Mammographic breast density as a general marker of breast cancer risk

Awareness of Breast Cancer Risk Factors in Women with vs. Without High Breast Density

Purpose

Women with high breast density (HBD) carry an increased risk for breast cancer (BC). The aim of the study was to provide data on awareness and knowledge gaps among women with vs w/o HBD about BC risk factors (BCRFs), which is the basis for effective communication about screening.

Patients and Methods

This was a web-based survey of 3000 women aged ≥30 and ≤70 from six countries. It comprised of 45 questions. T-tests and chi-square tests with False Discovery Rate adjustments were conducted as applicable, with significant differences reported at α=0.05.

Results

Three-thousand women were included in the analysis, 733 (24.4%) had HBD. Overall, 39% of women were familiar with the concept of HBD in the context of BC. Thirty-one percent of women were aware of HBD as BCRF and for 24% of women HBD was personally applicable. A significantly higher proportion of women with HBD were aware of almost all BCRFs compared to women w/o HBD (p ≤ 0.05). Similarly, a significantly higher proportion of women with HBD have undergone screening procedures compared to women w/o HBD (p ≤ 0.05). Women with HBD were significantly better aware of basic facts about BC (p ≤ 0.05). A total of 1617 women underwent mammography, 904 ultrasound and 150 MRI during their last screening. The most relevant source of information about BC was the health care professional, as reported by 63% of women.

Conclusion

Overall 39% of women were familiar with HBD as BCRF. Lack of BCRF awareness may contribute to delayed screenings, missed opportunities for early detection, and potentially poorer outcomes for individuals with dense breast tissue. Thus, this information should be communicated more widely.

Deep Learning Approaches with Digital Mammography for Evaluating Breast Cancer Risk, a Narrative Review

Breast cancer remains the leading cause of cancer-related deaths in women worldwide. Current screening regimens and clinical breast cancer risk assessment models use risk factors such as demographics and patient history to guide policy and assess risk. Applications of artificial intelligence methods (AI) such as deep learning (DL) and convolutional neural networks (CNNs) to evaluate individual patient information and imaging showed promise as personalized risk models. We reviewed the current literature for studies related to deep learning and convolutional neural networks with digital mammography for assessing breast cancer risk. We discussed the literature and examined the ongoing and future applications of deep learning techniques in breast cancer risk modeling.

Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction

Breast cancer remains the most diagnosed cancer in women. Advances in medical imaging modalities and technologies have greatly aided in the early detection of breast cancer and the decline of patient mortality rates. However, reading and interpreting breast images remains difficult due to the high heterogeneity of breast tumors and fibro-glandular tissue, which results in lower cancer detection sensitivity and specificity and large inter-reader variability. In order to help overcome these clinical challenges, researchers have made great efforts to develop computer-aided detection and/or diagnosis (CAD) schemes of breast images to provide radiologists with decision-making support tools. Recent rapid advances in high throughput data analysis methods and artificial intelligence (AI) technologies, particularly radiomics and deep learning techniques, have led to an exponential increase in the development of new AI-based models of breast images that cover a broad range of application topics. In this review paper, we focus on reviewing recent advances in better understanding the association between radiomics features and tumor microenvironment and the progress in developing new AI-based quantitative image feature analysis models in three realms of breast cancer: predicting breast cancer risk, the likelihood of tumor malignancy, and tumor response to treatment. The outlook and three major challenges of applying new AI-based models of breast images to clinical practice are also discussed. Through this review we conclude that although developing new AI-based models of breast images has achieved significant progress and promising results, several obstacles to applying these new AI-based models to clinical practice remain. Therefore, more research effort is needed in future studies.

Association of breast cancer risk, density, and stiffness: global tissue stiffness on breast MR elastography (MRE)

PURPOSE

Quantify in vivo biomechanical tissue properties in various breast densities and in average risk and high-risk women using Magnetic Resonance Imaging (MRI)/MRE and examine the association between breast biomechanical properties and cancer risk based on patient demographics and clinical data.

METHODS

Patients with average risk or high-risk of breast cancer underwent 3.0 T breast MR imaging and elastography. Breast parenchymal enhancement (BPE), density (from most recent mammogram), stiffness, elasticity, and viscosity were recorded. Within each breast density group (non-dense versus dense), stiffness, elasticity, and viscosity were compared across risk groups (average versus high). Separately for stiffness, elasticity, and viscosity, a multivariable logistic regression model was used to evaluate whether the MRE parameter predicted risk status after controlling for clinical factors.

RESULTS

50 average risk and 86 high-risk patients were included. Risk groups were similar in age, density, and menopausal status. Among patients with dense breasts, mean stiffness, elasticity, and viscosity were significantly higher in high-risk patients (N = 55) compared to average risk patients (N = 34; all p < 0.001). Stiffness remained a significant predictor of risk status (OR = 4.26, 95% CI [1.96, 9.25]) even after controlling for breast density, BPE, age, and menopausal status. Similar results were seen for elasticity and viscosity.

CONCLUSION

A structurally based, quantitative biomarker of tissue stiffness obtained from MRE is associated with differences in breast cancer risk in dense breasts. Tissue stiffness could provide a novel prognostic marker to help identify high-risk women with dense breasts who would benefit from increased surveillance and/or risk reduction measures.

Hormone and receptor activator of NF-κB (RANK) pathway gene expression in plasma and mammographic breast density in postmenopausal women

Background

Hormones impact breast tissue proliferation. Studies investigating the associations of circulating hormone levels with mammographic breast density have reported conflicting results. Due to the limited number of studies, we investigated the associations of hormone gene expression as well as their downstream mediators within the plasma with mammographic breast density in postmenopausal women.

Methods

We recruited postmenopausal women at their annual screening mammogram at Washington University School of Medicine, St. Louis. We used the NanoString nCounter platform to quantify gene expression of hormones (prolactin, progesterone receptor (PGR), estrogen receptor 1 (ESR1), signal transducer and activator of transcription (STAT1 and STAT5), and receptor activator of nuclear factor-kB (RANK) pathway markers (RANK, RANKL, osteoprotegerin, TNFRSF18, and TNFRSF13B) in plasma. We used Volpara to measure volumetric percent density, dense volume, and non-dense volume. Linear regression models, adjusted for confounders, were used to evaluate associations between gene expression (linear fold change) and mammographic breast density.

Results

One unit increase in ESR1, RANK, and TNFRSF18 gene expression was associated with 8% (95% CI 0–15%, p value = 0.05), 10% (95% CI 0–20%, p value = 0.04) and % (95% CI 0–9%, p value = 0.04) higher volumetric percent density, respectively. There were no associations between gene expression of other markers and volumetric percent density. One unit increase in osteoprotegerin and PGR gene expression was associated with 12% (95% CI 4–19%, p value = 0.003) and 7% (95% CI 0–13%, p value = 0.04) lower non-dense volume, respectively.

Conclusion

These findings provide new insight on the associations of plasma hormonal and RANK pathway gene expression with mammographic breast density in postmenopausal women and require confirmation in other studies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13058-022-01522-2.

Rapid Reductions in Breast Density following Tamoxifen Therapy as Evaluated by Whole-Breast Ultrasound Tomography

Purpose: Women whose mammographic breast density declines within 12–18 months of initiating tamoxifen for chemoprevention or adjuvant treatment show improved therapeutic responses compared with those whose density is unchanged. We tested whether measuring changes in sound speed (a surrogate of breast density) using ultrasound tomography (UST) could enable rapid identification of favorable responses to tamoxifen. Methods: We evaluated serial density measures at baseline and at 1 to 3, 4 to 6, and 12+ months among 74 women (aged 30–70 years) following initiation of tamoxifen for clinical indications, including an elevated risk of breast cancer (20%) and diagnoses of in situ (39%) or invasive (40%) breast carcinoma, enrolled at Karmanos Cancer Institute and Henry Ford Health System (Detroit, MI, USA). For comparison, we evaluated an untreated group with screen negative mammography and frequency-matched on age, race, and menopausal status (n = 150), at baseline and 12 months. Paired t-tests were used to assess differences in UST sound speed over time and between tamoxifen-treated and untreated patients. Results: Sound speed declined steadily over the 12 month period among patients receiving tamoxifen (mean (SD): −3.0 (8.2) m/s; p = 0.001), whereas density remained unchanged in the untreated group (mean (SD): 0.4 (7.1) m/s; p = 0.75 (relative change between groups: p = 0.0009)). In the tamoxifen group, we observed significant sound speed reductions as early as 4–6 months after tamoxifen initiation (mean (SD): −2.1 (6.8) m/s; p = 0.008). Sound speed reductions were greatest among premenopausal patients (P-interaction = 0.0002) and those in the middle and upper tertiles of baseline sound speed (P-interaction = 0.002). Conclusions: UST can image rapid declines in sound speed following initiation of tamoxifen. Given that sound speed and mammographic density are correlated, we propose that UST breast imaging may capture early responses to tamoxifen, which in turn may have utility in predicting therapeutic efficacy.

Factors associated with mammographic breast density among women in Karachi Pakistan

Background

There are no studies done to evaluate the distribution of mammographic breast density and factors associated with it among Pakistani women.

Methods

Participants included 477 women, who had received either diagnostic or screening mammography at two hospitals in Karachi Pakistan. Mammographic breast density was assessed using the Breast Imaging Reporting and Data System. In person interviews were conducted using a detailed questionnaire, to assess risk factors of interest, and venous blood was collected to measure serum vitamin D level at the end of the interview. To determine the association of potential factors with mammographic breast density, multivariable polytomous logistic regression was used.

Results

High-density mammographic breast density (heterogeneously and dense categories) was high and found in 62.4% of women. There was a significant association of both heterogeneously dense and dense breasts with women of a younger age group < 45 years (OR 2.68, 95% CI 1.60–4.49) and (OR 4.83, 95% CI 2.54–9.16) respectively. Women with heterogeneously dense and dense breasts versus fatty and fibroglandular breasts had a higher history of benign breast disease (OR 1.90, 95% CI 1.14–3.17) and (OR 3.61, 95% CI 1.90–6.86) respectively. There was an inverse relationship between breast density and body mass index. Women with dense breasts and heterogeneously dense breasts had lower body mass index (OR 0.94 95% CI 0.90–0.99) and (OR 0.81, 95% CI 0.76–0.87) respectively. There was no association of mammographic breast density with serum vitamin D levels, diet, and breast cancer.

Conclusions

The findings of a positive association of higher mammographic density with younger age and benign breast disease and a negative association between body mass index and breast density are important findings that need to be considered in developing screening guidelines for the Pakistani population.

Mammary collagen is under reproductive control with implications for breast cancer

Mammographically-detected breast density impacts breast cancer risk and progression, and fibrillar collagen is a key component of breast density. However, physiologic factors influencing collagen production in the breast are poorly understood. In female rats, we analyzed gene expression of the most abundantly expressed mammary collagens and collagen-associated proteins across a pregnancy, lactation, and weaning cycle. We identified a triphasic pattern of collagen gene regulation and evidence for reproductive state-dependent composition. An initial phase of collagen deposition occurred during pregnancy, followed by an active phase of collagen suppression during lactation. The third phase of collagen regulation occurred during weaning-induced mammary gland involution, which was characterized by increased collagen deposition. Concomitant changes in collagen protein abundance were confirmed by Masson's trichrome staining, second harmonic generation (SHG) imaging, and mass spectrometry. We observed similar reproductive-state dependent collagen patterns in human breast tissue obtained from premenopausal women. SHG analysis also revealed structural variation in collagen across a reproductive cycle, with higher packing density and more collagen fibers arranged perpendicular to the mammary epithelium in the involuting rat mammary gland compared to nulliparous and lactating glands. Involution was also characterized by high expression of the collagen cross-linking enzyme lysyl oxidase, which was associated with increased levels of cross-linked collagen. Breast cancer relevance is suggested, as we found that breast cancer diagnosed in recently postpartum women displayed gene expression signatures of increased collagen deposition and crosslinking compared to breast cancers diagnosed in age-matched nulliparous women. Using publically available data sets, we found this involution-like, collagen gene signature correlated with poor progression-free survival in breast cancer patients overall and in younger women. In sum, these findings of physiologic collagen regulation in the normal mammary gland may provide insight into normal breast function, the etiology of breast density, and inform breast cancer risk and outcomes.

UK Women's Views of the Concepts of Personalised Breast Cancer Risk Assessment and Risk-Stratified Breast Screening: A Qualitative Interview Study

Simple Summary

Risk-based breast screening will involve tailoring the amount of screening to women’s level of risk. Therefore, women at high-risk may be offered more frequent screening and over a longer period of time than those at low risk for whom less screening may be recommended. As this will involve considerable changes to the NHS Breast Screening Programme, it is important to explore what women in the UK think and feel about this approach. Analysis of in-depth interviews revealed that some women would find both high and low-risk screening options acceptable whereas others were resistant to the prospect of reduced screening if they were assessed as low-risk. We also found that the idea of risk-based screening had little influence on the attitudes of women who were already sceptical about breast screening. These findings highlight the communication challenges that will be faced by those introducing risk-based screening and suggest a need for tailored support and advice.

Abstract

Any introduction of risk-stratification within the NHS Breast Screening Programme needs to be considered acceptable by women. We conducted interviews to explore women’s attitudes to personalised risk assessment and risk-stratified breast screening. Twenty-five UK women were purposively sampled by screening experience and socioeconomic background. Interview transcripts were qualitatively analysed using Framework Analysis. Women expressed positive intentions for personal risk assessment and willingness to receive risk feedback to provide reassurance and certainty. Women responded to risk-stratified screening scenarios in three ways: ‘Overall acceptors’ considered both high- and low-risk options acceptable as a reasonable allocation of resources to clinical need, yet acceptability was subject to specified conditions including accuracy of risk estimates and availability of support throughout the screening pathway. Others who thought ‘more is better’ only supported high-risk scenarios where increased screening was proposed. ‘Screening sceptics’ found low-risk scenarios more aligned to their screening values than high-risk screening options. Consideration of screening recommendations for other risk groups had more influence on women’s responses than screening-related harms. These findings demonstrate high, but not universal, acceptability. Support and guidance, tailored to screening values and preferences, may be required by women at all levels of risk.

Comparable prediction of breast cancer risk from a glimpse or a first impression of a mammogram

Expert radiologists can discern normal from abnormal mammograms with above-chance accuracy after brief (e.g. 500 ms) exposure. They can even predict cancer risk viewing currently normal images (priors) from women who will later develop cancer. This involves a rapid, global, non-selective process called “gist extraction”. It is not yet known whether prolonged exposure can strengthen the gist signal, or if it is available solely in the early exposure. This is of particular interest for the priors that do not contain any localizable signal of abnormality. The current study compared performance with brief (500 ms) or unlimited exposure for four types of mammograms (normal, abnormal, contralateral, priors). Groups of expert radiologists and untrained observers were tested. As expected, radiologists outperformed naïve participants. Replicating prior work, they exceeded chance performance though the gist signal was weak. However, we found no consistent performance differences in radiologists or naïves between timing conditions. Exposure time neither increased nor decreased ability to identify the gist of abnormality or predict cancer risk. If gist signals are to have a place in cancer risk assessments, more efforts should be made to strengthen the signal.

Biological Mechanisms and Therapeutic Opportunities in Mammographic Density and Breast Cancer Risk

Mammographic density is an important risk factor for breast cancer; women with extremely dense breasts have a four to six fold increased risk of breast cancer compared to women with mostly fatty breasts, when matched with age and body mass index. High mammographic density is characterised by high proportions of stroma, containing fibroblasts, collagen and immune cells that suggest a pro-tumour inflammatory microenvironment. However, the biological mechanisms that drive increased mammographic density and the associated increased risk of breast cancer are not yet understood. Inflammatory factors such as monocyte chemotactic protein 1, peroxidase enzymes, transforming growth factor beta, and tumour necrosis factor alpha have been implicated in breast development as well as breast cancer risk, and also influence functions of stromal fibroblasts. Here, the current knowledge and understanding of the underlying biological mechanisms that lead to high mammographic density and the associated increased risk of breast cancer are reviewed, with particular consideration to potential immune factors that may contribute to this process.

Fully Automated Volumetric Breast Density Estimation from Digital Breast Tomosynthesis

Background While digital breast tomosynthesis (DBT) is rapidly replacing digital mammography (DM) in breast cancer screening, the potential of DBT density measures for breast cancer risk assessment remains largely unexplored. Purpose To compare associations of breast density estimates from DBT and DM with breast cancer. Materials and Methods This retrospective case-control study used contralateral DM/DBT studies from women with unilateral breast cancer and age- and ethnicity-matched controls (September 19, 2011-January 6, 2015). Volumetric percent density (VPD%) was estimated from DBT using previously validated software. For comparison, the publicly available Laboratory for Individualized Breast Radiodensity Assessment software package, or LIBRA, was used to estimate area-based percent density (APD%) from raw and processed DM images. The commercial Quantra and Volpara software packages were applied to raw DM images to estimate VPD% with use of physics-based models. Density measures were compared by using Spearman correlation coefficients (r), and conditional logistic regression was performed to examine density associations (odds ratios [OR]) with breast cancer, adjusting for age and body mass index. Results A total of 132 women diagnosed with breast cancer (mean age ± standard deviation [SD], 60 years ± 11) and 528 controls (mean age, 60 years ± 11) were included. Moderate correlations between DBT and DM density measures (r = 0.32-0.75; all P < .001) were observed. Volumetric density estimates calculated from DBT (OR, 2.3 [95% CI: 1.6, 3.4] per SD for VPD%_DBT) were more strongly associated with breast cancer than DM-derived density for both APD% (OR, 1.3 [95% CI: 0.9, 1.9] [P < .001] and 1.7 [95% CI: 1.2, 2.3] [P = .004] per SD for LIBRA raw and processed data, respectively) and VPD% (OR, 1.6 [95% CI: 1.1, 2.4] [P = .01] and 1.7 [95% CI: 1.2, 2.6] [P = .04] per SD for Volpara and Quantra, respectively). Conclusion The associations between quantitative breast density estimates and breast cancer risk are stronger for digital breast tomosynthesis compared with digital mammography. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Yaffe in this issue.

Relation of Quantitative Histologic and Radiologic Breast Tissue Composition Metrics With Invasive Breast Cancer Risk

Background

Benign breast disease (BBD) is a strong breast cancer risk factor, but identifying patients that might develop invasive breast cancer remains a challenge.

Methods

By applying machine-learning to digitized hematoxylin and eosin-stained biopsies and computer-assisted thresholding to mammograms obtained circa BBD diagnosis, we generated quantitative tissue composition metrics and determined their association with future invasive breast cancer diagnosis. Archival breast biopsies and mammograms were obtained for women (18-86 years of age) in a case-control study, nested within a cohort of 15 395 BBD patients from Kaiser Permanente Northwest (1970-2012), followed through mid-2015. Patients who developed incident invasive breast cancer (ie, cases; n = 514) and those who did not (ie, controls; n = 514) were matched on BBD diagnosis age and plan membership duration. All statistical tests were 2-sided.

Results

Increasing epithelial area on the BBD biopsy was associated with increasing breast cancer risk (odds ratio [OR]_{Q4 vs Q1} = 1.85, 95% confidence interval [CI] = 1.13 to 3.04; P _trend = .02). Conversely, increasing stroma was associated with decreased risk in nonproliferative, but not proliferative, BBD (P _{heterogeneity} = .002). Increasing epithelium-to-stroma proportion (OR_{Q4 vs Q1} = 2.06, 95% CI =1.28 to 3.33; P _trend = .002) and percent mammographic density (MBD) (OR_{Q4 vs Q1} = 2.20, 95% CI = 1.20 to 4.03; P _trend = .01) were independently and strongly predictive of increased breast cancer risk. In combination, women with high epithelium-to-stroma proportion and high MBD had substantially higher risk than those with low epithelium-to-stroma proportion and low MBD (OR = 2.27, 95% CI = 1.27 to 4.06; P _trend = .005), particularly among women with nonproliferative (P _trend = .01) vs proliferative (P _trend = .33) BBD.

Conclusion

Among BBD patients, increasing epithelium-to-stroma proportion on BBD biopsies and percent MBD at BBD diagnosis were independently and jointly associated with increasing breast cancer risk. These findings were particularly striking for women with nonproliferative disease (comprising approximately 70% of all BBD patients), for whom relevant predictive biomarkers are lacking.

Association of mammographic density measures and breast cancer "intrinsic" molecular subtypes

PURPOSE

We evaluated the association of percent mammographic density (PMD), absolute dense area (DA), and non-dense area (NDA) with risk of "intrinsic" molecular breast cancer (BC) subtypes.

METHODS

We pooled 3492 invasive BC and 10,148 controls across six studies with density measures from prediagnostic, digitized film-screen mammograms. We classified BC tumors into subtypes [63% Luminal A, 21% Luminal B, 5% HER2 expressing, and 11% as triple negative (TN)] using information on estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and tumor grade. We used polytomous logistic regression to calculate odds ratio (OR) and 95% confidence intervals (CI) for density measures (per SD) across the subtypes compared to controls, adjusting for age, body mass index and study, and examined differences by age group.

RESULTS

All density measures were similarly associated with BC risk across subtypes. Significant interaction of PMD by age (P = 0.001) was observed for Luminal A tumors, with stronger effect sizes seen for younger women < 45 years (OR = 1.69 per SD PMD) relative to women of older ages (OR = 1.53, ages 65-74, OR = 1.44 ages 75 +). Similar but opposite trends were seen for NDA by age for risk of Luminal A: risk for women: < 45 years (OR = 0.71 per SD NDA) was lower than older women (OR = 0.83 and OR = 0.84 for ages 65-74 and 75 + , respectively) (P < 0.001). Although not significant, similar patterns of associations were seen by age for TN cancers.

CONCLUSIONS

Mammographic density measures were associated with risk of all "intrinsic" molecular subtypes. However, findings of significant interactions between age and density measures may have implications for subtype-specific risk models.

Pubertal mammary gland development is a key determinant of adult mammographic density

Mammographic density refers to the radiological appearance of fibroglandular and adipose tissue on a mammogram of the breast. Women with relatively high mammographic density for their age and body mass index are at significantly higher risk for breast cancer. The association between mammographic density and breast cancer risk is well-established, however the molecular and cellular events that lead to the development of high mammographic density are yet to be elucidated. Puberty is a critical time for breast development, where endocrine and paracrine signalling drive development of the mammary gland epithelium, stroma, and adipose tissue. As the relative abundance of these cell types determines the radiological appearance of the adult breast, puberty should be considered as a key developmental stage in the establishment of mammographic density. Epidemiological studies have pointed to the significance of pubertal adipose tissue deposition, as well as timing of menarche and thelarche, on adult mammographic density and breast cancer risk. Activation of hypothalamic-pituitary axes during puberty combined with genetic and epigenetic molecular determinants, together with stromal fibroblasts, extracellular matrix, and immune signalling factors in the mammary gland, act in concert to drive breast development and the relative abundance of different cell types in the adult breast. Here, we discuss the key cellular and molecular mechanisms through which pubertal mammary gland development may affect adult mammographic density and cancer risk.

Factors Associated With Background Parenchymal Enhancement on Contrast-Enhanced Mammography

OBJECTIVE. The purpose of this study was to determine the relationship between background parenchymal enhancement (BPE) on contrast-enhanced mammography (CEM) and breast tissue density, menstrual status, endocrine therapy, and risk factors for breast cancer and also to evaluate interreader agreement on classification of BPE on CEM. MATERIALS AND METHODS. Five subspecialty-trained breast radiologists independently and blindly graded tissue density (with fatty tissue and scattered fibroglandular tissue classified as nondense tissue and with heterogeneously dense and extremely dense classified as dense tissue) and BPE (with minimal or mild BPE categorized as low BPE and moderate or marked BPE categorized as high BPE) on CEM examinations performed from 2014 to 2018. Electronic medical charts were reviewed for information on menstrual status, endocrine therapy, history of breast surgery, and other risk factors for breast cancer. Comparisons were performed using the Kruskal-Wallis test, Mann-Whitney test, and Spearman rank correlation. Interreader agreement was estimated using the Fleiss kappa test. RESULTS. A total of 202 patients (mean [± SD] age, 54 ± 10 years; range, 25-84 years) underwent CEM. Tissue density was categorized as fatty in two patients (1%), scattered fibroglandular in 67 patients (33%), heterogeneously dense in 117 patients (58%), and extremely dense in 16 patients (8%). Among the 202 patients, BPE was minimal in 77 (38%), mild in 80 (40%), moderate in 31 (15%), and marked in 14 (7%). Dense breasts, younger age, premenopausal status, no history of endocrine therapy, and no history of breast cancer were significantly associated with high BPE. Among premenopausal patients, no association was found between BPE and time from last menstrual period to CEM. Overall interreader agreement on BPE was moderate (κ = 0.41; 95% CI, 0.40-0.42). Interreader agreement on tissue density was substantial (κ = 0.67; 95% CI, 0.66-0.69). CONCLUSION. Women with dense breasts, premenopausal status, and younger age are more likely to have greater BPE. Targeting CEM to the last menstrual period is not indicated.

Menopausal Transition, Body Mass Index, and Prevalence of Mammographic Dense Breasts in Middle-Aged Women

The interrelationship between menopausal stage, excessive adiposity and dense breasts remains unclear. We aimed to investigate the relationship between menopausal stage and dense-breast prevalence in midlife women while considering a possible effect modification of being overweight. The present cross-sectional study comprised 82,677 Korean women, aged 35–65 years, who attended a screening exam. Menopausal stages were categorized based on the Stages of Reproductive Aging Workshop (STRAW + 10) criteria. Mammographic breast density was categorized according to Breast Imaging Reporting and Data System (BI-RADS). Dense breasts were defined as BI-RADS Breast Density category D (extremely dense). The prevalence of dense breasts decreased as menopausal stage increased (p-trend < 0.001), and this pattern was pronounced in overweight women than non-overweight women (p-interaction = 0.016). Compared with pre-menopause, the multivariable-adjusted prevalence ratios (and 95% confidence intervals) for dense breasts were 0.98 (0.96–1.00) in early transition, 0.89 (0.86–0.92) in late transition, and 0.55 (0.52–0.59) in post-menopause, among non-overweight women, while corresponding prevalence ratios were 0.92 (0.87–0.98), 0.83 (0.77–0.90) and 0.36 (0.31–0.41) among overweight women. The prevalence of dense breasts was inversely associated with increasing menopausal stages and significantly decreased from the late menopausal transition, with stronger declines among overweight women.

Associations of aspirin and other anti-inflammatory medications with mammographic breast density and breast cancer risk

PURPOSE

We investigated the associations of aspirin and other non-steroid anti-inflammatory drugs with mammographic breast density (MBD) and their interactions in relation to breast cancer risk.

METHODS

This study included 3,675 cancer-free women within the Nurses' Health Study (NHS) and Nurses' Health Study II (NHSII) cohorts. Percent breast density (PD), absolute dense area (DA), and non-dense area (NDA) were measured from digitized film mammograms using a computer-assisted thresholding technique; all measures were square root-transformed. Information on medication use was collected in 1980 (NHS) and 1989 (NHSII) and updated biennially. Medication use was defined as none, past or current; average cumulative dose and frequency were calculated for all past or current users from all bi-annual questionnaires preceding the mammogram date. We used generalized linear regression to quantify associations of medications with MBD. Two-way interactions were examined in logistic regression models.

RESULTS

In multivariate analysis, none of the anti-inflammatory medications were associated with PD, DA, and NDA. We found no interactions of any of the medications with PD with respect to breast cancer risk (all p-interactions > 0.05). However, some of the aspirin variables appeared to have positive associations with breast cancer risk limited only to women with PD 10-24% (past aspirin OR 1.56, 95% CI 1.03-2.35; current aspirin with < 5 years of use OR 1.82, 95% CI 1.01-3.28; current aspirin with ≥ 5 years of use OR 1.89, 95% CI 1.26-2.82).

CONCLUSIONS

Aspirin and NSAIDs are not associated with breast density measures. We found no interactions of aspirin with MBD in relation to breast cancer risk.

Is mammographic density a marker of breast cancer phenotypes?

PURPOSE

To investigate the association between mammographic density (MD) phenotypes and both clinicopathologic features of breast cancer (BC) and tumor location.

METHODS

MD was measured for 297 BC-affected females using qualitative (visual method) and quantitative (fully automated area-based method) approaches. Radiologists' description, visible external markers, and surgical scar were used to establish the location of tumors. Binary logistic regression models were used to assess the association between MD phenotypes and BC clinicopathologic features.

RESULTS

Categorical and numerical MD measures showed no association with clinicopathologic features of BC (p > 0.05). Participants with higher BI-RADS scores [(51-75% glandular) and (> 75% glandular)] (p < 0.001), and percent density (PD) categories [PD (21-49%) and PD ≥ 50%] (p = 0.01) were more likely to have tumors emanating from dense areas. Additionally, tumors were commonly found in dense regions of the breast among patients with higher medians of PD (p = 0.001), dense area (DA) (p = 0.02), and lower medians of non-dense area (NDA) (p < 0.001). Adjusted logistic regression models showed that high BI-RADS density (> 75% glandular) has an almost fivefold increased odds of tumors developing within dense areas (OR 4.99, 95% CI 0.93-25.9; p = 0.05. PD (OR 1.02, 95% CI 1-1.03, p = 0.002) and NDA (OR 0.99, 95% CI 0.991-0.997, p < 0.001) had very small effect on tumor location. Compared to tumors within non-dense areas, tumors in dense areas tended to exhibit human epidermal growth factor receptor 2 positive (p = 0.05) and carcinoma in situ (p = 0.01) characteristics.

CONCLUSION

MD shows no significant association with clinicopathologic features of BC. However, BC was more likely to originate from dense tissue, with tumors in dense regions having human epidermal growth receptor 2 positive and carcinoma in situ characteristics.

Using Whole Breast Ultrasound Tomography to Improve Breast Cancer Risk Assessment: A Novel Risk Factor Based on the Quantitative Tissue Property of Sound Speed

Mammographic percent density (MPD) is an independent risk factor for developing breast cancer, but its inclusion in clinical risk models provides only modest improvements in individualized risk prediction, and MPD is not typically assessed in younger women because of ionizing radiation concerns. Previous studies have shown that tissue sound speed, derived from whole breast ultrasound tomography (UST), a non-ionizing modality, is a potential surrogate marker of breast density, but prior to this study, sound speed has not been directly linked to breast cancer risk. To that end, we explored the relation of sound speed and MPD with breast cancer risk in a case-control study, including 61 cases with recent breast cancer diagnoses and a comparison group of 165 women, frequency matched to cases on age, race, and menopausal status, and with a recent negative mammogram and no personal history of breast cancer. Multivariable odds ratios (ORs) and 95% confidence intervals (CIs) were estimated for the relation of quartiles of MPD and sound speed with breast cancer risk adjusted for matching factors. Elevated MPD was associated with increased breast cancer risk, although the trend did not reach statistical significance (OR per quartile = 1.27, 95% CI: 0.95, 1.70; p_trend = 0.10). In contrast, elevated sound speed was significantly associated with breast cancer risk in a dose–response fashion (OR per quartile = 1.83, 95% CI: 1.32, 2.54; p_trend = 0.0003). The OR trend for sound speed was statistically significantly different from that observed for MPD (p = 0.005). These findings suggest that whole breast sound speed may be more strongly associated with breast cancer risk than MPD and offer future opportunities for refining the magnitude and precision of risk associations in larger, population-based studies, including women younger than usual screening ages.

Mammographic Breast Density Assessed with Fully Automated Method and its Risk for Breast Cancer

Objectives:

We evaluated the association between breast cancer and breast density (BD) measured using fully automated software. We also evaluated the performance of cancer risk models such as only clinical risk factors, density related measures, and both clinical risk factors and density-related measures for determining cancer risk.

Materials and Methods:

This is a retrospective case–control study. The data were collected from August 2015 to December 2018. Two hundred fifty women with breast cancer and 400 control subjects were included in this study. We evaluated the BD qualitatively using breast imaging-reporting and data system density and quantitatively using 3D slicer. We also collected clinical factors such as age, familial history of breast cancer, menopausal status, number of births, body mass index, and hormonal replacement therapy use. We calculated the odds ratio (OR) for BD to determine the risk of breast cancer. We performed receiver operating characteristic (ROC) curve to assess the performance of cancer risk models.

Results:

The OR for the percentage BD for second, third, and fourth quartiles was 1.632 (95% confidence intervals [CI]: 1.102–2.416), 2.756 (95% CI: 1.704–4.458), and 3.163 (95% CI: 1.356–5.61). The area under ROC curve for clinical risk factors only, mammographic density measures, combined mammographic, and clinical risk factors was 0.578 (95% CI: 0.45, 0.64), 0.684 (95% CI: 0.58, 0.75), and 0.724 (95% CI: 0.64, 0.80), respectively.

Conclusion:

Mammographic BD was found to be positively associated with breast cancer. The density related measures combined clinical risk factors, and density model had good discriminatory power in identifying the cancer risk.

Associations of mammographic breast density with breast stem cell marker-defined breast cancer subtypes

PURPOSE

High mammographic breast density is a strong, well-established breast cancer risk factor. Whether stem cells may explain high breast cancer risk in dense breasts is unknown. We investigated the association between breast density and breast cancer risk by the status of stem cell markers CD44, CD24, and ALDH1A1 in the tumor.

METHODS

We included 223 women with primary invasive or in situ breast cancer and 399 age-matched controls from Mayo Clinic Mammography Study. Percent breast density (PD), absolute dense area (DA), and non-dense area (NDA) were assessed using computer-assisted thresholding technique. Immunohistochemical analysis of the markers was performed on tumor tissue microarrays according to a standard protocol. We used polytomous logistic regression to quantify the associations of breast density measures with breast cancer risk across marker-defined tumor subtypes.

RESULTS

Of the 223 cancers in the study, 182 were positive for CD44, 83 for CD24 and 52 for ALDH1A1. Associations of PD were not significantly different across t marker-defined subtypes (51% + vs. 11-25%: OR 2.83, 95% CI 1.49-5.37 for CD44+ vs. OR 1.87, 95% CI 0.47-7.51 for CD44-, p-heterogeneity = 0.66; OR 2.80, 95% CI 1.27-6.18 for CD24+ vs. OR 2.44, 95% CI 1.14-5.22 for CD24-, p-heterogeneity = 0.61; OR 3.04, 95% CI 1.14-8.10 for ALDH1A1+ vs. OR 2.57. 95% CI 1.30-5.08 for ALDH1A1-, p-heterogeneity = 0.94). Positive associations of DA and inverse associations of NDA with breast cancer risk were similar across marker-defined subtypes.

CONCLUSIONS

We found no evidence of differential associations of breast density with breast cancer risk by the status of stem cell markers. Further studies in larger study populations are warranted to confirm these associations.

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

PURPOSE

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

METHODS

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

RESULTS AND CONCLUSIONS

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

NCTN Assessment on Current Applications of Radiomics in Oncology

Radiomics is a fast-growing research area based on converting standard-of-care imaging into quantitative minable data and building subsequent predictive models to personalize treatment. Radiomics has been proposed as a study objective in clinical trial concepts and a potential biomarker for stratifying patients across interventional treatment arms. In recognizing the growing importance of radiomics in oncology, a group of medical physicists and clinicians from NRG Oncology reviewed the current status of the field and identified critical issues, providing a general assessment and early recommendations for incorporation in oncology studies.

Joint association of mammographic density adjusted for age and body mass index and polygenic risk score with breast cancer risk

BACKGROUND

Mammographic breast density, adjusted for age and body mass index, and a polygenic risk score (PRS), comprised of common genetic variation, are both strong risk factors for breast cancer and increase discrimination of risk models. Understanding their joint contribution will be important to more accurately predict risk.

METHODS

Using 3628 breast cancer cases and 5126 controls of European ancestry from eight case-control studies, we evaluated joint associations of a 77-single nucleotide polymorphism (SNP) PRS and quantitative mammographic density measures with breast cancer. Mammographic percent density and absolute dense area were evaluated using thresholding software and examined as residuals after adjusting for age, 1/BMI, and study. PRS and adjusted density phenotypes were modeled both continuously (per 1 standard deviation, SD) and categorically. We fit logistic regression models and tested the null hypothesis of multiplicative joint associations for PRS and adjusted density measures using likelihood ratio and global and tail-based goodness of fit tests within the subset of six cohort or population-based studies.

RESULTS

Adjusted percent density (odds ratio (OR) = 1.45 per SD, 95% CI 1.38-1.52), adjusted absolute dense area (OR = 1.34 per SD, 95% CI 1.28-1.41), and the 77-SNP PRS (OR = 1.52 per SD, 95% CI 1.45-1.59) were associated with breast cancer risk. There was no evidence of interaction of the PRS with adjusted percent density or dense area on risk of breast cancer by either the likelihood ratio (P > 0.21) or goodness of fit tests (P > 0.09), whether assessed continuously or categorically. The joint association (OR) was 2.60 in the highest categories of adjusted PD and PRS and 0.34 in the lowest categories, relative to women in the second density quartile and middle PRS quintile.

CONCLUSIONS

The combined associations of the 77-SNP PRS and adjusted density measures are generally well described by multiplicative models, and both risk factors provide independent information on breast cancer risk.

Attributes, Performance, and Gaps in Current & Emerging Breast Cancer Screening Technologies

Background:

Early detection of breast cancer, combined with effective treatment, can reduce mortality. Millions of women are diagnosed with breast cancer and many die every year globally. Numerous early detection screening tests have been employed. A wide range of current breast cancer screening methods are reviewed based on a series of searchers focused on clinical testing and performance. </P><P> Discussion: The key factors evaluated centre around the trade-offs between accuracy (sensitivity and specificity), operator dependence of results, invasiveness, comfort, time required, and cost. All of these factors affect the quality of the screen, access/eligibility, and/or compliance to screening programs by eligible women. This survey article provides an overview of the working principles, benefits, limitations, performance, and cost of current breast cancer detection techniques. It is based on an extensive literature review focusing on published works reporting the main performance, cost, and comfort/compliance metrics considered.

Conclusion:

Due to limitations and drawbacks of existing breast cancer screening methods there is a need for better screening methods. Emerging, non-invasive methods offer promise to mitigate the issues particularly around comfort/pain and radiation dose, which would improve compliance and enable all ages to be screened regularly. However, these methods must still undergo significant validation testing to prove they can provide realistic screening alternatives to the current accepted standards.

Effects of isoflavones on breast tissue and the thyroid hormone system in humans: a comprehensive safety evaluation

Isoflavones are secondary plant constituents of certain foods and feeds such as soy, linseeds, and red clover. Furthermore, isoflavone-containing preparations are marketed as food supplements and so-called dietary food for special medical purposes to alleviate health complaints of peri- and postmenopausal women. Based on the bioactivity of isoflavones, especially their hormonal properties, there is an ongoing discussion regarding their potential adverse effects on human health. This review evaluates and summarises the evidence from interventional and observational studies addressing potential unintended effects of isoflavones on the female breast in healthy women as well as in breast cancer patients and on the thyroid hormone system. In addition, evidence from animal and in vitro studies considered relevant in this context was taken into account along with their strengths and limitations. Key factors influencing the biological effects of isoflavones, e.g., bioavailability, plasma and tissue concentrations, metabolism, temporality (pre- vs. postmenopausal women), and duration of isoflavone exposure, were also addressed. Final conclusions on the safety of isoflavones are guided by the aim of precautionary consumer protection.

Interactions of alcohol and postmenopausal hormone use in regards to mammographic breast density

PURPOSE

We investigated the association of alcohol intake with mammographic breast density in postmenopausal women by their hormone therapy (HT) status.

METHODS

This study included 2,100 cancer-free postmenopausal women within the Nurses' Health Study and Nurses' Health Study II cohorts. Percent breast density (PD), absolute dense (DA), and non-dense areas (NDA) were measured from digitized film mammograms using a computer-assisted thresholding technique; all measures were square root transformed. Alcohol consumption was assessed with a food frequency questionnaire (0, < 5, and ≥ 5 g/day). Information regarding breast cancer risk factors was obtained from baseline or biennial questionnaires closest to the mammogram date. We used generalized linear regression to examine associations between alcohol and breast density measures in women with no HT history, current, and past HT users.

RESULTS

In multivariable analyses, we found no associations of alcohol consumption with PD (p trend = 0.32) and DA (p trend = 0.53) and an inverse association with NDA (β = - 0.41, 95% CI - 0.73, - 0.09 for ≥ 5 g/day, p trend < 0.01). In the stratified analysis by HT status, alcohol was not associated with PD in any of the strata. We found a significant inverse association of alcohol with NDA among past HT users (β = - 0.79, 95% CI - 1.51, - 0.07 for ≥ 5 g/day, p trend = 0.02). There were no significant interactions between alcohol and HT in relation to PD, DA, and NDA (p interaction = 0.19, 0.42, and 0.43, respectively).

CONCLUSIONS

Our findings suggest that associations of alcohol with breast density do not vary by HT status.

Urinary Magnesium and Other Elements in Relation to Mammographic Breast Density, a Measure of Breast Cancer Risk

PURPOSE

Heavy metals and other elements may act as breast carcinogens due to estrogenic activity. We investigated associations between urine concentrations of a panel of elements and breast density.

METHODS

Mammographic density categories were abstracted from radiology reports of 725 women aged 40-65 yr in the Avon Army of Women. A panel of 27 elements was quantified in urine using high resolution magnetic sector inductively coupled plasma mass spectrometry. We applied LASSO (least absolute shrinkage and selection operator) logistic regression to the 27 elements and calculated odds ratios (OR) and 95% confidence intervals (CI) for dense vs. nondense breasts, adjusting for potential confounders.

RESULTS

Of the 27 elements, only magnesium (Mg) was selected into the optimal regression model. The odds ratio for dense breasts associated with doubling the Mg concentration was 1.24 (95% CI 1.03-1.49). Doubling the calcium-to-magnesium ratio was inversely associated with dense breasts (OR 0.83, 95% CI 0.70-0.98).

CONCLUSIONS

Our cross-sectional study found that higher levels of urinary magnesium were associated with greater breast density. Prospective studies are needed to confirm whether magnesium as evaluated in urine is prospectively associated with breast density and, more importantly, breast cancer.

Progesterone receptors (PR) mediate STAT actions: PR and prolactin receptor signaling crosstalk in breast cancer models

Estrogen is the major mitogenic stimulus of mammary gland development during puberty wherein ER signaling acts to induce abundant PR expression. PR signaling, in contrast, is the primary driver of mammary epithelial cell proliferation in adulthood. The high circulating levels of progesterone during pregnancy signal through PR, inducing expression of the prolactin receptor (PRLR). Cooperation between PR and prolactin (PRL) signaling, via regulation of downstream components in the PRL signaling pathway including JAKs and STATs, facilitates the alveolar morphogenesis observed during pregnancy. Indeed, these pathways are fully integrated via activation of shared signaling pathways (i.e. JAKs, MAPKs) as well as by the convergence of PRs and STATs at target genes relevant to both mammary gland biology and breast cancer progression (i.e. proliferation, stem cell outgrowth, tissue cell type heterogeneity). Thus, rather than a single mediator such as ER, transcription factor cascades (ER>PR>STATs) are responsible for rapid proliferative and developmental programming in the normal mammary gland. It is not surprising that these same mediators typify uncontrolled proliferation in a majority of breast cancers, where ER and PR are most often co-expressed and may cooperate to drive malignant tumor progression. This review will primarily focus on the integration of PR and PRL signaling in breast cancer models and the importance of this cross-talk in cancer progression in the context of mammographic density. Components of these PR/PRL signaling pathways could offer alternative drug targets and logical complements to anti-ER or anti-estrogen-based endocrine therapies.

Evaluation of nipple aspirate fluid as a diagnostic tool for early detection of breast cancer

There has been tremendous progress in detection of breast cancer in postmenopausal women, resulting in two-thirds of women surviving more than 20 years after treatment. However, breast cancer remains the leading cause of cancer-related deaths in premenopausal women. Breast cancer is increasing in younger women due to changes in life-style as well as those at high risk as carriers of mutations in high-penetrance genes. Premenopausal women with breast cancer are more likely to be diagnosed with aggressive tumours and therefore have a lower survival rate. Mammography plays an important role in detecting breast cancer in postmenopausal women, but is considerably less sensitive in younger women. Imaging techniques, such as contrast-enhanced MRI improve sensitivity, but as with all imaging approaches, cannot differentiate between benign and malignant growths. Hence, current well-established detection methods are falling short of providing adequate safety, convenience, sensitivity and specificity for premenopausal women on a global level, necessitating the exploration of new methods. In order to detect and prevent the disease in high risk women as early as possible, methods that require more frequent monitoring need to be developed. The emergence of "omics" strategies over the last 20 years, enabling the characterisation and understanding of breast cancer at the molecular level, are providing the potential for long term, longitudinal monitoring of the disease. Tissue and serum biomarkers for breast cancer stratification, diagnosis and predictive outcome have emerged, but have not successfully translated into clinical screening for early detection of the disease. The use of breast-specific liquid biopsies, such as nipple aspirate fluid (NAF), a natural secretion produced by breast epithelial cells, can be collected non-invasively for biomarker profiling. As we move towards an age of active surveillance, home-based liquid biopsy collection kits are increasingly being applied and these could provide a paradigm shift where NAF biomarker profiling is used for routine breast health monitoring. The current status of established and newly emerging imaging techniques for early detection of breast cancer and the potential for alternative biomarker screening of liquid biopsies, particularly those applied to high-risk, premenopausal women, will be reviewed.

Computer-aided assessment of breast density: comparison of supervised deep learning and feature-based statistical learning

Breast density is one of the most significant factors that is associated with cancer risk. In this study, our purpose was to develop a supervised deep learning approach for automated estimation of percentage density (PD) on digital mammograms (DMs). The input 'for processing' DMs was first log-transformed, enhanced by a multi-resolution preprocessing scheme, and subsampled to a pixel size of 800 µm  ×  800 µm from 100 µm  ×  100 µm. A deep convolutional neural network (DCNN) was trained to estimate a probability map of breast density (PMD) by using a domain adaptation resampling method. The PD was estimated as the ratio of the dense area to the breast area based on the PMD. The DCNN approach was compared to a feature-based statistical learning approach. Gray level, texture and morphological features were extracted and a least absolute shrinkage and selection operator was used to combine the features into a feature-based PMD. With approval of the Institutional Review Board, we retrospectively collected a training set of 478 DMs and an independent test set of 183 DMs from patient files in our institution. Two experienced mammography quality standards act radiologists interactively segmented PD as the reference standard. Ten-fold cross-validation was used for model selection and evaluation with the training set. With cross-validation, DCNN obtained a Dice's coefficient (DC) of 0.79  ±  0.13 and Pearson's correlation (r) of 0.97, whereas feature-based learning obtained DC  =  0.72  ±  0.18 and r  =  0.85. For the independent test set, DCNN achieved DC  =  0.76  ±  0.09 and r  =  0.94, while feature-based learning achieved DC  =  0.62  ±  0.21 and r  =  0.75. Our DCNN approach was significantly better and more robust than the feature-based learning approach for automated PD estimation on DMs, demonstrating its potential use for automated density reporting as well as for model-based risk prediction.

Association between mammographic breast density and histologic features of benign breast disease

BACKGROUND

Over 40% of women undergoing breast screening have mammographically dense breasts. Elevated mammographic breast density (MBD) is an established breast cancer risk factor and is known to mask tumors within the dense tissue. However, the association of MBD with high risk benign breast disease (BBD) is unknown.

METHOD

We analyzed data for 3400 women diagnosed with pathologically confirmed BBD in the Mayo Clinic BBD cohort from 1985-2001, with a clinical MBD measure (either parenchymal pattern (PP) or Breast Imaging Reporting and Data Systems (BI-RADS) density) and expert pathology review. Risk factor information was collected from medical records and questionnaires. MBD was dichotomized as dense (PP classification P2 or DY, or BI-RADS classification c or d) or non-dense (PP classification N1 or P1, or BI-RADS classification a or b). Associations of clinical and histologic characteristics with MBD were examined using logistic regression analysis to estimate odds ratios (ORs) with 95% confidence intervals (CIs).

RESULTS

Of 3400 women in the study, 2163 (64%) had dense breasts. Adjusting for age and body mass index (BMI), there were positive associations of dense breasts with use of hormone therapy (HT), lack of lobular involution, presence of atypical lobular hyperplasia (ALH), histologic fibrosis, columnar cell hyperplasia/flat epithelia atypia (CCH/FEA), sclerosing adenosis (SA), cyst, usual ductal hyperplasia, and calcifications. In fully adjusted multivariate models, HT (1.3, 95% CI 1.1-1.5), ALH (1.5, 95% CI 1.0-2.2), lack of lobular involution (OR 1.6, 95% CI 1.2-2.1, compared to complete involution), fibrosis (OR 2.2, 95% CI 1.9-2.6) and CCH/FEA (OR 1.3, 95% CI 1.0-1.6) remained significantly associated with high MBD.

CONCLUSION

Our findings support an association between high risk BBD and high MBD, suggesting that risks associated with the latter may act early in breast carcinogenesis.

Interaction of mammographic breast density with menopausal status and postmenopausal hormone use in relation to the risk of aggressive breast cancer subtypes

PURPOSE

We examined the associations of mammographic breast density with breast cancer risk by tumor aggressiveness and by menopausal status and current postmenopausal hormone therapy.

METHODS

This study included 2596 invasive breast cancer cases and 4059 controls selected from participants of four nested case-control studies within four established cohorts: the Mayo Mammography Health Study, the Nurses' Health Study, Nurses' Health Study II, and San Francisco Mammography Registry. Percent breast density (PD), absolute dense (DA), and non-dense areas (NDA) were assessed from digitized film-screen mammograms using a computer-assisted threshold technique and standardized across studies. We used polytomous logistic regression to quantify the associations of breast density with breast cancer risk by tumor aggressiveness (defined as presence of at least two of the following tumor characteristics: size ≥2 cm, grade 2/3, ER-negative status, or positive nodes), stratified by menopausal status and current hormone therapy.

RESULTS

Overall, the positive association of PD and borderline inverse association of NDA with breast cancer risk was stronger in aggressive vs. non-aggressive tumors (≥51 vs. 11-25% OR 2.50, 95% CI 1.94-3.22 vs. OR 2.03, 95% CI 1.70-2.43, p-heterogeneity = 0.03; NDA 4th vs. 2nd quartile OR 0.54, 95% CI 0.41-0.70 vs. OR 0.71, 95% CI 0.59-0.85, p-heterogeneity = 0.07). However, there were no differences in the association of DA with breast cancer by aggressive status. In the stratified analysis, there was also evidence of a stronger association of PD and NDA with aggressive tumors among postmenopausal women and, in particular, current estrogen+progesterone users (≥51 vs. 11-25% OR 3.24, 95% CI 1.75-6.00 vs. OR 1.93, 95% CI 1.25-2.98, p-heterogeneity = 0.01; NDA 4th vs. 2nd quartile OR 0.43, 95% CI 0.21-0.85 vs. OR 0.56, 95% CI 0.35-0.89, p-heterogeneity = 0.01), even though the interaction was not significant.

CONCLUSION

Our findings suggest that associations of mammographic density with breast cancer risk differ by tumor aggressiveness. While there was no strong evidence that these associations differed by menopausal status or hormone therapy, they did appear more prominent among current estrogen+progesterone users.

3D MRI for Quantitative Analysis of Quadrant Percent Breast Density: Correlation with Quadrant Location of Breast Cancer

RATIONALE AND OBJECTIVES

Breast cancer occurs more frequently in the upper outer (UO) quadrant, but whether this higher cancer incidence is related to the greater amount of dense tissue is not known. Magnetic resonance imaging acquires three-dimensional volumetric images and is the most suitable among all breast imaging modalities for regional quantification of density. This study applied a magnetic resonance imaging-based method to measure quadrant percent density (QPD), and evaluated its association with the quadrant location of the developed breast cancer.

MATERIALS AND METHODS

A total of 126 cases with pathologically confirmed breast cancer were reviewed. Only women who had unilateral breast cancer located in a clear quadrant were selected for analysis. A total of 84 women, including 47 Asian women and 37 western women, were included. An established computer-aided method was used to segment the diseased breast and the contralateral normal breast, and to separate the dense and fatty tissues. Then, a breast was further separated into four quadrants using the nipple and the centroid as anatomic landmarks. The tumor was segmented using a computer-aided method to determine its quadrant location. The distribution of cancer quadrant location, the quadrant with the highest QPD, and the proportion of cancers occurring in the highest QPD were analyzed.

RESULTS

The highest incidence of cancer occurred in the UO quadrant (36 out of 84, 42.9%). The highest QPD was also noted most frequently in the UO quadrant (31 out of 84, 36.9%). When correlating the highest QPD with the quadrant location of breast cancer, only 17 women out of 84 (20.2%) had breast cancer occurring in the quadrant with the highest QPD.

CONCLUSIONS

The results showed that the development of breast cancer in a specific quadrant could not be explained by the density in that quadrant, and further studies are needed to find the biological reasons accounting for the higher breast cancer incidence in the UO quadrant.

Evaluation of the association between quantitative mammographic density and breast cancer occurred in different quadrants

BACKGROUND

To investigate the relationship between mammographic density measured in four quadrants of a breast with the location of the occurred cancer.

METHODS

One hundred and ten women diagnosed with unilateral breast cancer that could be determined in one specific breast quadrant were retrospectively studied. Women with previous cancer/breast surgery were excluded. The craniocaudal (CC) and mediolateral oblique (MLO) mammography of the contralateral normal breast were used to separate a breast into 4 quadrants: Upper-Outer (UO), Upper-Inner (UI), Lower-Outer (LO), and Lower-Inner (LI). The breast area (BA), dense area (DA), and percent density (PD) in each quadrant were measured by using the fuzzy-C-means segmentation. The BA, DA, and PD were compared between patients who had cancer occurring in different quadrants.

RESULTS

The upper-outer quadrant had the highest BA (37 ± 15 cm²) and DA (7.1 ± 2.9 cm²), with PD = 20.0 ± 5.8%. The order of BA and DA in the 4 separated quadrants were: UO > UI > LO > LI, and almost all pair-wise comparisons showed significant differences. For tumor location, 67 women (60.9%) had tumor in UO, 16 (14.5%) in UI, 7 (6.4%) in LO, and 20 (18.2%) in LI quadrant, respectively. The estimated odds and the 95% confidence limits of tumor development in the UO, UI, LO and LI quadrants were 1.56 (1.06, 2.29), 0.17 (0.10, 0.29), 0.07 (0.03, 0.15), and 0.22 (0.14, 0.36), respectively. In these 4 groups of women, the order of quadrant BA and DA were all the same (UO > UI > LO > LI), and there was no significant difference in BA, DA or PD among them (all p > 0.05).

CONCLUSIONS

Breast cancer was most likely to occur in the UO quadrant, which was also the quadrant with highest BA and DA; but for women with tumors in other quadrants, the density in that quadrant was not the highest. Therefore, there was no direct association between quadrant density and tumor occurrence.

Racial Differences in Quantitative Measures of Area and Volumetric Breast Density

BACKGROUND

Increased breast density is a strong risk factor for breast cancer and also decreases the sensitivity of mammographic screening. The purpose of our study was to compare breast density for black and white women using quantitative measures.

METHODS

Breast density was assessed among 5282 black and 4216 white women screened using digital mammography. Breast Imaging-Reporting and Data System (BI-RADS) density was obtained from radiologists' reports. Quantitative measures for dense area, area percent density (PD), dense volume, and volume percent density were estimated using validated, automated software. Breast density was categorized as dense or nondense based on BI-RADS categories or based on values above and below the median for quantitative measures. Logistic regression was used to estimate the odds of having dense breasts by race, adjusted for age, body mass index (BMI), age at menarche, menopause status, family history of breast or ovarian cancer, parity and age at first birth, and current hormone replacement therapy (HRT) use. All statistical tests were two-sided.

RESULTS

There was a statistically significant interaction of race and BMI on breast density. After accounting for age, BMI, and breast cancer risk factors, black women had statistically significantly greater odds of high breast density across all quantitative measures (eg, PD nonobese odds ratio [OR] = 1.18, 95% confidence interval [CI] = 1.02 to 1.37, P = .03, PD obese OR = 1.26, 95% CI = 1.04 to 1.53, P = .02). There was no statistically significant difference in BI-RADS density by race.

CONCLUSIONS

After accounting for age, BMI, and other risk factors, black women had higher breast density than white women across all quantitative measures previously associated with breast cancer risk. These results may have implications for risk assessment and screening.

Opportunistic Breast Density Assessment in Women Receiving Low-dose Chest Computed Tomography Screening

RATIONALE AND OBJECTIVES

Low-dose chest computed tomography (LDCT), increasingly being used for screening of lung cancer, may also be used to measure breast density, which is proven as a risk factor for breast cancer. In this study, we developed a segmentation method to measure quantitative breast density on CT images and correlated with magnetic resonance density.

MATERIALS AND METHODS

Forty healthy women receiving both LDCT and breast magnetic resonance imaging (MRI) were studied. A semiautomatic method was applied to quantify the breast density on LDCT images. The intra- and interoperator reproducibility was evaluated. The volumetric density on MRI was obtained by using a well-established automatic template-based segmentation method. The breast volume (BV), fibroglandular tissue volume (FV), and percent breast density (PD) measured on LDCT and MRI were compared.

RESULTS

The measurements of BV, FV, and PD on LDCT images yield highly consistent results, with the intraclass correlation coefficient of 0.999 for BV, 0.977 for FV, and 0.966 for PD for intraoperator reproducibility, and intraclass correlation coefficient of 0.953 for BV, 0.974 for FV, and 0.973 for PD for interoperator reproducibility. The BV, FV, and PD measured on LDCT and MRI were well correlated (all r ≥ 0.90). Bland-Altman plots showed that a larger BV and FV were measured on LDCT than on MRI.

CONCLUSIONS

The preliminary results showed that quantitative breast density can be measured from LDCT, and that our segmentation method could yield a high reproducibility on the measured volume and PD. The results measured on LDCT and MRI were highly correlated. Our results showed that LDCT may provide valuable information about breast density for evaluating breast cancer risk.

Adolescent fiber intake and mammographic breast density in premenopausal women

Background

To date, there is limited and inconsistent epidemiologic evidence for associations of adolescent diet with mammographic breast density, a strong and consistent predictor of breast cancer. We investigated the association of adolescent fiber intake with mammographic density in premenopausal women.

Methods

This study included 743 cancer-free premenopausal women (mean age, 44.9 years) within the Nurses’ Health Study II cohort. Percent breast density, absolute dense and non-dense areas were measured from digitized film mammograms using a computer-assisted thresholding technique. Adolescent and adult diet were assessed with a food frequency questionnaire; energy-adjusted nutrient intakes were estimated for each food item. Information regarding breast cancer risk factors was obtained from baseline or biennial questionnaires closest to the mammogram date. We used generalized linear regression to quantify associations between quartiles of adolescent fiber intake and each of the breast density measures, adjusted for potential confounders. Associations were examined separately for total fiber intake; fiber from fruits, vegetables, legumes, and cereal; and food sources of fiber (fruits, vegetables, and nuts).

Results

In multivariable analyses, total fiber intake during adolescence was not associated with percent breast density (p for trend = 0.64), absolute dense area (p for trend = 0.80), or non-dense area (p for trend = 0.75). Similarly, neither consumption of fiber from fruits, vegetables, legumes, or cereal nor specific sources of fiber intake (fruits, vegetables, or nuts) during adolescence were associated with any of the mammographic density phenotypes.

Conclusions

Our findings do not support the hypothesis that adolescent fiber intake is associated with premenopausal mammographic breast density.

Improvement in risk prediction, early detection and prevention of breast cancer in the NHS Breast Screening Programme and family history clinics: a dual cohort study

Background

In the UK, women are invited for 3-yearly mammography screening, through the NHS Breast Screening Programme (NHSBSP), from the ages of 47–50 years to the ages of 69–73 years. Women with family histories of breast cancer can, from the age of 40 years, obtain enhanced surveillance and, in exceptionally high-risk cases, magnetic resonance imaging. However, no NHSBSP risk assessment is undertaken. Risk prediction models are able to categorise women by risk using known risk factors, although accurate individual risk prediction remains elusive. The identification of mammographic breast density (MD) and common genetic risk variants [single nucleotide polymorphisms (SNPs)] has presaged the improved precision of risk models.

Objectives

To (1) identify the best performing model to assess breast cancer risk in family history clinic (FHC) and population settings; (2) use information from MD/SNPs to improve risk prediction; (3) assess the acceptability and feasibility of offering risk assessment in the NHSBSP; and (4) identify the incremental costs and benefits of risk stratified screening in a preliminary cost-effectiveness analysis.

Design

Two cohort studies assessing breast cancer incidence.

Setting

High-risk FHC and the NHSBSP Greater Manchester, UK.

Participants

A total of 10,000 women aged 20–79 years [Family History Risk Study (FH-Risk); UK Clinical Research Network identification number (UKCRN-ID) 8611] and 53,000 women from the NHSBSP [aged 46–73 years; Predicting the Risk of Cancer At Screening (PROCAS) study; UKCRN-ID 8080].

Interventions

Questionnaires collected standard risk information, and mammograms were assessed for breast density by a number of techniques. All FH-Risk and 10,000 PROCAS participants participated in deoxyribonucleic acid (DNA) studies. The risk prediction models Manual method, Tyrer–Cuzick (TC), BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) and Gail were used to assess risk, with modelling based on MD and SNPs. A preliminary model-based cost-effectiveness analysis of risk stratified screening was conducted.

Main outcome measures

Breast cancer incidence.

Data sources

The NHSBSP; cancer registration.

Results

A total of 446 women developed incident breast cancers in FH-Risk in 97,958 years of follow-up. All risk models accurately stratified women into risk categories. TC had better risk precision than Gail, and BOADICEA accurately predicted risk in the 6268 single probands. The Manual model was also accurate in the whole cohort. In PROCAS, TC had better risk precision than Gail [area under the curve (AUC) 0.58 vs. 0.54], identifying 547 prospective breast cancers. The addition of SNPs in the FH-Risk case–control study improved risk precision but was not useful inBRCA1(breast cancer 1 gene) families. Risk modelling of SNPs in PROCAS showed an incremental improvement from using SNP18 used in PROCAS to SNP67. MD measured by visual assessment score provided better risk stratification than automatic measures, despite wide intra- and inter-reader variability. Using a MD-adjusted TC model in PROCAS improved risk stratification (AUC = 0.6) and identified significantly higher rates (4.7 per 10,000 vs. 1.3 per 10,000;p < 0.001) of high-stage cancers in women with above-average breast cancer risks. It is not possible to provide estimates of the incremental costs and benefits of risk stratified screening because of lack of data inputs for key parameters in the model-based cost-effectiveness analysis.

Conclusions

Risk precision can be improved by using DNA and MD, and can potentially be used to stratify NHSBSP screening. It may also identify those at greater risk of high-stage cancers for enhanced screening. The cost-effectiveness of risk stratified screening is currently associated with extensive uncertainty. Additional research is needed to identify data needed for key inputs into model-based cost-effectiveness analyses to identify the impact on health-care resource use and patient benefits.

Future work

A pilot of real-time NHSBSP risk prediction to identify women for chemoprevention and enhanced screening is required.

Funding

The National Institute for Health Research Programme Grants for Applied Research programme. The DNA saliva collection for SNP analysis for PROCAS was funded by the Genesis Breast Cancer Prevention Appeal.

Volumetric breast density measurement: sensitivity analysis of a relative physics approach

OBJECTIVE

To investigate the sensitivity and robustness of a volumetric breast density (VBD) measurement system to errors in the imaging physics parameters including compressed breast thickness (CBT), tube voltage (kVp), filter thickness, tube current-exposure time product (mAs), detector gain, detector offset and image noise.

METHODS

3317 raw digital mammograms were processed with Volpara(®) (Matakina Technology Ltd, Wellington, New Zealand) to obtain fibroglandular tissue volume (FGV), breast volume (BV) and VBD. Errors in parameters including CBT, kVp, filter thickness and mAs were simulated by varying them in the Digital Imaging and Communications in Medicine (DICOM) tags of the images up to ±10% of the original values. Errors in detector gain and offset were simulated by varying them in the Volpara configuration file up to ±10% from their default values. For image noise, Gaussian noise was generated and introduced into the original images.

RESULTS

Errors in filter thickness, mAs, detector gain and offset had limited effects on FGV, BV and VBD. Significant effects in VBD were observed when CBT, kVp, detector offset and image noise were varied (p < 0.0001). Maximum shifts in the mean (1.2%) and median (1.1%) VBD of the study population occurred when CBT was varied.

CONCLUSION

Volpara was robust to expected clinical variations, with errors in most investigated parameters giving limited changes in results, although extreme variations in CBT and kVp could lead to greater errors.

ADVANCES IN KNOWLEDGE

Despite Volpara's robustness, rigorous quality control is essential to keep the parameter errors within reasonable bounds. Volpara appears robust within those bounds, albeit for more advanced applications such as tracking density change over time, it remains to be seen how accurate the measures need to be.

Stiffness of the microenvironment upregulates ERBB2 expression in 3D cultures of MCF10A within the range of mammographic density

The effects of the stiffness of the microenvironment on the molecular response of 3D colony organization, at the maximum level of mammographic density (MD), are investigated. Phenotypic profiling reveals that 3D colony formation is heterogeneous and increased stiffness of the microenvironment, within the range of the MD, correlates with the increased frequency of aberrant 3D colony formation. Further integrative analysis of the genome-wide transcriptome and phenotypic profiling hypothesizes overexpression of ERBB2 in the premalignant MCF10A cell lines at a stiffness value that corresponds to the collagen component at high mammographic density. Subsequently, ERBB2 overexpression has been validated in the same cell line. Similar experiments with a more genetically stable cell line of 184A1 also revealed an increased frequency of aberrant colony formation with the increased stiffness; however, 184A1 did not demonstrate overexpression of ERBB2 at the same stiffness value of the high MD. These results suggest that stiffness exacerbates premalignant cell line of MCF10A.

Reproductive factors related to childbearing and mammographic breast density

We investigated the associations of reproductive factors related to childbearing with percent breast density, absolute dense and nondense areas, by menopausal status. This study included 4110 cancer-free women within the Nurses' Health Study and Nurses' Health Study II cohorts. Percent breast density, absolute dense and nondense areas were measured from digitized mammography film images with computerized techniques. All density measures were square root-transformed in all the analyses to improve normality. The data on reproductive variables and other breast cancer risk factors were obtained from biennial questionnaires, at the time of the mammogram date. As compared to nulliparous women, parous postmenopausal women had lower percent density (β = -0.60, 95 % CI -0.84; -0.37), smaller absolute dense area (β = -0.66, 95 % CI -1.03; -0.29), and greater nondense area (β = 0.72, 95 % CI 0.27; 1.16). Among parous women, number of children was inversely associated with percent density in pre- (β per one child = -0.12, 95 % CI -0.20; -0.05) and postmenopausal women (β per one child = -0.07, 95 % CI -0.12; -0.02). The positive associations of breastfeeding with absolute dense and nondense areas were limited to premenopausal women, while the positive association of the age at first child's birth with percent density and the inverse association with nondense area were limited to postmenopausal women. Women with greater number of children and younger age at first child's birth have more favorable breast density patterns that could explain subsequent breast cancer risk reduction.

Breast Density Analysis Using an Automatic Density Segmentation Algorithm

Breast density is a strong risk factor for breast cancer. In this paper, we present an automated approach for breast density segmentation in mammographic images based on a supervised pixel-based classification and using textural and morphological features. The objective of the paper is not only to show the feasibility of an automatic algorithm for breast density segmentation but also to prove its potential application to the study of breast density evolution in longitudinal studies. The database used here contains three complete screening examinations, acquired 2 years apart, of 130 different patients. The approach was validated by comparing manual expert annotations with automatically obtained estimations. Transversal analysis of the breast density analysis of craniocaudal (CC) and mediolateral oblique (MLO) views of both breasts acquired in the same study showed a correlation coefficient of ρ = 0.96 between the mammographic density percentage for left and right breasts, whereas a comparison of both mammographic views showed a correlation of ρ = 0.95. A longitudinal study of breast density confirmed the trend that dense tissue percentage decreases over time, although we noticed that the decrease in the ratio depends on the initial amount of breast density.

Reliability of Computer-Assisted Breast Density Estimation: Comparison of Interactive Thresholding, Semiautomated, and Fully Automated Methods

OBJECTIVE

The purpose of this study was to investigate the reliability of computer-assisted methods of estimating breast density.

MATERIALS AND METHODS

Craniocaudal mammograms of 100 healthy subjects were collected from a screening mammography database. Three expert readers independently assessed mammographic breast density twice in a 1-month period using interactive thresholding and semiautomated methods. In addition, fully automated breast density estimation software was used to generate objective breast density estimates. The reliability of the computer-assisted breast density estimation was assessed in terms of concordance correlation coefficients, limits of agreement, systematic difference, and reader variability.

RESULTS

Statistically significant systematic bias (paired t test, p < 0.01) and variability (4.75-10.91) were found within and between readers for both the interactive thresholding and the semiautomated methods. Using the semiautomated method significantly reduced the within-reader bias of one reader (p < 0.02) and the between-reader variability of all three readers (p < 0.05). The breast density estimates obtained with the fully automated method had excellent agreement with those of the reference standard (concordance correlation coefficient, 0.93) without a significant systematic difference.

CONCLUSION

Reader-dependent variability and systematic bias exist in breast density estimates obtained with the interactive thresholding method, but they may be reduced in part by use of the semiautomated method. Assessing reader performance may be necessary for more reliable breast density estimation, especially for surveillance of breast density over time. The fully automated method has the potential to provide reliable breast density estimates nearly free from reader-dependent systematic bias and reader variability.