Mammographic Density Research Articles

Metabolites and lipid species mediate the associations of adiposity in childhood and early adulthood with mammographic breast density in premenopausal women

BackgroundMammographic breast density (MBD), a strong predictor of breast cancer, is highly influenced by body mass index (BMI) in childhood and early adulthood, but the mechanisms underlying these associations are not fully understood. Our goal is to identify biomarkers that mediate the associations of BMI at ages 10 and 18 with MBD in premenopausal women.MethodsThis study consists of 705 premenopausal women who had their screening mammogram at Washington University in St. Louis, MO, and provided a fasting blood sample. Our comprehensive metabolomic and lipidomic profiling yielded complete data for 828 metabolites and 857 lipid species after imputation. We used Volpara to determine volumetric measures of MBD. We performed high dimensional mediation analysis using the HIMA R package, adjusted for confounders, to determine whether lipid species and metabolites mediate the associations of BMI at 10 and 18 with MBD. We applied a false discovery rate (FDR) p-value < 0.1.ResultsFour metabolites (glutamate, β-cryptoxanthin, cortolone glucuronide (1), phytanate) significantly mediated the association of BMI at 10 with volumetric percent density (VPD), and two (glutamate, β-cryptoxanthin) mediated the association of BMI at 18 with VPD. Glutamate was the strongest mediator across time points. Glutamate mediated 6.7% (FDR p-value = 0.06) and 9.3% (FDR p-value = 0.008) of the association between BMI at age 10 and 18, respectively. Four lipid species (CER(18:0), LCER(14:0), LPC(18:1), PC(18:1/18:1)), mediated the association of BMI at 10 with VPD, while five lipid species (CER(18:0), LCER(14:0), PC(18:1/18:1), TAG56:5-FA22:5, TAG52:2-FA16:0) mediated the association of BMI at 18 with VPD. The strongest mediator was PC(18:1/18:1), which mediated 9.7%, (FDR-p = 0.009) and 7.7%, (FDR-p = 0.04) of the association of BMI at age 10 and 18 with VPD, respectively.ConclusionsMetabolites in amino acid, lipid, cofactor/vitamin, and xenobiotic super-pathways as well as lipid species across the phospholipid, neutral complex lipid and sphingolipid super-pathways mediated the associations of BMI in early-life and MBD in premenopausal women. This study offers insight into the biological mechanisms underlying the link between early-life adiposity and MBD, which can support future research into breast cancer prevention.

Age at First Full-term Pregnancy and Other Reproductive Factors Are Associated with Mammographic Breast Density in Postmenopausal Women: A Study in Flanders, Belgium.

We consider our findings to be highly novel, and to the best of our knowledge, our study is one of the first to investigate associations between age at FFTP and three markers of MBD (GLAND, VBD, and BI-RADS), uncovering that MBD is significantly higher when FFTP occurs after 25.7 years.

A Deep Learning Approach for the Classification of Fibroglandular Breast Density in Histology Images of Human Breast Tissue

Background: To progress research into the biological mechanisms that link mammographic breast density to breast cancer risk, fibroglandular breast density can be used as a surrogate measure. This study aimed to develop a computational tool to classify fibroglandular breast density in hematoxylin and eosin (H&amp;E)-stained breast tissue sections using deep learning approaches that would assist future mammographic density research. Methods: Four different architectural configurations of transferred MobileNet-v2 convolutional neural networks (CNNs) and four different models of vision transformers were developed and trained on a database of H&amp;E-stained normal human breast tissue sections (965 tissue blocks from 93 patients) that had been manually classified into one of five fibroglandular density classes, with class 1 being very low fibroglandular density and class 5 being very high fibroglandular density. Results: The MobileNet-Arc 1 and ViT model 1 achieved the highest overall F1 scores of 0.93 and 0.94, respectively. Both models exhibited the lowest false positive rate and highest true positive rate in class 5, while the most challenging classification was class 3, where images from classes 2 and 4 were mistakenly classified as class 3. The area under the curves (AUCs) for all classes were higher than 0.98. Conclusions: Both the ViT and MobileNet models showed promising performance in the accurate classification of H&amp;E-stained tissue sections across all five fibroglandular density classes, providing a rapid and easy-to-use computational tool for breast density analysis.

External validation of a multivariable prediction model for positive resection margins in breast-conserving surgery

ObjectivesPositive resection margins after breast-conserving surgery (BCS) most often demands a repeat surgery. To preoperatively identify patients at risk of positive margins, a multivariable model has been developed that predicts positive margins after BCS with a high accuracy. This study aimed to externally validate this prediction model to explore its generalizability and assess if additional preoperatively available variables can further improve its predictive accuracy. The validation cohort included 225 patients with invasive breast cancer who underwent BCS at Aarhus University Hospital, Aarhus, Denmark during 2020–2022. Receiver operating characteristic (ROC) and calibration analysis were used to validate the prediction model. Univariable logistic regression was used to evaluate if additional variables available in the validation cohort were associated with positive margins and backward elimination to explore if these variables could further improve the model´s predictive accuracy.ResultsThe AUC of the model was 0.60 (95% CI: 0.50–0.70) indicating a lower discriminative capacity in the external cohort. We found weak evidence for an association between increased preoperative breast density on mammography and positive resection margins after BCS (p = 0.027), but the AUC of the model did not improve, when mammographic breast density was included as an additional variable in the model.

Machine Learning-Based Approaches for Breast Density Estimation from Mammograms: A Comprehensive Review

Breast cancer, as of 2022, is the most prevalent type of cancer in women. Breast density—a measure of the non-fatty tissue in the breast—is a strong risk factor for breast cancer that can be estimated from mammograms. The importance of studying breast density is twofold. First, high breast density can be a factor in lowering mammogram sensitivity, as dense tissue can mask tumors. Second, higher breast density is associated with an increased risk of breast cancer, making accurate assessments vital. This paper presents a comprehensive review of the mammographic density estimation literature, with an emphasis on machine-learning-based approaches. The approaches reviewed can be classified as visual, software-, machine learning-, and segmentation-based. Machine learning methods can be further broken down into two categories: traditional machine learning and deep learning approaches. The most commonly utilized models are support vector machines (SVMs) and convolutional neural networks (CNNs), with classification accuracies ranging from 76.70% to 98.75%. Major limitations of the current works include subjectivity and cost-inefficiency. Future work can focus on addressing these limitations, potentially through the use of unsupervised segmentation and state-of-the-art deep learning models such as transformers. By addressing the current limitations, future research can pave the way for more reliable breast density estimation methods, ultimately improving early detection and diagnosis.

Green Tea's EGCG: Brewing Hope in the Battle against Breast Cancer

Breast cancer, a pervasive global malignancy, is anticipated to undergo a significant increase by 2040. Despite the conventional armamentarium of treatments including chemotherapy, radiation therapy, and surgery, the intricate landscape of breast cancer, characterized by its multifaceted surface receptors and signalling pathways, presents formidable challenges to treatment efficacy. Epigallocatechin-3-gallate (EGCG), extracted from Camellia sinensis, has emerged as a subject of interest due to its robust antioxidative properties stemming from its chemical structure. EGCG exerts its effects on pivotal stages of tumour growth and proliferation by modulating key signalling pathways such as MAPK, PI3K, NFkB, and ERK1/2 influencing apoptosis and cell cycle regulation. Clinical trials have provided insights into EGCG's potential impact on breast cancer such as mammographic density and pharmacokinetics, indicating its potential as a potent therapeutic agent. Moreover, when administered with conventional chemotherapy, EGCG demonstrates synergistic effects, enhancing therapeutic outcomes. Nevertheless, further research is warranted to validate the safety and efficacy of EGCG in breast cancer prevention and treatment.

External Validation of a 5-Factor Risk Model for Breast Cancer–Related Lymphedema

Secondary lymphedema is a common, harmful side effect of breast cancer treatment. Robust risk models that are externally validated are needed to facilitate clinical translation. A published risk model used 5 accessible clinical factors to predict the development of breast cancer-related lymphedema; this model included a patient's mammographic breast density as a novel predictive factor. To investigate the external validity of a previously reported 5-factor model by applying it to an independent cohort of patients with breast cancer. This prognostic study collected data on a longitudinal cohort of patients with predominantly early-stage breast cancer treated with curative intent at the Princess Margaret Cancer Centre in Toronto, Canada between February 1, 2010, and July 31, 2014, with a median (IQR) follow-up of 4.3 (2.4-7.6) years. The 5 factors (age, body mass index, breast density, nodal burden, and use of axillary lymph node dissection [ALND]) were used as input into the established regression-based model. The analysis was performed from July 2 through August 29, 2024. Lymphedema after breast cancer treatment. Lymphedema-free survival (LFS) was analyzed using Kaplan-Meier analysis, and sensitivity, specificity, and accuracy performance metrics of predicting breast cancer-related lymphedema were calculated. A total of 101 female patients (median [IQR] age, 54.8 [48.8-62.3] years) were included in the analysis. These patients had localized or locoregional breast cancer treated with primary lumpectomy (90 [89%]) or mastectomy (11 [11%]); 75 (74%) had no axillary biopsy or sentinel lymph node biopsy; 26 (26%) had undergone ALND; and 38 (38%) had received chemotherapy, 101 (100%) received radiotherapy, and 64 (63%) received hormone therapy. Kaplan-Meier analysis showed a 2-year LFS of 97.5% (95% CI, 94.0%-100.0%) vs 65.0% (95% CI, 47.1%-89.7%) for the low- vs high-risk groups as defined by the 5-factor model (P < .001). The model sensitivity was 0.83 (95% CI, 0.52-0.98), specificity was 0.89 (95% CI, 0.80-0.94), and accuracy was 0.88 (95% CI, 0.80-0.94) for predicting breast cancer-related lymphedema. These findings validate the performance of a 5-factor risk model for its prediction of 2-year LFS. Future clinical translation of this model can help with identifying patients at the highest risk of breast cancer-related lymphedema to facilitate closer surveillance and/or preventive management to improve health outcomes and quality of life.

Evaluation of an AI tool to measure mammographic density for use in a FAST MRI trial

Evaluation of an AI tool to measure mammographic density for use in a FAST MRI trial

Prevalence and outcomes of nonmass lesions detected on screening breast ultrasound based on ultrasound features.

To determine how often non-mass lesions are seen in screening breast ultrasounds, and analyze their ultrasound features according to the ultrasound lexicon to find features suggestive of malignant non-mass lesions. This study is a single center retrospective study for nonmass lesions on screening breast ultrasound. Among 21,604 patients who underwent screening breast US, there were 279 patients with nonmass lesions. Of these lesions, 242 lesions were included for analysis. To distinguish between benign and malignant nonmass lesions, univariate analysis was performed on size, echogenicity, distribution, orientation, and associated ultrasound features. Additionally, Fisher's exact test was performed for mammographic density and abnormalities. 279 patients with nonmass lesions were included (mean age 53.7 ± 9.7years, all women). The incidence of nonmass lesions on screening breast ultrasound was 1.29% with positive predictive value of 5.78%. The most common malignant nonmass lesion was ductal carcinoma in situ. Nonparallel orientation (p = 0.002), echogenic rim (p = 0.005), architectural distortion (p = 0.0004), posterior shadowing (p = 0.007), vascularity (p < 0.001), and calcifications (p < 0.001) were indicators of malignant lesions. Additionally, mammographic abnormalities were significantly associated with malignant lesions (p < 0.001). The incidence of nonmass lesions on screening breast ultrasound was 1.29%, with a positive predictive value of 5.78%. Mammographic abnormalities, nonparallel orientation, architectural distortion, posterior shadowing, vascularity, and calcifications were associated with malignant nonmass lesions.

Risk-based breast cancer screening: What are the challenges?

Population-based screening programs aim to detect the disease at an early stage, so less treatment will be needed as well as having better oncological outcomes when diagnosed earlier. In the majority of European countries, breast cancer screening programs are designed based on women age.Meta-analysis of randomized clinical trial data demonstrates a reduction in the relative risk of breast cancer mortality due to screening, which has been estimated to be approximately 20%.One of the controversies about the population breast screening programs is that age-based screening ignores women's individual breast cancer risk. Identification of high-risk women may intensify the screening measures and will optimize the population screening programs to align them to individual risks.Family history of breast cancer is one of the risk factors to consider along with the recently developed polygenic risk scores to stratify women into a risk group. Other factors to assess risk include: mammographic breast density; B3 lesions with atypia in breast biopsy specimens; hormonal and lyfestyle and, potentially, epigenetic markers. Still, there are some difficulties in validating these factors and reflecting the interaction between risk factors in the models.Ongoing screening trials (e.g., WISDOM and MyPebs) are currently evaluating the clinical acceptability and utility of risk-stratified screening programs in the general population, and should provide valuable information for the possible implementation of such programs.Communication of complex risk information to the women, as well as assessing ethical concerns need to be addressed before implementation of risk stratified programs.

Investigating the added value of incorporatingmammographic density to an integrated breastcancer risk model with questionnaire-based riskfactors and polygenic risk score.

Incorporation of mammographic density to breast cancer risk models could improve risk stratification to tailor screening and prevention strategies according to risk. Robust evaluation of the value of adding mammographic density to models with comprehensive information on questionnaire-based risk factors and polygenic risk score is needed to determine its effectiveness in improving risk stratification of such models. We used the Individualized Coherent Absolute Risk Estimator (iCARE) tool for risk model building and validation to incorporate density to a previously validated literature-based model with questionnaire-based risk factors and a 313-variant polygenic risk score (PRS). The model was evaluated for calibration and discrimination in three prospective cohorts of European-ancestry women (1,468 cases, 19,104 controls): US-based Nurses' Health Study (NHS I and II) and Mayo Mammography Health Study (MMHS); and Sweden-based Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) study. Analyses were done separately for women younger (NHS II, KARMA) and older than 50 years (NHS I, MMHS, KARMA). Improvements in terms of risk stratification and reclassification proportions were assessed among European-ancestry women aged 50-70 years in US and Sweden. For women younger and older than 50 years, the model with questionnaire-based risk factors, PRS and density was generally well calibrated across risk with some evidence of miscalibration at the extremes of the risk distribution. Incorporation of density led to modest improvements risk discrimination beyond the model with questionnaire-based risk factors and PRS: the area under the curve (AUC) among younger women was 67.0% (95% CI: 63.5-70.6%) vs. 65.6% (95% CI: 61.9-69.3%) for models with and without density; and 66.1% (95% CI 64.4-67.8%) vs. 65.5% (95% CI: 63.8-67.2%) among older women. The model with density identified 18.4% of US women 50-70 years old ≥ 3% 5-year predicted risk (threshold used for recommending risk-reducing medication in the US), with 42.4% of future cases expected to occur in this group. At this threshold, 7.9% of US women were reclassified by adding density to the model, resulting in the identification of 2.8% of additional future cases. The model with density identified 10.3% of Swedish women ≥ 3% 5-year predicted risk, with 29.4% of future cases expected to occur in this group. At this threshold, 5.3% of women were reclassified with the addition of density, leading to the identification of an additional 4.4% of future cases. Integrating density with questionnaire-based risk factors and PRS could potentially identify more women of European-ancestry with elevated risk of breast cancer in the United States and Sweden. Further investigations of the integrated model in non-European ancestry populations are needed prior to considering clinical applications.

Education and mammographic breast density.

There are conflicting reports on the associations of education with mammographic breast density (MBD). To address this, we investigated the associations of education with MBD and additionally determined if and to what extent this association is mediated by known confounders such as age and adiposity. Women (n = 1155) were recruited during their annual screening mammogram at the Joanne Knight Breast Health Center at Washington University School of Medicine in St. Louis, MO. We assessed MBD using volumetric percent density (VPD; Volpara 1.5; Volpara Health®). We performed generalized linear modeling adjusted for potential confounders to estimate the differences in VPD by education level and evaluated whether associations differ by race and menopausal status. We also performed mediation analysis using PROCESS macro version 4.3. VPD was log-transformed, and back-transformed values are reported. Women with college (n = 401) and postgraduate education (n = 396) had higher VPD (7.21% [95% CI 6.87-7.59] and 7.18% [95% CI 6.82-7.53], respectively) compared to women (n = 358) with below college education (6.62% [95% CI 6.27-7.00]; p = 0.051) in analysis adjusted for age, body mass index (BMI) and other confounders. However, the association attenuated when the analysis was adjusted for body fat% instead of BMI (college graduate: 7.26% [95% CI 6.92-7.62]; postgraduate: 7.19% [95% CI 6.85-7.55] vs. below college: 6.78% [95% CI 6.41-7.16]; p = 0.156). Body fat% and BMI mediated 71% and64% of the association between education and VPD, respectively in premenopausal women, but not in postmenopausal women. Associations did not differ by race. Education was not associated with VPD after adjusting for adiposity. Adiposity mostly mediated the association between education and VPD, particularly among premenopausal women with body fat% a slightly more robust capture of adiposity than BMI.

Evaluating the performance of the BOADICEA model in predicting 10-year breast cancer risks in UK Biobank.

The BOADICEA model predicts breast cancer risk using cancer family history, epidemiological and genetic data. We evaluated its validity in a large prospective cohort. We assessed model calibration, discrimination and risk classification ability in 217,885 women (6,838 incident breast cancers) aged 40-70 years old of self-reported White ethnicity with no previous cancer from the UK Biobank. Age-specific risk classification was assessed using relative risk (RR) thresholds equivalent to the absolute lifetime risk categories of < 17%, 17-30% and ≥30%, recommended by the National Institute for Health and Care Excellence guidelines. We predicted 10-year risks using BOADICEA v.6 considering cancer family history, questionnaire-based risk factors, a 313-SNP polygenic score and pathogenic variants. Mammographic density data were not available. The PRS was the most discriminative risk factor (AUC=0.65). Discrimination was highest when considering all risk factors (AUC=0.66). The model was well calibrated overall (E/O=0.99, 95%CI=0.97-1.02; calibration slope=0.99, 95%CI:0.99-1.00), and in deciles of predicted risks. Discrimination was similar in women younger and older than 50 years. There was some underprediction in women under age 50 (E/O=0.89, 95%CI=0.84-0.94; calibration slope=0.96, 95%CI:0.94-0.97), which was explained by the higher breast cancer incidence in UK Biobank than the UK population incidence in this age group. The model classified 87.2%, 11.4% and 1.4% of women in RR categories <1.6, 1.6-3.1 and ≥3.1, identifying 25.6% of incident breast cancer cases in category RR ≥ 1.6. BOADICEA, implemented in CanRisk (www.canrisk.org), provides valid 10-year breast cancer risk which can facilitate risk-stratified screening and personalized breast cancer risk management.

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

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

The Untapped Opportunities of Addressing Lifecourse Growth in Mammographic Breast Density and Breast Cancer Prevention in Non-Hispanic Black Women.

This commentary discusses the importance of adiposity and lifecourse growth with mammographic breast density (MBD) in non-Hispanic Black (NHB) women. Although high MBD is a well-established risk factor for breast cancer, the determinants of MBD in NHB women remain understudied. Although adiposity at the time of mammography is most strongly associated with MBD, adiposity as early as ages 10 and 18 years is also independently associated with MBD. The commentary emphasizes the need for future research to identify biological mechanisms underlying the associations of adulthood adiposity and lifecourse growth with MBD in NHB women and how these can be translated to breast cancer prevention. Additionally, it highlights the need for more studies looking at lifecourse growth and microscopic breast tissue characteristics such as terminal duct lobular unit involution and epithelial-to-stromal proportions. See related article by Bigham et al., p. 1640.

Longitudinal history of mammographic breast density and breast cancer risk by familial risk, menopausal status, and initial mammographic density level in a high risk cohort: a nested case–control study

BackgroundElevated mammographic density is associated with increased breast cancer risk. However, the contribution of longitudinal changes in mammographic density to breast cancer risk beyond initial mammographic density levels, considering familial breast cancer risk and menopausal status, remains uncertain but holds important clinical implications.MethodsIn a nested case–control study within the Sister Study (323 cases, 899 controls; 12,095 mammograms), a cohort enriched for family history of breast cancer, we examined case–control status in relation to the largest annual change in percent density and dense area using mammograms available spanning 5.4 years, on average, using multivariable logistic regression and to the rate of mammographic density change using linear mixed-effects models. We considered effect modification by: mammographic density level of the earlier mammogram, the extent of family history, Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation (BOADICEA) risk strata, and menopausal status.ResultsCases (diagnosed < 60 years) had greater initial percent density and dense area levels and a slower rate of decline in dense area than controls. Women with stable mammographic density (≤ 10% annual change) had an increased breast cancer risk as compared with women whose largest mammographic density change was > 10% annual decline (e.g., Odds Ratio (OR) 2.34, 95% Confidence Interval (CI) 1.63–3.37 for dense area). Increasing vs. decreasing dense area was also associated with elevated risk, especially in women with the highest dense area levels at the earlier mammogram (OR: 2.56, 95%CI 1.50–4.36). Although generally similar across menopausal and familial risk categories, the associations of MD change with risk appeared stronger in pre-menopausal and lower-risk women.ConclusionsWomen who maintain higher levels of mammographic density (i.e. do not decrease over time) or have increasing mammographic density over time have a higher risk of subsequent breast cancer than women with high mammographic density that decreases over time. These findings suggest potential for incorporating mammographic density trajectories in clinical risk assessment, and the importance of additional breast cancer monitoring in women not experiencing declines in mammographic density over time.

Low-dose tamoxifen treatment reduces collagen organisation indicative of tissue stiffness in the normal breast: results from the KARISMA randomised controlled trial.

Tissue stiffness, dictated by organisation of interstitial fibrillar collagens, increases breast cancer risk and contributes to cancer progression. Tamoxifen is a standard treatment for receptor-positive breast cancer and is also aproved for primary prevention. We investigated the effect of tamoxifen and its main metabolites on the breast tissue collagen organisation as a proxy for stiffness and explored the relationship between mammographic density (MD) and collagen organisation. This sub-study of the double-blinded dose-determination trial, KARISMA, included 83 healthy women randomised to 6months of 20, 10, 5, 2.5, and 1mg of tamoxifen or placebo. Ultrasound-guided core-needle breast biopsies collected before and after treatment were evaluated for collagen organisation by polarised light microscopy. Tamoxifen reduced the amount of organised collagen and overall organisation, reflected by a shift from heavily crosslinked thick fibres to thinner, less crosslinked fibres. Collagen remodelling correlated with plasma concentrations of tamoxifen metabolites. MD change was not associated with changes in amount of organised collagen but was correlated with less crosslinking in premenopausal women. In this study of healthy women, tamoxifen decreased the overall organisation of fibrillar collagens, and consequently, the breast tissue stiffness. These stromal alterations may play a role in the well-established preventive and therapeutic effects of tamoxifen. Trial registration ClinicalTrials.gov ID: NCT03346200. Registered November 1st, 2017. Retrospectively registered.

Association between patient position-induced breast shape changes on prone and supine MRI and mammographic breast density or thickness.

The breast shape differs between the prone position in breast magnetic resonance imaging (MRI) and the supine position on an operating table. We sought to determine the relationship between patient position-induced changes on prone and supine MRI in breast shape and mammographic breast density or thickness. We evaluated data from 68 women with 69 breast cancers in this retrospective observational study. The difference in the minimal distance from the nipple to the pectoralis major (DNPp-s) or the internal thoracic artery between the prone and supine MRI (DNIs-p) was defined as the breast shape changes. Mammographic breast density was assessed by conventional 4-level classification and automated and manual quantification using a dedicated mammography viewer. The compressed breast thickness was recorded during mammography (MMG). We determined the association between patient position-induced breast shape changes on MRI and mammographic breast density or compressed breast thickness on MMG. On the conventional 4-level qualification, one breast appeared fatty, 39 appeared with scattered density, 23 appeared heterogeneously dense, and 6 breasts appeared extremely dense. Both automated and manual quantification of mammographic breast density differed between the 4 levels (p < 0.01 for both) and correlated with the 4 levels (p < 0.001 for both, r = 0.654 and 0.693, respectively). The manual quantification inversely correlated with DNPp-s and DNIs-p (p < 0.01 and < 0.05, r = - 0.330 and - 0.273, respectively). The compressed breast thickness significantly correlated with DNPp-s and DNIs-p (p < 0.01 for both, r = 0.648 and 0.467, respectively). Compressed breast thickness during MMG can predict the degree of patient position-induced changes in breast shape on MRI. The manual quantification of the mammographic breast density, which may reflect the biomechanical properties of the breast tissues, also correlates to the breast shape changes.

Exploring Dense Breast Density in Mammography: A Comparative Analysis of Breast Cancer Risk.

Breast density is a strong predictor of breast cancer. However, the difference in risk between breast density categories C and D remains inadequately explored. Given the low occurrence of extremely dense breasts, this investigation is crucial because it may lead to modifications in screening techniques for those with these conditions. The objective of the study is to evaluate the difference in breast cancer risk among women undergoing mammography and biopsy at a tertiary referral hospital in Colombia, based on American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) results in categories of breast density C (heterogeneously dense) and D (extremely dense). Methods: This retrospective cross-sectional study recorded variables from the mammographic BI-RADS scale, as well as histological and clinical variables from digital medical records. A stratified analysis of lesion malignancy/benignity was conducted according to density category by mammography and histological findings. The association between mammographic breast density subclassification of dense breasts and the occurrence or not of pathology-defined malignancy was sought. Results: A total of 107 patients with breast density in categories C and D were included, with 88.7% having heterogeneously dense breasts. The frequency of breast cancer was 32%. Infiltrating ductal carcinoma was the most frequently diagnosed malignancy (N = 14). Ahigher BI-RADS category was correlated withbreast density grade D and malignancy. A statistically significant association (p <0.045, RR 1.95, CI: 1.12-3.50) was found when comparing breast density (categories C and D) with the risk of malignancy. The positive predictive value (PPV) varied across different BI-RADS categories (BI-RADS 4A 25% vs. 0%; p-value 0.005; BI-RADS 4B 50% vs 10.5%; p-value 0.032). Efforts and resources should be focused on patients with extremely dense breasts, emphasizing the importance of additional (individualized) screening. Breast density could change the PPV within each BI-RADS category. However, further studies are needed to define the risk associated with each breast density subcategory and within BI-RADS categories, as well as to assess the efficacy of additional screening in patients with extremely dense breasts.

Low breast density is associated with epicardial adipose tissue volume and coronary artery disease

PurposeEpicardial adipose tissue volume (EATv), is well correlated with coronary artery disease (CAD), however not reported clinically. Breast density, measured on mammography, has shown promise as a reflector of cardiometabolic risk, with less dense breasts indicating greater proportion of adipose tissue. We aimed to evaluate the association between breast density, EATv and CAD. MethodRetrospective, cross-sectional study including 153 women who had both clinically indicated coronary computed tomography angiogram (CCTA) and mammography. EATv was quantified using semi-automated software. Breast density was visually assessed by standard 4-level BI-RADS grading (low: BI-RADS A–B, high: BI-RADS CD). CAD was categorised as presence/absence of coronary artery plaque and severity was quantified using CAD-RADS score. ResultsAmong 153 patients (mean age 62 ± 10), 103 (67.3 %) had low breast density (high breast adiposity). Low breast density patients were older, had greater rates of hypertension, higher mean BMI (p < 0.001) and EATv (106.6 ± 43.0 ml vs 81.0 ± 31.6 ml, p < 0.001). EATv was predictive of low breast density (OR: 1.02[1.01–1.03], p = 0.006), independent of age and hypertension. Low breast density was strongly associated with presence of CAD (prevalence 75 % vs 48 %, OR: 3.21[1.58–6.53], p = 0.001) independent of EATv, and modifiable (OR: 2.69[1.24–5.92], p = 0.012) and non-modifiable (OR: 2.42[1.04–5.85], p = 0.047) cardiovascular risk factors. Low breast density made up a higher proportion of mild (76.5 %), moderate (73.9 %) and severe (80.0 %) CAD. ConclusionsLow breast density is associated with higher EATv and independently associated with CAD presence beyond EATv and other cardiovascular risk factors. Mammographic breast density may therefore have value as an early risk identification tool for CAD in women.