations between genetically predicted testosterone concentration and dense area (β = - 0.22; 95% CI = - 0.38, - 0.053; p = 0.009) and between genetically predicted estradiol concentration and non-dense area (β =  - 3.32; 95% CI = - 5.83, - 0.82; p = 0.009), although results were not consistent across a range of MR approaches.

CONCLUSION

Our findings support a positive causal association between BMI and mammographic non-dense area and an inverse association between BMI and dense area. Evidence was weaker and inconsistent for a causal effect of circulating sex hormone concentrations on mammographic density phenotypes. Based on our findings, associations between circulating sex hormone concentrations and mammographic density phenotypes are weak at best.

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.

The Associations between Intakes of One-Carbon Metabolism-Related Vitamins and Breast Density among Young Women

BACKGROUND

Folate is the primary methyl donor and B vitamins are cofactors for one-carbon metabolism that maintain DNA integrity and epigenetic signatures implicated in carcinogenesis. Breast tissue is particularly susceptible to stimuli in early life. Only limited data are available on associations of one-carbon metabolism-related vitamin intake during youth and young adulthood with breast density, a strong risk factor for breast cancer.

METHODS

Over 18 years in the DISC and DISC06 Follow-up Study, diets of 182 young women were assessed by three 24-hour recalls on five occasions at ages 8 to 18 years and once at 25 to 29 years. Multivariable-adjusted linear mixed-effects regression was used to examine associations of intakes of one-carbon metabolism-related vitamins with MRI-measured percent dense breast volume (%DBV) and absolute dense breast volume (ADBV) at ages 25 to 29 years.

RESULTS

Folate intake in youth was inversely associated with %DBV (Ptrend = 0.006) and ADBV (Ptrend = 0.02). These inverse associations were observed with intake during post-, though not premenarche. In contrast, premenarche vitamin B2 intake was positively associated with ADBV (Ptrend < 0.001). Young adult folate and vitamin B6 intakes were inversely associated with %DBV (all Ptrend ≤ 0.04), whereas vitamins B6 and B12 were inversely associated with ADBV (all Ptrend ≤ 0.04).

CONCLUSIONS

Among these DISC participants intakes of one-carbon metabolism-related vitamins were associated with breast density. Larger prospective studies among diverse populations are needed to replicate these findings.

IMPACT

Our results suggest the importance of one-carbon metabolism-related vitamin intakes early in life with development of breast density and thereby potentially breast cancer risk later in life.

A Public Health Emergency: Breast Cancer Among Black Communities in the United States

While Black people have a similar incidence of breast cancer compared to White people, they have a 40% increased death rate. Black people are more likely to be diagnosed with aggressive subtypes such as triple-negative breast cancer. However, despite biological factors, systemic racism and social determinants of health create delays in care and barriers to treatment. While genetic testing holds incredible promise for Black people, uptake remains low and results may be challenging to interpret. There is a need for more robust, multidisciplinary, and antiracist interventions to reverse breast cancer-related racial disparities.

Cabergoline as a Novel Strategy for Post-Pregnancy Breast Cancer Prevention in Mice and Human

Post-pregnancy breast cancer often carries a poor prognosis, posing a major clinical challenge. The increasing trend of later-life pregnancies exacerbates this risk, highlighting the need for effective chemoprevention strategies. Current options, limited to selective estrogen receptor modulators, aromatase inhibitors, or surgical procedures, offer limited efficacy and considerable side effects. Here, we report that cabergoline, a dopaminergic agonist, reduces the risk of breast cancer post-pregnancy in a Brca1/P53-deficient mouse model, with implications for human breast cancer prevention. We show that a single dose of cabergoline administered post-pregnancy significantly delayed the onset and reduced the incidence of breast cancer in Brca1/P53-deficient mice. Histological analysis revealed a notable acceleration in post-lactational involution over the short term, characterized by increased apoptosis and altered gene expression related to ion transport. Over the long term, histological changes in the mammary gland included a reduction in the ductal component, decreased epithelial proliferation, and a lower presence of recombinant Brca1/P53 target cells, which are precursors of tumors. These changes serve as indicators of reduced breast cancer susceptibility. Additionally, RNA sequencing identified gene expression alterations associated with decreased proliferation and mammary gland branching. Our findings highlight a mechanism wherein cabergoline enhances the protective effect of pregnancy against breast cancer by potentiating postlactational involution. Notably, a retrospective cohort study in women demonstrated a markedly lower incidence of post-pregnancy breast cancer in those treated with cabergoline compared to a control group. Our work underscores the importance of enhancing postlactational involution as a strategy for breast cancer prevention, and identifies cabergoline as a promising, low-risk option in breast cancer chemoprevention. This strategy has the potential to revolutionize breast cancer prevention approaches, particularly for women at increased risk due to genetic factors or delayed childbirth, and has wider implications beyond hereditary breast cancer cases.

Associations of Vitamins and Related Cofactor Metabolites with Mammographic Breast Density in Premenopausal Women

BACKGROUND

Identifying biological drivers of mammographic breast density (MBD), a strong risk factor for breast cancer, could provide insight into breast cancer etiology and prevention. Studies on dietary factors and MBD have yielded conflicting results. There are, however, very limited data on the associations of dietary biomarkers and MBD.

OBJECTIVE

We aimed to investigate the associations of vitamins and related cofactor metabolites with MBD in premenopausal women.

METHODS

We measured 37 vitamins and related cofactor metabolites in fasting plasma samples of 705 premenopausal women recruited during their annual screening mammogram at the Washington University School of Medicine, St. Louis, MO. Volpara was used to assess volumetric percent density (VPD), dense volume (DV), and nondense volume (NDV). We estimated the least square means of VPD, DV, and NDV across quartiles of each metabolite, as well as the regression coefficient of a metabolite in continuous scale from multiple covariate-adjusted linear regression. We corrected for multiple testing using the Benjamini-Hochberg procedure to control the false discover rate (FDR) at a 5% level.

RESULTS

Participants' mean VPD was 10.5%. Two vitamin A metabolites (β-cryptoxanthin and carotene diol 2) were positively associated, and one vitamin E metabolite (γ-tocopherol) was inversely associated with VPD. The mean VPD increased across quartiles of β-cryptoxanthin (Q1 = 7.2%, Q2 = 7.7%, Q3 = 8.4%%, Q4 = 9.2%; P-trend = 1.77E-05, FDR P value = 1.18E-03). There was a decrease in the mean VPD across quartiles of γ-tocopherol (Q1 = 9.4%, Q2 = 8.1%, Q3 = 8.0%, Q4 = 7.8%; P -trend = 4.01E-03, FDR P value = 0.04). Seven metabolites were associated with NDV: 3 vitamin E (γ-CEHC glucuronide, δ-CEHC, and γ-tocopherol) and 1 vitamin C (gulonate) were positively associated, whereas 2 vitamin A (carotene diol 2 and β-cryptoxanthin) and 1 vitamin C (threonate) were inversely associated with NDV. No metabolite was significantly associated with DV.

CONCLUSION

We report novel associations of vitamins and related cofactor metabolites with MBD in premenopausal women.

Untargeted serum metabolomic profiles and breast density in young women

PURPOSE OF THE STUDY

Breast density is an established risk factor for breast cancer. However, little is known about metabolic influences on breast density phenotypes. We conducted untargeted serum metabolomics analyses to identify metabolic signatures associated with breast density phenotypes among young women.

METHODS

In a cross-sectional study of 173 young women aged 25-29 who participated in the Dietary Intervention Study in Children 2006 Follow-up Study, 449 metabolites were measured in fasting serum samples using ultra-high-performance liquid chromatography-tandem mass spectrometry. Multivariable-adjusted mixed-effects linear regression identified metabolites associated with magnetic resonance imaging measured breast density phenotypes: percent dense breast volume (%DBV), absolute dense breast volume (ADBV), and absolute non-dense breast volume (ANDBV). Metabolite results were corrected for multiple comparisons using a false discovery rate adjusted p-value (q).

RESULTS

The amino acids valine and leucine were significantly inversely associated with %DBV. For each 1 SD increase in valine and leucine, %DBV decreased by 20.9% (q = 0.02) and 18.4% (q = 0.04), respectively. ANDBV was significantly positively associated with 16 lipid and one amino acid metabolites, whereas no metabolites were associated with ADBV. Metabolite set enrichment analysis also revealed associations of distinct metabolic signatures with %DBV, ADBV, and ANDBV; branched chain amino acids had the strongest inverse association with %DBV (p = 0.002); whereas, diacylglycerols and phospholipids were positively associated with ANDBV (p ≤ 0.002), no significant associations were observed for ADBV.

CONCLUSION

Our results suggest an inverse association of branched chain amino acids with %DBV. Larger studies in diverse populations are needed.

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.

Multi-MicroRNA Analysis Can Improve the Diagnostic Performance of Mammography in Determining Breast Cancer Risk

The objective of this study was to determine whether multi-microRNA analysis using a combination of four microRNA biomarkers (miR-1246, 202, 21, and 219B) could improve the diagnostic performance of mammography in determining breast cancer risk by age group (under 50 vs. over 50) and distinguish breast cancer from benign breast diseases and other cancers (thyroid, colon, stomach, lung, liver, and cervix cancers). To verify breast cancer classification performance of the four miRNA biomarkers and whether the model providing breast cancer risk score could distinguish between benign breast disease and other cancers, the model was verified using nonlinear support vector machine (SVM) and generalized linear model (GLM) and age and four miRNA qRT-PCR analysis values (dCt) were input to these models. Breast cancer risk scores for each Breast Imaging-Reporting and Data System (BI-RADS) category in multi-microRNA analysis were analyzed to examine the correlation between breast cancer risk scores and mammography categories. We generated two models using two classification algorithms, SVM and GLM, with a combination of four miRNA biomarkers showing high performance and sensitivities of 84.5% and 82.1%, a specificity of 85%, and areas under the curve (AUCs) of 0.967 and 0.965, respectively, which showed consistent performance across all stages of breast cancer and patient ages. The results of this study showed that this multi-microRNA analysis using the four miRNA biomarkers was effective in classifying breast cancer in patients under the age of 50, which is challenging to accurately diagnose. In addition, breast cancer and benign breast diseases can be classified, showing the possibility of helping with diagnosis by mammography. Verification of the performance of the four miRNA biomarkers confirmed that multi-microRNA analysis could be used as a new breast cancer screening aid to improve the accuracy of mammography. However, many factors must be considered for clinical use. Further validation with an appropriate screening population in large clinical trials is required. This trial is registered with (KNUCH 2022-04-036).

Mammographic Breast Density and Risk of Ovarian Cancer in Korean Women

BACKGROUND

This study aimed to investigate the potential association between mammographic breast density and ovarian cancer risk.

METHODS

This retrospective cohort study included women ≥40 years of age who underwent a mammography screening from 2009 to 2014. Breast density was assessed using the Breast Imaging-Reporting and Data System. The primary outcome was ovarian cancer development, and the cases were recorded until 2020. Cox proportional hazards regression was used to assess the association between breast density and ovarian cancer development. Subgroup analyses stratified by age, menopausal status, and body mass index (BMI) were conducted.

RESULTS

Of the 8,556,914 women included in this study, 9,246 ovarian cancer events were recorded during a median follow-up period of 10 years (interquartile range, 8.1-11.0 years). Compared with women with almost entirely fat density, those with scattered fibroglandular density, heterogeneous density, and extreme density had an increased risk of ovarian cancer with adjusted HRs of 1.08 [95% confidence interval (CI), 1.02-1.15], 1.16 (95% CI, 1.09-1.24), and 1.24 (95% CI, 1.15-1.34), respectively. The strongest association was observed in the ≥60 years age group; subgroup analysis indicated a significant increase in association between the higher-density category and ovarian cancer risk, regardless of BMI or menopausal status.

CONCLUSIONS

Higher levels of breast density are associated with an increased risk of ovarian cancer.

IMPACT

Breast density may have a relationship with ovarian cancer risk and could be used to assess future risk.

Mammographic breast density and cardiovascular disease risk in women

AIMS

We aimed to determine the predictive role of mammographic breast density in addition to the Framingham Risk Score (FRS) on subsequent CVD events in women.

METHODS AND RESULTS

This cohort study included 4,268,579 women aged ≥40 years who underwent mammography screening between 2009 and 2010 with follow-up until 2020. Breast density was reported following the Breast Imaging Reporting and Data System. Primary outcomes included coronary heart disease, cerebrovascular disease, peripheral arterial disease, and heart failure. The incremental predictive ability of breast density added to the FRS model was assessed using the ROC and net reclassification index (NRI) among all women and strata based on FRS risk categories (<5% as low-risk, 5%-10% as moderate-risk, and ≥10% as high-risk). In total, 135,475 CVD events were recorded after a median follow-up of 10.9 years. A lower category of breast density was associated with a higher risk of CVD. Compared to the extremely dense breast group, the hazard ratios (95% CI) for CVDs were 1.12 (1.09-1.14), 1.19 (1.17-1.22), and 1.29 (1.26-1.32) in women with heterogeneously dense, scattered fibroglandular densities, and almost entirely fat breast density, respectively. Adding breast density to the FRS showed a slight improvement in AUROC but a modest improvement in NRI; the C-statistic difference was 0.083% (95% CI 0.069-0.096) with a 7.15% (6.85-7.69) increase in NRI, with the strongest improvement observed in the low-risk group.

CONCLUSIONS

Mammographic breast density is an independent predictor of incident CVD among women. The addition of mammographic breast density to FRS improves the prediction of CVDs, especially in low-risk individuals.

Protocol for a pragmatic stepped wedge cluster randomized clinical trial testing behavioral economic implementation strategies to increase supplemental breast MRI screening among patients with extremely dense breasts

BACKGROUND

Increased breast density augments breast cancer risk and reduces mammography sensitivity. Supplemental breast MRI screening can significantly increase cancer detection among women with dense breasts. However, few women undergo this exam, and screening is consistently lower among racially minoritized populations. Implementation strategies informed by behavioral economics ("nudges") can promote evidence-based practices by improving clinician decision-making under conditions of uncertainty. Nudges directed toward clinicians and patients may facilitate the implementation of supplemental breast MRI.

METHODS

Approximately 1600 patients identified as having extremely dense breasts after non-actionable mammograms, along with about 1100 clinicians involved with their care at 32 primary care or OB/GYN clinics across a racially diverse academically based health system, will be enrolled. A 2 × 2 randomized pragmatic trial will test nudges to patients, clinicians, both, or neither to promote supplemental breast MRI screening. Before implementation, rapid cycle approaches informed by clinician and patient experiences and behavioral economics and health equity frameworks guided nudge design. Clinicians will be clustered into clinic groups based on existing administrative departments and care patterns, and these clinic groups will be randomized to have the nudge activated at different times per a stepped wedge design. Clinicians will receive nudges integrated into the routine mammographic report or sent through electronic health record (EHR) in-basket messaging once their clinic group (i.e., wedge) is randomized to receive the intervention. Independently, patients will be randomized to receive text message nudges or not. The primary outcome will be defined as ordering or scheduling supplemental breast MRI. Secondary outcomes include MRI completion, cancer detection rates, and false-positive rates. Patient sociodemographic information and clinic-level variables will be examined as moderators of nudge effectiveness. Qualitative interviews conducted at the trial's conclusion will examine barriers and facilitators to implementation.

DISCUSSION

This study will add to the growing literature on the effectiveness of behavioral economics-informed implementation strategies to promote evidence-based interventions. The design will facilitate testing the relative effects of nudges to patients and clinicians and the effects of moderators of nudge effectiveness, including key indicators of health disparities. The results may inform the introduction of low-cost, scalable implementation strategies to promote early breast cancer detection.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05787249. Registered on March 28, 2023.

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 density analysis of digital breast tomosynthesis

Mammography shifted to digital breast tomosynthesis (DBT) in the US. An automated percentage of breast density (PD) technique designed for two-dimensional (2D) applications was evaluated with DBT using several breast cancer risk prediction measures: normalized-volumetric; dense volume; applied to the volume slices and averaged (slice-mean); and applied to synthetic 2D images. Volumetric measures were derived theoretically. PD was modeled as a function of compressed breast thickness (CBT). The mean and standard deviation of the pixel values were investigated. A matched case-control (CC) study (n = 426 pairs) was evaluated. Odd ratios (ORs) were estimated with 95% confidence intervals. ORs were significant for PD: identical for volumetric and slice-mean measures [OR = 1.43 (1.18, 1.72)] and [OR = 1.44 (1.18, 1.75)] for synthetic images. A 2nd degree polynomial (concave-down) was used to model PD as a function of CBT: location of the maximum PD value was similar across CCs, occurring at 0.41 × CBT, and PD was significant [OR = 1.47 (1.21, 1.78)]. The means from the volume and synthetic images were also significant [ORs ~ 1.31 (1.09, 1.57)]. An alternative standardized 2D synthetic image was constructed, where each pixel value represents the percentage of breast density above its location. Several measures were significant and an alternative method for constructing a standardized 2D synthetic image was produced.

Causal relationships between breast cancer risk factors based on mammographic features

BACKGROUND

Mammogram risk scores based on texture and density defined by different brightness thresholds are associated with breast cancer risk differently and could reveal distinct information about breast cancer risk. We aimed to investigate causal relationships between these intercorrelated mammogram risk scores to determine their relevance to breast cancer aetiology.

METHODS

We used digitised mammograms for 371 monozygotic twin pairs, aged 40-70 years without a prior diagnosis of breast cancer at the time of mammography, from the Australian Mammographic Density Twins and Sisters Study. We generated normalised, age-adjusted, and standardised risk scores based on textures using the Cirrus algorithm and on three spatially independent dense areas defined by increasing brightness threshold: light areas, bright areas, and brightest areas. Causal inference was made using the Inference about Causation from Examination of FAmilial CONfounding (ICE FALCON) method.

RESULTS

The mammogram risk scores were correlated within twin pairs and with each other (r = 0.22-0.81; all P < 0.005). We estimated that 28-92% of the associations between the risk scores could be attributed to causal relationships between the scores, with the rest attributed to familial confounders shared by the scores. There was consistent evidence for positive causal effects: of Cirrus, light areas, and bright areas on the brightest areas (accounting for 34%, 55%, and 85% of the associations, respectively); and of light areas and bright areas on Cirrus (accounting for 37% and 28%, respectively).

CONCLUSIONS

In a mammogram, the lighter (less dense) areas have a causal effect on the brightest (highly dense) areas, including through a causal pathway via textural features. These causal relationships help us gain insight into the relative aetiological importance of different mammographic features in breast cancer. For example our findings are consistent with the brightest areas being more aetiologically important than lighter areas for screen-detected breast cancer; conversely, light areas being more aetiologically important for interval breast cancer. Additionally, specific textural features capture aetiologically independent breast cancer risk information from dense areas. These findings highlight the utility of ICE FALCON and family data in decomposing the associations between intercorrelated disease biomarkers into distinct biological pathways.

Impact of breast density on diagnostic accuracy in digital breast tomosynthesis versus digital mammography: results from a European screening trial

BACKGROUND

The diagnostic accuracy of digital breast tomosynthesis (DBT) and digital mammography (DM) in breast cancer screening may vary per breast density subgroup. The purpose of this study was to evaluate which women, based on automatically assessed breast density subgroups, have the greatest benefit of DBT compared with DM in the prospective Malmö Breast Tomosynthesis Screening Trial.

MATERIALS AND METHODS

The prospective European, Malmö Breast Tomosynthesis Screening Trial (n = 14,848, Jan. 27, 2010-Feb. 13, 2015) compared one-view DBT and two-view DM, with consensus meeting before recall. Breast density was assessed in this secondary analysis with the automatic software Laboratory for Individualized Breast Radiodensity Assessment. DBT and DM's diagnostic accuracies were compared by breast density quintiles of breast percent density (PD) and absolute dense area (DA) with confidence intervals (CI) and McNemar's test. The association between breast density and cancer detection was analyzed with logistic regression, adjusted for ages < 55 and ≥ 55 years and previous screening participation.

RESULTS

In total, 14,730 women (median age: 58 years; inter-quartile range = 16) were included in the analysis. Sensitivity was higher and specificity lower for DBT compared with DM for all density subgroups. The highest breast PD quintile showed the largest difference in sensitivity and specificity at 81.1% (95% CI 65.8-90.5) versus 43.2% (95% CI 28.7-59.1), p < .001 and 95.5% (95% CI 94.7-96.2) versus 97.2% (95% CI 96.6-97.8), p < 0.001, respectively. Breast PD quintile was also positively associated with cancer detected via DBT at odds ratio 1.24 (95% CI 1.09-1.42, p = 0.001).

CONCLUSION

Women with the highest breast density had the greatest benefit from digital breast tomosynthesis compared with digital mammography with increased sensitivity at the cost of slightly lower specificity. These results may influence digital breast tomosynthesis's use in an individualized screening program stratified by, for instance, breast density.

TRIAL REGISTRATION

Trial registration at https://www.

CLINICALTRIALS

gov : NCT01091545, registered March 24, 2010.

Breast cancer risk stratification using genetic and non-genetic risk assessment tools for 246,142 women in the UK Biobank

PURPOSE

The benefit of using individual risk prediction tools to identify high-risk individuals for breast cancer (BC) screening is uncertain, despite the personalized approach of risk-based screening.

METHODS

We studied the overlap of predicted high-risk individuals among 246,142 women enrolled in the UK Biobank. Risk predictors assessed include the Gail model (Gail), BC family history (FH, binary), BC polygenic risk score (PRS), and presence of loss-of-function (LoF) variants in BC predisposition genes. Youden J-index was used to select optimal thresholds for defining high-risk.

RESULTS

In total, 147,399 were considered at high risk for developing BC within the next 2 years by at least 1 of the 4 risk prediction tools examined (Gail2-year > 0.5%: 47%, PRS2-yea r > 0.7%: 30%, FH: 6%, and LoF: 1%); 92,851 (38%) were flagged by only 1 risk predictor. The overlap between individuals flagged as high-risk because of genetic (PRS) and Gail model risk factors was 30%. The best-performing combinatorial model comprises a union of high-risk women identified by PRS, FH, and, LoF (AUC2-year [95% CI]: 62.2 [60.8 to 63.6]). Assigning individual weights to each risk prediction tool increased discriminatory ability.

CONCLUSION

Risk-based BC screening may require a multipronged approach that includes PRS, predisposition genes, FH, and other recognized risk factors.

The Correlation Between Progesterone and Mammographic Density in Postmenopausal Women: A Systematic Review of the Literature and Meta-Analysis

Higher mammographic breast density in premenopausal and postmenopausal women is related to a higher breast cancer risk. In this review, we analyze the correlation between estrogen, progesterone, and mammographic density in postmenopausal women and clarify whether these findings are consistent across different types of mammographic breast density. We extracted data concerning mammographic density increases in the populations treated with estrogen-only hormone replacement therapy and those treated with estrogen and progestin hormone replacement therapy. Postmenopausal women treated with estrogen and progesterone regimens had a statistically significant lesser mammographic density increase than estrogen-only hormone replacement therapy regimens.

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.

Prenatal airborne polycyclic aromatic hydrocarbon exposure, altered regulation of peroxisome proliferator-activated receptor gamma (Ppar)γ, and links with mammary cancer

Environmental exposure to polycyclic aromatic hydrocarbons (PAH) has been shown to be associated with chronic disease outcomes through multiple mechanisms including altered regulation of the transcription factor peroxisome proliferator-activated receptor gamma (Ppar) γ. Because PAH exposure and Pparγ each have been associated with mammary cancer, we asked whether PAH would induce altered regulation of Pparγ in mammary tissue, and whether this association may underlie the association between PAH and mammary cancer. Pregnant mice were exposed to aerosolized PAH at proportions that mimic equivalent human exposures in New York City air. We hypothesized that prenatal PAH exposure would alter Pparγ DNA methylation and gene expression and induce the epithelial to mesenchymal transition (EMT) in mammary tissue of offspring (F1) and grandoffspring (F2) mice. We also hypothesized that altered regulation of Pparγ in mammary tissue would associate with biomarkers of EMT, and examined associations with whole body weight. We found that prenatal PAH exposure lowered Pparγ mammary tissue methylation among grandoffspring mice at postnatal day (PND) 28. However, PAH exposure did not associate with altered Pparγ gene expression or consistently with biomarkers of EMT. Finally, lower Pparγ methylation, but not gene expression, was associated with higher body weight among offspring and grandoffspring mice at PND28 and PND60. Findings suggest additional evidence of multi-generational adverse epigenetic effects of prenatal PAH exposure among grandoffspring mice.

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.

Automated Estimation of Mammary Gland Content Ratio Using Regression Deep Convolutional Neural Network and the Effectiveness in Clinical Practice as Explainable Artificial Intelligence

Recently, breast types were categorized into four types based on the Breast Imaging Reporting and Data System (BI-RADS) atlas, and evaluating them is vital in clinical practice. A Japanese guideline, called breast composition, was developed for the breast types based on BI-RADS. The guideline is characterized using a continuous value called the mammary gland content ratio calculated to determine the breast composition, therefore allowing a more objective and visual evaluation. Although a discriminative deep convolutional neural network (DCNN) has been developed conventionally to classify the breast composition, it could encounter two-step errors or more. Hence, we propose an alternative regression DCNN based on mammary gland content ratio. We used 1476 images, evaluated by an expert physician. Our regression DCNN contained four convolution layers and three fully connected layers. Consequently, we obtained a high correlation of 0.93 (p < 0.01). Furthermore, to scrutinize the effectiveness of the regression DCNN, we categorized breast composition using the estimated ratio obtained by the regression DCNN. The agreement rates are high at 84.8%, suggesting that the breast composition can be calculated using regression DCNN with high accuracy. Moreover, the occurrence of two-step errors or more is unlikely, and the proposed method can intuitively understand the estimated results.

Genome-Wide Association Study of Breast Density among Women of African Ancestry

Breast density, the amount of fibroglandular versus fatty tissue in the breast, is a strong breast cancer risk factor. Understanding genetic factors associated with breast density may help in clarifying mechanisms by which breast density increases cancer risk. To date, 50 genetic loci have been associated with breast density, however, these studies were performed among predominantly European ancestry populations. We utilized a cohort of women aged 40-85 years who underwent screening mammography and had genetic information available from the Penn Medicine BioBank to conduct a Genome-Wide Association Study (GWAS) of breast density among 1323 women of African ancestry. For each mammogram, the publicly available "LIBRA" software was used to quantify dense area and area percent density. We identified 34 significant loci associated with dense area and area percent density, with the strongest signals in GACAT3, CTNNA3, HSD17B6, UGDH, TAAR8, ARHGAP10, BOD1L2, and NR3C2. There was significant overlap between previously identified breast cancer SNPs and SNPs identified as associated with breast density. Our results highlight the importance of breast density GWAS among diverse populations, including African ancestry populations. They may provide novel insights into genetic factors associated with breast density and help in elucidating mechanisms by which density increases breast cancer risk.

Lifetime changes in body fatness and breast density in postmenopausal women: the FEDRA study

BACKGROUND

High mammographic breast density (MBD) is an established risk factor for breast cancer (BC). Body fatness conveys an increased BC risk in postmenopause but is associated with less dense breasts. Here, we studied the relationship between body fatness and breast composition within the FEDRA (Florence-EPIC Digital mammographic density and breast cancer Risk Assessment) longitudinal study.

METHODS

Repeated anthropometric data and MBD parameters (obtained through an automated software on BC screening digital mammograms) were available for all participants, as well as information on other BC risk factors. Multivariate linear regression and functional data analysis were used to longitudinally evaluate the association of body fatness, and changes thereof over time, with dense (DV) and non-dense (NDV) breast volumes and volumetric percent density (VPD).

RESULTS

A total of 5,262 women were included, with anthropometric data available at 20 and 40 years of age, at EPIC baseline (mean 49.0 years), and an average of 9.4 years thereafter. The mean number of mammograms per woman was 3.3 (SD 1.6). Body fatness (and increases thereof) at any age was positively associated with DV and NDV (the association being consistently stronger for the latter), and inversely associated with VPD. For instance, an increase by 1 kg/year between the age of 40 years and EPIC baseline was significantly associated with 1.97% higher DV, 8.85% higher NDV, and 5.82% lower VPD.

CONCLUSION

Body fatness and its increase from young adulthood until midlife are inversely associated with volumetric percent density, but positively associated with dense and non-dense breast volumes in postmenopausal women.

Associations of alcohol consumption with breast tissue composition

BACKGROUND

We investigated the associations of alcohol with percentage of epithelium, stroma, fibroglandular tissue (epithelium + stroma), and fat in benign breast biopsy samples.

METHODS

We included 857 cancer-free women with biopsy-confirmed benign breast disease within the Nurses' Health Study (NHS) and NHSII cohorts. Percentage of each tissue was measured on whole slide images using a deep-learning algorithm and then log-transformed. Alcohol consumption (recent and cumulative average) was assessed with semi-quantitative food frequency questionnaires. Regression estimates were adjusted for known breast cancer risk factors. All tests were 2-sided.

RESULTS

Alcohol was inversely associated with % of stroma and fibroglandular tissue (recent ≥ 22 g/day vs. none: stroma: β = - 0.08, 95% Confidence Interval [CI] - 0.13; - 0.03; fibroglandular: β = - 0.08, 95% CI - 0.13; - 0.04; cumulative ≥ 22 g/day vs. none: stroma: β = - 0.08, 95% CI - 0.13; - 0.02; fibroglandular: β = - 0.09, 95% CI - 0.14; - 0.04) and positively associated with fat % (recent ≥ 22 g/day vs. none: β = 0.30, 95% CI 0.03; 0.57; cumulative ≥ 22 g/day vs. none: β = 0.32, 95% CI 0.04; 0.61). In stratified analysis, alcohol consumption was not associated with tissue measures in premenopausal women. In postmenopausal women, cumulative alcohol use was inversely associated with % of stroma and fibroglandular tissue and positively associated with fat % (≥ 22 g/day vs. none: stroma: β = - 0.16, 95% CI - 0.28; - 0.07; fibroglandular: β = - 0.18, 95% CI - 0.28; - 0.07; fat: β = 0.61, 95% CI 0.01; 1.22), with similar results for recent alcohol use.

CONCLUSION

Our findings suggest that alcohol consumption is associated with smaller % of stroma and fibroglandular tissue and a greater % of fat in postmenopausal women. Future studies are warranted to confirm our findings and to elucidate the underlying biological mechanisms.

Breast density and risk of breast cancer

Early studies reported a 4- to 6-fold risk of breast cancer between women with extremely dense and fatty breasts. As most early studies were case-control studies, we took advantage of a population-based screening program to study density and breast cancer incidence in a cohort design. In the Capital Region, Denmark, women aged 50 to 69 are invited to screening biennially. Women screened November 2012 to December 2017 were included, and classified by BI-RADS density code, version 4, at first screen after recruitment. Women were followed up for incident breast cancer, including ductal carcinoma in situ (DCIS), to 2020 in nationwide pathology data. Rate ratios (RRs) and 95% confidence intervals (CI) were compared across density groups using Poisson-regression. We included 189 609 women; 1 067 282 person-years; and 4110 incident breast cancers/DCIS. Thirty-three percent of women had BI-RADS density code 1; 38% code 2; 24% code 3; 4.7% code 4; and missing 0.3%. Using women with BI-RADS density code 1 as baseline; women with code 2 had RR 1.69 (95% CI 1.56-1.84); women with code 3, RR 2.06 (95% CI 1.89-2.25); and women with code 4, RR 2.37 (95% CI 1.05-2.74). Results differed between observations accumulated during screening and above screening age. Our results indicated less difference in breast cancer risk across level of breast density than normally stated. Translated into absolute risk of breast cancer after age 50, we found a 6.2% risk for the one-third of women with lowest density, and 14.7% for the 5% of women with highest density.

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.

Breast Density Analysis Using Digital Breast Tomosynthesis

We evaluated an automated percentage of breast density (BD) technique (PDa) with digital breast tomosynthesis (DBT) data. The approach is based on the wavelet expansion followed by analyzing signal dependent noise. Several measures were investigated as risk factors: normalized volumetric; total dense volume; average of the DBT slices (slice-mean); a two-dimensional (2D) metric applied to the synthetic images; and the mean and standard deviations of the pixel values. Volumetric measures were derived theoretically, and PDa was modeled as a function of compressed breast thickness. An alternative method for constructing synthetic 2D mammograms was investigated using the volume results. A matched case-control study (n = 426 pairs) was analyzed. Conditional logistic regression modeling, controlling body mass index and ethnicity, was used to estimate odds ratios (ORs) for each measure with 95% confidence intervals provided parenthetically. There were several significant findings: volumetric measure [OR = 1.43 (1.18, 1.72)], which produced an identical OR as the slice-mean measure as predicted; [OR =1.44 (1.18, 1.75)] when applied to the synthetic images; and mean of the pixel values (volume or 2D synthetic) [ORs ~ 1.31 (1.09, 1.57)]. PDa was modeled as 2nd degree polynomial (concave-down): its maximum value occurred at 0.41×(compressed breast thickness), which was similar across case-control groups, and was significant from this position [OR = 1.47 (1.21, 1.78)]. A standardized 2D synthetic image was produced, where each pixel value represents the percentage of BD above its location. The significant findings indicate the validity of the technique. Derivations supported by empirical analyses produced a new synthetic 2D standardized image technique. Ancillary to the objectives, the results provide evidence for understanding the percentage of BD measure applied to 2D mammograms. Notwithstanding the findings, the study design provides a template for investigating other measures such as texture.

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.

MiXcan: a framework for cell-type-aware transcriptome-wide association studies with an application to breast cancer

Human bulk tissue samples comprise multiple cell types with diverse roles in disease etiology. Conventional transcriptome-wide association study approaches predict genetically regulated gene expression at the tissue level, without considering cell-type heterogeneity, and test associations of predicted tissue-level expression with disease. Here we develop MiXcan, a cell-type-aware transcriptome-wide association study approach that predicts cell-type-level expression, identifies disease-associated genes via combination of cell-type-level association signals for multiple cell types, and provides insight into the disease-critical cell type. As a proof of concept, we conducted cell-type-aware analyses of breast cancer in 58,648 women and identified 12 transcriptome-wide significant genes using MiXcan compared with only eight genes using conventional approaches. Importantly, MiXcan identified genes with distinct associations in mammary epithelial versus stromal cells, including three new breast cancer susceptibility genes. These findings demonstrate that cell-type-aware transcriptome-wide analyses can reveal new insights into the genetic and cellular etiology of breast cancer and other diseases.

Mammographic Density Reduction is Associated to the Prognosis in Asian Breast Cancer Patients Receiving Hormone Therapy

INTRODUCTION

Using mammographic density as a significant biomarker for predicting prognosis in adjuvant hormone therapy patients is controversial due to the conflicting results of recent studies. This study aimed to evaluate hormone therapy-induced mammographic density reduction and its association with prognosis in Taiwanese patients.

METHODS

In this retrospective study, 1941 patients with breast cancer were screened, and 399 patients with estrogen receptor-positive breast cancer who received adjuvant hormone therapy were enrolled. The mammographic density was measured using a fully automatic estimation procedure based on full-field digital mammography. The prognosis included relapse and metastasis during treatment follow-up. The Kaplan-Meier method and Cox proportional hazards model were used for disease-free survival analysis.

RESULTS

A mammographic density reduction rate >20.8%, measured preoperatively and after receiving hormone therapy from 12-18 months, was a significant threshold for predicting prognosis in patients with breast cancer. The disease-free survival rate was significantly higher in patients whose mammographic density reduction rate was >20.8% (P = .048).

CONCLUSION

This study's findings could help estimate the prognosis for patients with breast cancer and may improve the quality of adjuvant hormone therapy after enlarging the study cohort in the future.

Prediction of breast cancer risk by automated volumetric breast density measurement

PURPOSE

Dense breast (DB) is recognized as a breast cancer (BC) risk factor. Although DB is common in Japanese women, the incidence of BC is lower than in Caucasians. We evaluated whether DB is a risk factor or whether there are other risk factors for BC in Japanese women.

MATERIALS AND METHODS

We retrospectively analyzed 635 BC patients and 999 controls who received a mammography at our hospital between February 2019 and March 2021. Volumetric breast density percentage (VBD%), breast volume (BV), and fibroglandular volume (FGV) were measured using Volpara™, an automated, three-dimensional image analysis program. A VBD% of 7.5% or higher was classified as DB. The association between the VBD%, BV, and FGV, and BC risk were assessed using logistic regression.

RESULTS

Of the BC group and the control group, 77% and 79% had DB. The stratified FGV was positively associated with BC risk (odds ratio: 2.84; 95% confidence interval 1.58-5.12; P < 0.001). No significant association was found between either the VBD% or BV and BC risk.

CONCLUSION

The proportion of Japanese women with DB was high, suggesting that DB might not be significantly associated with BC risk. However, our results also suggested that the FGV may be related to BC risk in Japanese women.

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.

Association of body composition fat parameters and breast density in mammography by menopausal status

We investigated the relationship between body fat-driven obesity and breast fat density in mammography according to menopausal status. We retrospectively analyzed 8537 women (premenopausal, n = 4351; postmenopausal, n = 4186). Body fat parameters included BMI (body mass index), waist circumference (WC), waist-hip ratio (WHR), fat mass index (FMI), Percentage of body fat (PBF), and visceral fat area (VFA). Body fat-driven obesity was defined as follows: overall obesity, BMI ≥ 25 kg/m²; central obesity, WC > 85 cm; abdominal obesity, WHR > 0.85; excessive FMI, the highest quartile (Q4) of FMI; excessive PBF, the highest quartile (Q4) of VFA; visceral obesity, and the highest quartile (Q4) of VFA). Breast density was classified according to BI-RADS (grade a, b, c, and d), which defined as an ordinal scale (grade a = 1, grade b = 2, grade c = 3, and grade d = 4). All body fat-driven obesity parameters were negatively associated with the grade of breast density in both groups of women (p < 0.001): The more fatty parameters are, the less dense breast is. In multivariable binary logistic regression, all body fat-driven obesity parameters also showed a negative association with grade d density (vs. grade a, b, or c). In premenopausal women, BMI was a more associated parameter with grade d density than those of the other fat-driven parameters (OR 0.265, CI 0.204-0.344). In postmenopausal women, WC was more associated with grade d density than the others (OR 0.315, CI 0.239-0.416). We found that BMI, WC, WHR, FMI, PBF and VFA were negatively correlated with dense breast, and the association degree pattern between body fat-driven obesity and dense breast differs according to menopausal status.

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.

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Gestational Hypertensive Disorders and Maternal Breast Cancer Risk in a Nationwide Cohort of 40,720 Parous Women

BACKGROUND

Preeclampsia and gestational hypertension are hypothesized to be associated with reduced maternal breast cancer risk, but the epidemiologic evidence is inconclusive. Our objective was to examine associations between gestational hypertensive disorders and breast cancer in a nationwide cohort of women with a family history of breast cancer.

METHODS

Women ages 35-74 years who had a sister previously diagnosed with breast cancer, but had never had breast cancer themselves, were enrolled in the Sister Study from 2003 to 2009 (N = 50,884). At enrollment, participants reported diagnoses of eclampsia, preeclampsia, or gestational hypertension in each pregnancy. We used Cox proportional hazards models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between history of a gestational hypertensive disorder and incident invasive breast cancer or ductal carcinoma in situ among 40,720 parous women. We used age as the time scale and adjusted for birth cohort, race-ethnicity, and reproductive, socioeconomic, and behavioral factors. We examined effect measure modification by risk factors for gestational hypertensive disease and breast cancer and assessed possible etiologic heterogeneity across tumor characteristics.

RESULTS

The prevalence of gestational hypertensive disease was 12%. During follow-up (mean = 10.9 years), 3,198 eligible women self-reported a breast cancer diagnosis. History of a gestational hypertensive disorder was not associated with breast cancer risk (HR = 1.0; 95% CI = 0.90, 1.1). We did not observe clear evidence of effect measure modification or etiologic heterogeneity.

CONCLUSIONS

History of a gestational hypertensive disorder was not associated with breast cancer risk in a cohort of women with a first-degree family history of breast cancer.

Family history of breast cancer, mammographic breast density and breast cancer risk: Findings from a cohort study of Korean women

BACKGROUND

This study investigated whether the association between family history of breast cancer in first-degree relatives and breast cancer risk varies by breast density.

METHODS

Women aged 40 years and older who underwent screening between 2009 and 2010 were followed up until 2020. Family history was assessed using a self-reported questionnaire. Using Breast Imaging Reporting and Data System (BI-RADS), breast density was categorized into dense breast (heterogeneously or extremely dense) and non-dense breast (almost entirely fatty or scattered areas of fibro-glandular). Cox regression model was used to assess the association between family history and breast cancer risk.

RESULTS

Of the 4,835,507 women, 79,153 (1.6%) reported having a family history of breast cancer and 77,238 women developed breast cancer. Family history led to an increase in the 5-year cumulative incidence in women with dense- and non-dense breasts. Results from the regression model with and without adjustment for breast density yielded similar HRs in all age groups, suggesting that breast density did not modify the association between family history and breast cancer. After adjusting for breast density and other factors, family history of breast cancer was associated with an increased risk of breast cancer in all three age groups (age 40-49 years: aHR 1.96, 95% confidence interval [CI] 1.85-2.08; age 50-64 years: aHR 1.70, 95% CI 1.58-1.82, and age ≥65 years: aHR 1.95, 95% CI 1.78-2.14).

CONCLUSION

Family history of breast cancer and breast density are independently associated with breast cancer. Both factors should be carefully considered in future risk prediction models of breast cancer.

Breast Cancer in Asia: Incidence, Mortality, Early Detection, Mammography Programs, and Risk-Based Screening Initiatives

Close to half (45.4%) of the 2.3 million breast cancers (BC) diagnosed in 2020 were from Asia. While the burden of breast cancer has been examined at the level of broad geographic regions, literature on more in-depth coverage of the individual countries and subregions of the Asian continent is lacking. This narrative review examines the breast cancer burden in 47 Asian countries. Breast cancer screening guidelines and risk-based screening initiatives are discussed.

The relationship of breast cancer deaths with age groups and urbanization of the population: a multi-country analysis

Aim: In this retrospective observational study, it was aimed to evaluate the relationship between breast cancer deaths and demographic properties of countries. Material and Method: The research was conducted on World Health Organization (WHO) 10th International Classification of Diseases (ICD-10) mortality list and World Bank Country Reports (WBCR). Total breast cancer-related deaths, age groups and urban population rates of 14 countries between 1996 and 2017 were evaluated. Results: Both uncontrolled and controlled correlation analysis results showed that population age distribution had a significant correlation with total breast cancer-related deaths (p0.05). Generalized Linear Model (GLM) results showed that only the country had a significant effect on total breast cancer related deaths (p0.05). Conclusion: Although reasons such as age and urbanization play an important role among breast cancer risk factors, it is found that they do not affect mortality rates. A total of 22 years of WHO data and 14 country results showed that deaths due to breast cancer are only related to the country. Therefore, countries can minimize deaths due to breast cancer by carrying out more effective struggles, early diagnosis, treatment and awareness activities.

Mammographic breast density and breast cancer risk in the Saudi population: a case-control study using visual and automated methods

OBJECTIVE

This study aims to establish risk of breast cancer based on breast density among Saudi women and to compare cancer prediction using different breast density methods.

METHODS

1140 pseudonymised screening mammograms from Saudi females were retrospectively collected. Breast density was assessed using Breast Imaging Reporting and Data System (BI-RADS) density categories and visual analogue scale (VAS) of 285 cases and 855 controls matched on age and body mass index. In a subset of 160 cases and 480 controls density was estimated by two automated methods, Volpara Density™ and predicted VAS (pVAS). Odds ratios (ORs) between the highest and second categories in BI-RADS and Volpara density grades, and highest vs lowest quartiles in VAS, pVAS and Volpara Density™, were estimated using conditional logistic regression.

RESULTS

For BI-RADS, the OR was 6.69 (95% CI 2.79-16.06) in the highest vs second category and OR = 4.78 (95% CI 3.01-7.58) in the highest vs lowest quartile for VAS. In the subset, VAS was the strongest predictor OR = 7.54 (95% CI 3.86-14.74), followed by pVAS using raw images OR = 5.38 (95% CI 2.68-10.77) and Volpara Density ™ OR = 3.55, (95% CI 1.86-6.75) for highest vs lowest quartiles. The matched concordance index for VAS was 0.70 (95% CI 0.65-0.75) demonstrating better discrimination between cases and controls than all other methods.

CONCLUSION

Increased mammographic density was strongly associated with risk of breast cancer among Saudi women. Radiologists' visual assessment of breast density is superior to automated methods. However, pVAS and Volpara Density ™ also significantly predicted breast cancer risk based on breast density.

ADVANCES IN KNOWLEDGE

Our study established an association between breast density and breast cancer in a Saudi population and compared the performance of automated methods. This provides a stepping-stone towards personalised screening using automated breast density methods.

Body mass index, breast density, and the risk of breast cancer development in relation to the menopausal status; results from a population-based screening program in a native African-Arab country

Background

Risk factors are traits or behaviors that have an influence on the development of breast cancer (BC). Awareness of the prevalent risk factors can guide in developing prevention interventions.

Purpose

To evaluate the correlation between the breast density, body mass index, and the risk of breast cancer development in relation to the menopausal status in a native African-Arab population.

Material and methods

The study included 30,443 screened females who were classified into cancer and non-cancer groups and each group was further sub-classified into pre- and postmenopausal groups. The breast density (BD) was reported and subjectively classified according to the 2013 ACR BI-RADS breast density classification. The weight and height were measured, and the body mass index (BMI) was calculated and classified according to the WHO BMI classification.

Results

A statistically significant difference was calculated between the mean BMI in the cancer and non-cancer groups (p: .027) as well as between the pre- and postmenopausal groups (p < .001). A positive statistically insignificant correlation was calculated between the breast density and the risk of breast cancer in the premenopausal group (OR: 1.062, p: .919) and a negative highly significant correlation was calculated in the postmenopausal group (OR: 0.234, p < .001).

Conclusion

BMI and BD are inversely associated with each other. The current studied population presented unique ethnic characteristics, where a decreased BD and an increased BMI were found to be independent risk factors for developing breast cancer.

Synthesis of a theranostic platform based on fibrous silica nanoparticles for the enhanced treatment of triple-negative breast cancer promoted by a combination of chemotherapeutic agents

A new series of theranostic silica materials based on fibrous silica particles acting as nanocarriers of two different cytotoxic agents, namely, chlorambucil and an organotin metallodrug have been prepared and structurally characterized. Besides the combined therapeutic activity, these platforms have been decorated with a targeting molecule (folic acid, to selectively target triple negative breast cancer) and a molecular imaging agent (Alexa Fluor 647, to enable their tracking both in vitro and in vivo). The in vitro behaviour of the multifunctional silica systems showed a synergistic activity of the two chemotherapeutic agents in the form of an enhanced cytotoxicity against MDA-MB-231 cells (triple negative breast cancer) as well as by a higher cell migration inhibition. Subsequently, the in vivo applicability of the siliceous nanotheranostics was successfully assessed by observing with in vivo optical imaging techniques a selective tumour accumulation (targeting ability), a marked inhibition of tumour growth paired to a marked antiangiogenic ability after 13 days of systemic administration, thus, confirming the enhanced theranostic activity. The systemic nanotoxicity was also evaluated by analyzing specific biochemical markers. The results showed a positive effect in form of reduced cytotoxicity when both chemotherapeutics are administered in combination thanks to the fibrous silica nanoparticles. Overall, our results confirm the promising applicability of these novel silica-based nanoplatforms as advanced drug-delivery systems for the synergistic theranosis of triple negative breast cancer.

Mammographic Breast Density, Benign Breast Disease, and Subsequent Breast Cancer Risk in 3.9 Million Korean Women

Background Mammographic breast density and benign breast disease are strong risk factors for breast cancer. Accordingly, women with both risk factors may have a markedly high risk for developing breast cancer. Purpose To investigate the risk of breast cancer associated with the combination of mammographic density and benign breast disease in Korean women, where population-based mammographic breast cancer screening is provided for all women aged at least 40 years. Materials and Methods This is a retrospective analysis of data from a nationwide breast cancer screening program linked with the national cancer registry. The study included Korean women between 40-74 years of age screened for breast cancer between January 2009 and December 2010 and observed up to December 2020 (median follow-up of 10.6 years). Benign breast disease and breast density were extracted from mammography screening results. Cox proportional hazard regression analysis was used to calculate adjusted hazard ratios (HRs) for breast cancer risk. Results In this study, 3 911 348 women (mean age, 53 years ± 9 [SD]) were analyzed. During follow-up (median, 10.6 years), 58 321 women developed breast cancer. At screening, 10 729 (18.4%) cases of benign breast disease were detected among women who developed breast cancer. Women with extremely dense breasts (Breast Imaging Reporting and Data System [BI-RADS] density category D) and benign breast disease had a greater risk of breast cancer when compared with women presenting with fatty breast (BI-RADS density category A) and those without benign breast disease (HR, 2.75; 95% CI: 2.63, 2.88; P < .001). Women with benign breast disease and fatty breasts (HR, 1.49; 95% CI: 1.40, 1.58; P < .001) and women with extremely dense breasts and without benign breast disease (HR, 2.28; 95% CI: 2.20, 2.35; P < .001) also had an elevated breast cancer risk compared with women with fatty breast tissue. Conclusion Women with dense breasts and benign breast disease at screening mammography had an elevated risk of future breast cancer. © RSNA, 2022 Online supplemental material is available for this article.