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Background: Breast density impacts upon breast cancer risk and recurrence, but its influence on breast cancer-specific survival is unclear. This study examines the influence of mammographic breast density (MBD) at diagnosis on breast cancer-specific survival. Methods: The data of 224 patients diagnosed with breast cancer were analyzed. Two area-based MBD measurement tools—AutoDensity and LIBRA—were used to measure MBD via a mammogram of the contralateral breast acquired at the time of diagnosis. These patients were split into two groups based on their percent breast density (PBD): high (PBD ≥ 20%) versus low (PBD < 20%). Breast cancer-specific survival in each of these PBD groups was assessed at a median follow-up of 34 months using Kaplan–Meier analysis and the Cox proportional hazards model. Results: The proportion of women with low PBD who died from breast cancer was significantly higher than that seen with high PBD (p = 0.01). The 5-year breast cancer-specific survival was poorer among women with low PBD than those with high PBD (0.348; 95% CI: 0.13–0.94) vs. 0.87; 95% CI: (0.8–0.96); p < 0.001)]. Women with higher breast density demonstrated longer survival regardless of the method of PBD measurement: LIBRA [log-rank test (Mantel–Cox): 9.4; p = 0.002)]; AutoDensity [log-rank test (Mantel–Cox) 7.6; p = 0.006]. Multivariate analysis also demonstrated that there was a higher risk of breast cancer-related deaths in women with low PBD (adjusted HR: 5.167; 95% CI: 1.974–13.521; p = 0.001). Conclusion: Women with <20% breast density at breast cancer diagnosis demonstrate poor survival regarding the disease. The impact of breast density on survival is not influenced by the method of measurement. [ABSTRACT FROM AUTHOR]

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Background: Elevated mammographic density (MD) for a woman's age and body mass index (BMI) is an established breast cancer risk factor. The relationship of parity, age at first birth, and breastfeeding with MD is less clear. We examined the associations of these factors with MD within the International Consortium of Mammographic Density (ICMD). Methods: ICMD is a consortium of 27 studies with pooled individual-level epidemiological and MD data from 11,755 women without breast cancer aged 35–85 years from 22 countries, capturing 40 country-& ethnicity-specific population groups. MD was measured using the area-based tool Cumulus. Meta-analyses across population groups and pooled analyses were used to examine linear regression associations of square-root (√) transformed MD measures (percent MD (PMD), dense area (DA), and non-dense area (NDA)) with parity, age at first birth, ever/never breastfed and lifetime breastfeeding duration. Models were adjusted for age at mammogram, age at menarche, BMI, menopausal status, use of hormone replacement therapy, calibration method, mammogram view and reader, and parity and age at first birth when not the association of interest. Results: Among 10,988 women included in these analyses, 90.1% (n = 9,895) were parous, of whom 13% (n = 1,286) had ≥ five births. The mean age at first birth was 24.3 years (Standard deviation = 5.1). Increasing parity (per birth) was inversely associated with √PMD (β: − 0.05, 95% confidence interval (CI): − 0.07, − 0.03) and √DA (β: − 0.08, 95% CI: − 0.12, − 0.05) with this trend evident until at least nine births. Women who were older at first birth (per five-year increase) had higher √PMD (β:0.06, 95% CI:0.03, 0.10) and √DA (β:0.06, 95% CI:0.02, 0.10), and lower √NDA (β: − 0.06, 95% CI: − 0.11, − 0.01). In stratified analyses, this association was only evident in women who were post-menopausal at MD assessment. Among parous women, no associations were found between ever/never breastfed or lifetime breastfeeding duration (per six-month increase) and √MD. Conclusions: Associations with higher parity and older age at first birth with √MD were consistent with the direction of their respective associations with breast cancer risk. Further research is needed to understand reproductive factor-related differences in the composition of breast tissue and their associations with breast cancer risk. [ABSTRACT FROM AUTHOR]

Background: Mammographic density has been associated with breast cancer risk, and is modulated by established breast cancer risk factors, such as reproductive and hormonal history, as well as lifestyle. Recent epidemiological and biological findings underscore the recognized benefits of breastfeeding in reducing breast cancer risk, especially for aggressive subtypes. Current research exploring the association among mammographic density, breastfeeding, and breast cancer is sparse. Main findings: Changes occur in the breasts during pregnancy in preparation for lactation, characterized by the proliferation of mammary gland tissues and the development of mammary alveoli. During lactation, the alveoli fill with milk, and subsequent weaning triggers the involution and remodeling of these tissues. Breastfeeding influences the breast microenvironment, potentially altering mammographic density. When breastfeeding is not initiated after birth, or is abruptly discontinued shortly after, the breast tissue undergoes forced and abrupt involution. Conversely, when breastfeeding is sustained over an extended period and concludes gradually, the breast tissue undergoes slow remodeling process known as gradual involution. Breast tissue undergoing abrupt involution displays denser stroma, altered collagen composition, heightened inflammation and proliferation, along with increased expression of estrogen receptor α (ERα) and progesterone receptor. Furthermore, elevated levels of pregnancy-associated plasma protein-A (PAPP-A) surpass those of its inhibitors during abrupt involution, enhancing insulin-like growth factor (IGF) signaling and collagen deposition. Prolactin and small molecules in breast milk may also modulate DNA methylation levels. Drawing insights from contemporary epidemiological and molecular biology studies, our review sheds light on how breastfeeding impacts mammographic density and explores its role in influencing breast cancer. Conclusion: This review highlights a clear protective link between breastfeeding and reduced breast cancer risk via changes in mammographic density. Future research should investigate the effects of breastfeeding on mammographic density and breast cancer risk among various ethnic groups and elucidate the molecular mechanisms underlying these associations. Such comprehensive research will enhance our understanding and facilitate the development of targeted breast cancer prevention and treatment strategies. [ABSTRACT FROM AUTHOR]

Objectives: To compare the location of AI markings on screening mammograms with cancer location on diagnostic mammograms, and to classify interval cancers with high AI score as false negative, minimal sign, or true negative. Methods: In a retrospective study from 2022, we compared the performance of an AI system with independent double reading according to cancer detection. We found 93% (880/949) of the screen-detected cancers, and 40% (122/305) of the interval cancers to have the highest AI risk score (AI score of 10). In this study, four breast radiologists reviewed mammograms from 126 randomly selected screen-detected cancers and all 120 interval cancers with an AI score of 10. The location of the AI marking was stated as correct/not correct in craniocaudal and mediolateral oblique view. Interval cancers with an AI score of 10 were classified as false negative, minimal sign significant/non-specific, or true negative. Results: All screen-detected cancers and 78% (93/120) of the interval cancers with an AI score of 10 were correctly located by the AI system. The AI markings matched in both views for 79% (100/126) of the screen-detected cancers and 22% (26/120) of the interval cancers. For interval cancers with an AI score of 10, 11% (13/120) were correctly located and classified as false negative, 10% (12/120) as minimal sign significant, 26% (31/120) as minimal sign non-specific, and 31% (37/120) as true negative. Conclusion: AI markings corresponded to cancer location for all screen-detected cancers and 78% of the interval cancers with high AI score, indicating a potential for reducing the number of interval cancers. However, it is uncertain whether interval cancers with subtle findings in only one view are actionable for recall in a true screening setting. Clinical relevance statement: In this study, AI markings corresponded to the location of the cancer in a high percentage of cases, indicating that the AI system accurately identifies the cancer location in mammograms with a high AI score. Key Points: • All screen-detected and 78% of the interval cancers with high AI risk score (AI score of 10) had AI markings in one or two views corresponding to the location of the cancer on diagnostic images. • Among all 120 interval cancers with an AI score of 10, 21% (25/120) were classified as a false negative or minimal sign significant and had AI markings matching the cancer location, suggesting they may be visible on prior screening. • Most of the correctly located interval cancers matched only in one view, and the majority were classified as either true negative or minimal sign non-specific, indicating low potential for being detected earlier in a real screening setting. [ABSTRACT FROM AUTHOR]

Mammographic density (MD) assessment is subject to inter- and intra-observer variability. An automated method, such as Quantra software, could be a useful tool for an objective and reproducible MD assessment. Our purpose was to evaluate the performance of Quantra software in assessing MD, according to BI-RADS® Atlas Fifth Edition recommendations, verifying the degree of agreement with the gold standard, given by the consensus of two breast radiologists. A total of 5009 screening examinations were evaluated by two radiologists and analysed by Quantra software to assess MD. The agreement between the three assigned values was expressed as intraclass correlation coefficients (ICCs). The agreement between the software and the two readers (R1 and R2) was moderate with ICC values of 0.725 and 0.713, respectively. A better agreement was demonstrated between the software's assessment and the average score of the values assigned by the two radiologists, with an index of 0.793, which reflects a good correlation. Quantra software appears a promising tool in supporting radiologists in the MD assessment and could be part of a personalised screening protocol soon. However, some fine-tuning is needed to improve its accuracy, reduce its tendency to overestimate, and ensure it excludes high-density structures from its assessment. [ABSTRACT FROM AUTHOR]

Abstract Background Mammographic density has been associated with breast cancer risk, and is modulated by established breast cancer risk factors, such as reproductive and hormonal history, as well as lifestyle. Recent epidemiological and biological findings underscore the recognized benefits of breastfeeding in reducing breast cancer risk, especially for aggressive subtypes. Current research exploring the association among mammographic density, breastfeeding, and breast cancer is sparse. Main findings Changes occur in the breasts during pregnancy in preparation for lactation, characterized by the proliferation of mammary gland tissues and the development of mammary alveoli. During lactation, the alveoli fill with milk, and subsequent weaning triggers the involution and remodeling of these tissues. Breastfeeding influences the breast microenvironment, potentially altering mammographic density. When breastfeeding is not initiated after birth, or is abruptly discontinued shortly after, the breast tissue undergoes forced and abrupt involution. Conversely, when breastfeeding is sustained over an extended period and concludes gradually, the breast tissue undergoes slow remodeling process known as gradual involution. Breast tissue undergoing abrupt involution displays denser stroma, altered collagen composition, heightened inflammation and proliferation, along with increased expression of estrogen receptor α (ERα) and progesterone receptor. Furthermore, elevated levels of pregnancy-associated plasma protein-A (PAPP-A) surpass those of its inhibitors during abrupt involution, enhancing insulin-like growth factor (IGF) signaling and collagen deposition. Prolactin and small molecules in breast milk may also modulate DNA methylation levels. Drawing insights from contemporary epidemiological and molecular biology studies, our review sheds light on how breastfeeding impacts mammographic density and explores its role in influencing breast cancer. Conclusion This review highlights a clear protective link between breastfeeding and reduced breast cancer risk via changes in mammographic density. Future research should investigate the effects of breastfeeding on mammographic density and breast cancer risk among various ethnic groups and elucidate the molecular mechanisms underlying these associations. Such comprehensive research will enhance our understanding and facilitate the development of targeted breast cancer prevention and treatment strategies.

Abstract Background Elevated mammographic density (MD) for a woman’s age and body mass index (BMI) is an established breast cancer risk factor. The relationship of parity, age at first birth, and breastfeeding with MD is less clear. We examined the associations of these factors with MD within the International Consortium of Mammographic Density (ICMD). Methods ICMD is a consortium of 27 studies with pooled individual-level epidemiological and MD data from 11,755 women without breast cancer aged 35–85 years from 22 countries, capturing 40 country-& ethnicity-specific population groups. MD was measured using the area-based tool Cumulus. Meta-analyses across population groups and pooled analyses were used to examine linear regression associations of square-root (√) transformed MD measures (percent MD (PMD), dense area (DA), and non-dense area (NDA)) with parity, age at first birth, ever/never breastfed and lifetime breastfeeding duration. Models were adjusted for age at mammogram, age at menarche, BMI, menopausal status, use of hormone replacement therapy, calibration method, mammogram view and reader, and parity and age at first birth when not the association of interest. Results Among 10,988 women included in these analyses, 90.1% (n = 9,895) were parous, of whom 13% (n = 1,286) had ≥ five births. The mean age at first birth was 24.3 years (Standard deviation = 5.1). Increasing parity (per birth) was inversely associated with √PMD (β: − 0.05, 95% confidence interval (CI): − 0.07, − 0.03) and √DA (β: − 0.08, 95% CI: − 0.12, − 0.05) with this trend evident until at least nine births. Women who were older at first birth (per five-year increase) had higher √PMD (β:0.06, 95% CI:0.03, 0.10) and √DA (β:0.06, 95% CI:0.02, 0.10), and lower √NDA (β: − 0.06, 95% CI: − 0.11, − 0.01). In stratified analyses, this association was only evident in women who were post-menopausal at MD assessment. Among parous women, no associations were found between ever/never breastfed or lifetime breastfeeding duration (per six-month increase) and √MD. Conclusions Associations with higher parity and older age at first birth with √MD were consistent with the direction of their respective associations with breast cancer risk. Further research is needed to understand reproductive factor-related differences in the composition of breast tissue and their associations with breast cancer risk.

Abstract Background Higher mammographic density (MD), a radiological measure of the proportion of fibroglandular tissue in the breast, and lower terminal duct lobular unit (TDLU) involution, a histological measure of the amount of epithelial tissue in the breast, are independent breast cancer risk factors. Previous studies among predominantly white women have associated reduced TDLU involution with higher MD. Methods In this cohort of 611 invasive breast cancer patients (ages 23–91 years [58.4% ≥ 50 years]) from China, where breast cancer incidence rates are lower and the prevalence of dense breasts is higher compared with Western countries, we examined the associations between TDLU involution assessed in tumor-adjacent normal breast tissue and quantitative MD assessed in the contralateral breast obtained from the VolparaDensity software. Associations were estimated using generalized linear models with MD measures as the outcome variables (log-transformed), TDLU measures as explanatory variables (categorized into quartiles or tertiles), and adjusted for age, body mass index, parity, age at menarche and breast cancer subtype. Results We found that, among all women, percent dense volume (PDV) was positively associated with TDLU count (highest tertile vs. zero: Expbeta = 1.28, 95% confidence interval [CI] 1.08–1.51, ptrend =

Purpose: The receptor activator of nuclear factor kappa B (RANK) and its ligand (RANKL) have been shown to promote proliferation of the breast and breast carcinogenesis. The objective of this analysis was to investigate whether tumor-specific RANK and RANKL expression in patients with primary breast cancer is associated with high percentage mammographic density (PMD), which is a known breast cancer risk factor. Methods: Immunohistochemical staining of RANK and RANKL was performed in tissue microarrays (TMAs) from primary breast cancer samples of the Bavarian Breast Cancer Cases and Controls (BBCC) study. For RANK and RANKL expression, histochemical scores (H scores) with a cut-off value of > 0 vs 0 were established. PMD was measured in the contralateral, non-diseased breast. Linear regression models with PMD as outcome were calculated using common predictors of PMD (age at breast cancer diagnosis, body mass index (BMI) and parity) and RANK and RANKL H scores. Additionally, Spearman rank correlations (ρ) between PMD and RANK and RANKL H score were performed. Results: In the final cohort of 412 patients, breast cancer-specific RANK and RANKL expression was not associated with PMD (P = 0.68). There was no correlation between PMD and RANK H score (Spearman's ρ = 0.01, P = 0.87) or RANKL H score (Spearman's ρ = 0.04, P = 0.41). RANK expression was highest in triple-negative tumors, followed by HER2-positive, luminal B-like and luminal A-like tumors, while no subtype-specific expression of RANKL was found. Conclusion: Results do not provide evidence for an association of RANK and RANKL expression in primary breast cancer with PMD. [ABSTRACT FROM AUTHOR]

Background: This in silico study investigated the association between the local biosynthesis of cholesterol and mammographic density, the major risk of developing breast cancer, as a function of the three cellular components of breast tissue (epithelium, fatty, and non-fatty stroma). Methods: The study compared the expression of 7 genes (HMGCR, FDPS, FDFT1, GGPS1, SQLE, LSS, and SREBF2) involved in the de novo cholesterol biosynthesis, first, according to the radiological density (dense vs. non-dense breast) and, then, according to the cellular components of breast tissue, regardless of the radiological classification. Results: HMGCR, SQLE, and SBREF2 were significantly more frequently expressed in radiologically dense than in non-dense breasts (-1.70 vs. -1.41, P=0.0028; -1.20 vs. -1.11, P=0.0501; -3.63 vs. -3.31 P=0.0003; -0.92 vs. -0.76, P=0.0271, respectively). When the samples were reclassified based on their cellular components as highly fatty and highly non-fatty, HMGCR, SQLE, and SBREF2 were significantly more frequently expressed in highly non-fatty samples (-1.48 vs. -1.94, P<0.0001; -3.39 vs. -4.18, P<0.0001; -0.77 vs. -0.94, P=0.0103, respectively), whereas LSS was overexpressed in high fatty ones (0.28 vs. -0.60, P<0.0001). Besides, while in the highly non-fatty subgroup, SREBF2 was positively associated with both HMGCR (r=0.53, P<0.0001) and SQLE (r=0.73, P<0.0001), in the highly fatty subgroup, these positive correlations disappeared (SREBF2*HMGCR: r=-0.19, P=0.3026) or substantially decreased (SREBF2*SQLE: r=0.41, P=0.0173). Conclusion: Findings provide a compelling biological explanation for the clinical evidence that women with radiologically dense breasts are at a higher risk of developing cancer compared to those with non-dense breasts because of the prevalence of non-fatty tissue, where the altered expression of genes leading to an increased cholesterol production can contribute to the transformation of epithelial cells, and support the use of mammographic density as a reliable surrogate marker to identify women who may benefit from a preventive treatment aimed at reducing cholesterol production. [ABSTRACT FROM AUTHOR]

Background: Higher mammographic density (MD), a radiological measure of the proportion of fibroglandular tissue in the breast, and lower terminal duct lobular unit (TDLU) involution, a histological measure of the amount of epithelial tissue in the breast, are independent breast cancer risk factors. Previous studies among predominantly white women have associated reduced TDLU involution with higher MD. Methods: In this cohort of 611 invasive breast cancer patients (ages 23–91 years [58.4% ≥ 50 years]) from China, where breast cancer incidence rates are lower and the prevalence of dense breasts is higher compared with Western countries, we examined the associations between TDLU involution assessed in tumor-adjacent normal breast tissue and quantitative MD assessed in the contralateral breast obtained from the VolparaDensity software. Associations were estimated using generalized linear models with MD measures as the outcome variables (log-transformed), TDLU measures as explanatory variables (categorized into quartiles or tertiles), and adjusted for age, body mass index, parity, age at menarche and breast cancer subtype. Results: We found that, among all women, percent dense volume (PDV) was positively associated with TDLU count (highest tertile vs. zero: Expbeta = 1.28, 95% confidence interval [CI] 1.08–1.51, ptrend = <.0001), TDLU span (highest vs. lowest tertile: Expbeta = 1.23, 95% CI 1.11–1.37, ptrend = <.0001) and acini count/TDLU (highest vs. lowest tertile: Expbeta = 1.22, 95% CI 1.09–1.37, ptrend = 0.0005), while non-dense volume (NDV) was inversely associated with these measures. Similar trend was observed for absolute dense volume (ADV) after the adjustment of total breast volume, although the associations for ADV were in general weaker than those for PDV. The MD-TDLU associations were generally more pronounced among breast cancer patients ≥ 50 years and those with luminal A tumors compared with patients < 50 years and with luminal B tumors. Conclusions: Our findings based on quantitative MD and TDLU involution measures among Chinese breast cancer patients are largely consistent with those reported in Western populations and may provide additional insights into the complexity of the relationship, which varies by age, and possibly breast cancer subtype. [ABSTRACT FROM AUTHOR]

Purpose: Single‐sided portable NMR (pNMR) has previously been demonstrated to be suitable for quantification of mammographic density (MD) in excised breast tissue samples. Here we investigate the precision and accuracy of pNMR measurements of MD ex vivo as compared with the gold standards. Methods: Forty‐five breast‐tissue explants from 9 prophylactic mastectomy patients were measured. The relative tissue water content was taken as the MD‐equivalent quantity. In each sample, the water content was measured using some combination of three pNMR techniques (apparent T2, diffusion, and T1 measurements) and two gold‐standard techniques (computed microtomography [μCT] and hematoxylin and eosin [H&E] histology). Pairwise correlation plots and Bland–Altman analysis were used to quantify the degree of agreement between pNMR techniques and the gold standards. Results: Relative water content measured from both apparent T2 relaxation spectra, and diffusion decays exhibited strong correlation with the H&E and μCT results. Bland–Altman analysis yielded average bias values of −0.4, −2.6, 2.6, and 2.8 water percentage points (pp) and 95% confidence intervals of 13.1, 7.5, 11.2, and 11.8 pp for the H&E – T2, μCT – T2, H&E – diffusion, and μCT – diffusion comparison pairs, respectively. T1‐based measurements were found to be less reliable, with the Bland–Altman confidence intervals of 27.7 and 33.0 pp when compared with H&E and μCT, respectively. Conclusion: Apparent T2‐based and diffusion‐based pNMR measurements enable quantification of MD in breast‐tissue explants with the precision of approximately 10 pp and accuracy of approximately 3 pp or better, making pNMR a promising measurement modality for radiation‐free quantification of MD. [ABSTRACT FROM AUTHOR]

Background High mammographic density increases breast cancer risk and reduces mammographic sensitivity. We reviewed evidence on accuracy of supplemental MRI for women with dense breasts at average or increased risk. Methods PubMed and Embase were searched 1995-2022. Articles were included if women received breast MRI following 2D or tomosynthesis mammography. Risk of bias was assessed using QUADAS-2. Analysis used independent studies from the articles. Fixed-effect meta-analytic summaries were estimated for predefined groups (PROSPERO: 230277). Results Eighteen primary research articles (24 studies) were identified in women aged 19-87 years. Breast density was heterogeneously or extremely dense (BI-RADS C/D) in 15/18 articles and extremely dense (BI-RADS D) in 3/18 articles. Twelve of 18 articles reported on increased-risk populations. Following 21 440 negative mammographic examinations, 288/320 cancers were detected by MRI. Substantial variation was observed between studies in MRI cancer detection rate, partly associated with prevalent vs incident MRI exams (prevalent: 16.6/1000 exams, 12 studies; incident: 6.8/1000 exams, 7 studies). MRI had high sensitivity for mammographically occult cancer (20 studies with at least 1-year follow-up). In 5/18 articles with sufficient data to estimate relative MRI detection rate, approximately 2 in 3 cancers were detected by MRI (66.3%, 95% CI, 56.3%-75.5%) but not mammography. Positive predictive value was higher for more recent studies. Risk of bias was low in most studies. Conclusion Supplemental breast MRI following negative mammography in women with dense breasts has breast cancer detection rates of ~16.6/1000 at prevalent and ~6.8/1000 at incident MRI exams, considering both high and average risk settings. [ABSTRACT FROM AUTHOR]

Abstract Background Mammographic density (MD) has been shown to be a strong and independent risk factor for breast cancer in women of European and Asian descent. However, the majority of Asian studies to date have used BI-RADS as the scoring method and none have evaluated area and volumetric densities in the same cohort of women. This study aims to compare the association of MD measured by two automated methods with the risk of breast cancer in Asian women, and to investigate if the association is different for premenopausal and postmenopausal women. Methods In this case–control study of 531 cases and 2297 controls, we evaluated the association of area-based MD measures and volumetric-based MD measures with breast cancer risk in Asian women using conditional logistic regression analysis, adjusting for relevant confounders. The corresponding association by menopausal status were assessed using unconditional logistic regression. Results We found that both area and volume-based MD measures were associated with breast cancer risk. Strongest associations were observed for percent densities (OR (95% CI) was 2.06 (1.42–2.99) for percent dense area and 2.21 (1.44–3.39) for percent dense volume, comparing women in highest density quartile with those in the lowest quartile). The corresponding associations were significant in postmenopausal but not premenopausal women (premenopausal versus postmenopausal were 1.59 (0.95–2.67) and 1.89 (1.22–2.96) for percent dense area and 1.24 (0.70–2.22) and 1.96 (1.19–3.27) for percent dense volume). However, the odds ratios were not statistically different by menopausal status [p difference = 0.782 for percent dense area and 0.486 for percent dense volume]. Conclusions This study confirms the associations of mammographic density measured by both area and volumetric methods and breast cancer risk in Asian women. Stronger associations were observed for percent dense area and percent dense volume, and strongest effects were seen in postmenopausal individuals.

Background: Mammographic density (MD) has been shown to be a strong and independent risk factor for breast cancer in women of European and Asian descent. However, the majority of Asian studies to date have used BI-RADS as the scoring method and none have evaluated area and volumetric densities in the same cohort of women. This study aims to compare the association of MD measured by two automated methods with the risk of breast cancer in Asian women, and to investigate if the association is different for premenopausal and postmenopausal women. Methods: In this case–control study of 531 cases and 2297 controls, we evaluated the association of area-based MD measures and volumetric-based MD measures with breast cancer risk in Asian women using conditional logistic regression analysis, adjusting for relevant confounders. The corresponding association by menopausal status were assessed using unconditional logistic regression. Results: We found that both area and volume-based MD measures were associated with breast cancer risk. Strongest associations were observed for percent densities (OR (95% CI) was 2.06 (1.42–2.99) for percent dense area and 2.21 (1.44–3.39) for percent dense volume, comparing women in highest density quartile with those in the lowest quartile). The corresponding associations were significant in postmenopausal but not premenopausal women (premenopausal versus postmenopausal were 1.59 (0.95–2.67) and 1.89 (1.22–2.96) for percent dense area and 1.24 (0.70–2.22) and 1.96 (1.19–3.27) for percent dense volume). However, the odds ratios were not statistically different by menopausal status [p difference = 0.782 for percent dense area and 0.486 for percent dense volume]. Conclusions: This study confirms the associations of mammographic density measured by both area and volumetric methods and breast cancer risk in Asian women. Stronger associations were observed for percent dense area and percent dense volume, and strongest effects were seen in postmenopausal individuals. [ABSTRACT FROM AUTHOR]

Simple Summary: Breast density is an independent risk factor for breast cancer and can impede detection of cancer by mammography. There is growing awareness of breast density in Australia and globally, but it is unclear whether this awareness is increasing knowledge of what breast density is and what it means to have dense breasts. This study was conducted to investigate South Australian women's knowledge of the common facts and misconceptions about breast density. This study reports that women who had previously heard the term breast density had increased knowledge compared to those who had not, suggesting that current efforts to raise awareness are leading to better knowledge. Despite this, the study shows that there are widespread misconceptions that must be actively dispelled, including the misunderstanding that breast density can be determined by touch. Background: There is growing awareness of breast density in women attending breast cancer screening; however, it is unclear whether this awareness is associated with increased knowledge. This study aims to evaluate breast density knowledge among Australian women attending breast cancer screening. Method: This cross-sectional study was conducted on women undergoing breast cancer screening at The Queen Elizabeth Hospital Breast/Endocrine outpatient department. Participants were provided with a questionnaire to assess knowledge, awareness, and desire to know their own breast density. Result: Of the 350 women who participated, 61% were familiar with 'breast density' and 57% had 'some knowledge'. Prior breast density notification (OR = 4.99, 95% CI = 2.76, 9.03; p = 0.004), awareness (OR = 4.05, 95% CI = 2.57, 6.39; p = 0.004), younger age (OR = 0.97, 95% CI = 0.96, 0.99; p = 0.02), and English as the language spoken at home (OR = 3.29, 95% CI = 1.23, 8.77; p = 0.02) were independent predictors of 'some knowledge' of breast density. A significant proportion of participants (82%) expressed desire to ascertain their individual breast density. Conclusions: While knowledge of breast density in this Australian cohort is generally quite low, we have identified factors associated with increased knowledge. Further research is required to determine optimal interventions to increase breast density knowledge. [ABSTRACT FROM AUTHOR]

Abstract Purpose Breast density has consistently been shown to be an independent risk factor for breast cancer in Western populations; however, few studies have evaluated this topic in Chinese women and there is not yet a unified view. This study investigated the association between mammographic density (MD) and breast cancer risk in Chinese women. Methods The PubMed, Cochrane Library, Embase, and Wanfang databases were systematically searched in June 2023 to include all studies on the association between MD and breast cancer risk in Chinese women. A total of 13,977 breast cancer cases from 14 studies were chosen, including 10 case-control/cross-sectional studies, and 4 case-only studies. For case-control/cross-sectional studies, the odds ratios (ORs) of MD were combined using random effects models, and for case-only studies, relative odds ratios (RORs) were combinations of premenopausal versus postmenopausal breast cancer cases. Results Women with BI-RADS density category II-IV in case-control/cross-sectional studies had a 0.93-fold (95% confidence interval [CI] 0.55, 1.57), 1.08-fold (95% CI 0.40, 2.94), and 1.24-fold (95% CI 0.42, 3.69) higher risk compared to women with the lowest density category. Combined RORs for premenopausal versus postmenopausal women in case-only studies were 3.84 (95% CI 2.92, 5.05), 22.65 (95% CI 7.21, 71.13), and 42.06 (95% CI 4.22, 419.52), respectively, for BI-RADS density category II-IV versus I. Conclusions For Chinese women, breast cancer risk is weakly associated with MD; however, breast cancer risk is more strongly correlated with mammographic density in premenopausal women than postmenopausal women. Further research on the factors influencing MD in premenopausal women may provide meaningful insights into breast cancer prevention in China.

Background: Mammographic density and family history of breast cancer (FHBC) are well-established independent factors affecting breast cancer risk; however, the association between these two risk factors in premenopausal-screened women remains unclear. The aim of this study is to investigate the relationship between mammographic density and FHBC among Saudi premenopausal-screened women. Methods: A total of 446 eligible participants were included in the study. Mammographic density was assessed qualitatively using the Breast Imaging Reporting and Data System (BIRADS 4th edition). Logistic regression models were built to investigate the relationship between mammographic density and FHBC. Results: Women with a family history of breast cancer demonstrated an 87% greater chance of having dense tissue than women without a family history of breast cancer (95% CI: 1.14–3.08; p = 0.01). Having a positive family history for breast cancer in mothers was significantly associated with dense tissue (adjusted odds ratio (OR): 5.6; 95% CI: 1.3–24.1; p = 0.02). Conclusion: Dense breast tissue in Saudi premenopausal women undergoing screening may be linked to FHBC. If this conclusion is replicated in larger studies, then breast cancer risk prediction models must carefully consider these breast cancer risk factors.

Background: Breast density impacts upon breast cancer risk and recurrence, but its influence on breast cancer-specific survival is unclear. This study examines the influence of mammographic breast density (MBD) at diagnosis on breast cancer-specific survival. Methods: The data of 224 patients diagnosed with breast cancer were analyzed. Two area-based MBD measurement tools—AutoDensity and LIBRA—were used to measure MBD via a mammogram of the contralateral breast acquired at the time of diagnosis. These patients were split into two groups based on their percent breast density (PBD): high (PBD ≥ 20%) versus low (PBD < 20%). Breast cancer-specific survival in each of these PBD groups was assessed at a median follow-up of 34 months using Kaplan–Meier analysis and the Cox proportional hazards model. Results: The proportion of women with low PBD who died from breast cancer was significantly higher than that seen with high PBD (p = 0.01). The 5-year breast cancer-specific survival was poorer among women with low PBD than those with high PBD (0.348; 95% CI: 0.13–0.94) vs. 0.87; 95% CI: (0.8–0.96); p < 0.001)]. Women with higher breast density demonstrated longer survival regardless of the method of PBD measurement: LIBRA [log-rank test (Mantel–Cox): 9.4; p = 0.002)]; AutoDensity [log-rank test (Mantel–Cox) 7.6; p = 0.006]. Multivariate analysis also demonstrated that there was a higher risk of breast cancer-related deaths in women with low PBD (adjusted HR: 5.167; 95% CI: 1.974–13.521; p = 0.001). Conclusion: Women with

Mammographic density (MD) assessment is subject to inter- and intra-observer variability. An automated method, such as Quantra software, could be a useful tool for an objective and reproducible MD assessment. Our purpose was to evaluate the performance of Quantra software in assessing MD, according to BI-RADS® Atlas Fifth Edition recommendations, verifying the degree of agreement with the gold standard, given by the consensus of two breast radiologists. A total of 5009 screening examinations were evaluated by two radiologists and analysed by Quantra software to assess MD. The agreement between the three assigned values was expressed as intraclass correlation coefficients (ICCs). The agreement between the software and the two readers (R1 and R2) was moderate with ICC values of 0.725 and 0.713, respectively. A better agreement was demonstrated between the software’s assessment and the average score of the values assigned by the two radiologists, with an index of 0.793, which reflects a good correlation. Quantra software appears a promising tool in supporting radiologists in the MD assessment and could be part of a personalised screening protocol soon. However, some fine-tuning is needed to improve its accuracy, reduce its tendency to overestimate, and ensure it excludes high-density structures from its assessment.

UNLABELLED: Inverse associations between natural vegetation exposure (i.e., greenness) and breast cancer risk have been reported; however, it remains unknown whether greenness affects breast tissue development or operates through other mechanisms (e.g., body mass index [BMI] or physical activity). We examined the association between greenness and mammographic density-a strong breast cancer risk factor-to determine whether greenness influences breast tissue composition independent of lifestyle factors. METHODS: Women (n = 2,318) without a history of breast cancer underwent mammographic screening at Brigham and Womens Hospital in Boston, Massachusetts, from 2006 to 2014. Normalized Difference Vegetation Index (NDVI) satellite data at 1-km2 resolution were used to estimate greenness at participants residential address 1, 3, and 5 years before mammogram. We used multivariable linear regression to estimate differences in log-transformed volumetric mammographic density measures and 95% confidence intervals (CIs) for each 0.1 unit increase in NDVI. RESULTS: Five-year annual average NDVI was not associated with percent mammographic density in premenopausal (β = -0.01; 95% CI = -0.03, 0.02; P = 0.58) and postmenopausal women (β = -0.02; 95% CI = -0.04, 0.01; P = 0.18). Results were similar for 1-year and 3-year NDVI measures and in models including potential mediators of BMI and physical activity. There were also no associations between greenness and dense volume and nondense volume. CONCLUSIONS: Greenness exposures were not associated with mammographic density. IMPACT: Prior observations of a protective association between greenness and breast cancer may not be driven by differences in breast tissue composition, as measured by mammographic density, but rather other mechanisms.

Purpose: Breast density has consistently been shown to be an independent risk factor for breast cancer in Western populations; however, few studies have evaluated this topic in Chinese women and there is not yet a unified view. This study investigated the association between mammographic density (MD) and breast cancer risk in Chinese women. Methods: The PubMed, Cochrane Library, Embase, and Wanfang databases were systematically searched in June 2023 to include all studies on the association between MD and breast cancer risk in Chinese women. A total of 13,977 breast cancer cases from 14 studies were chosen, including 10 case-control/cross-sectional studies, and 4 case-only studies. For case-control/cross-sectional studies, the odds ratios (ORs) of MD were combined using random effects models, and for case-only studies, relative odds ratios (RORs) were combinations of premenopausal versus postmenopausal breast cancer cases. Results: Women with BI-RADS density category II-IV in case-control/cross-sectional studies had a 0.93-fold (95% confidence interval [CI] 0.55, 1.57), 1.08-fold (95% CI 0.40, 2.94), and 1.24-fold (95% CI 0.42, 3.69) higher risk compared to women with the lowest density category. Combined RORs for premenopausal versus postmenopausal women in case-only studies were 3.84 (95% CI 2.92, 5.05), 22.65 (95% CI 7.21, 71.13), and 42.06 (95% CI 4.22, 419.52), respectively, for BI-RADS density category II-IV versus I. Conclusions: For Chinese women, breast cancer risk is weakly associated with MD; however, breast cancer risk is more strongly correlated with mammographic density in premenopausal women than postmenopausal women. Further research on the factors influencing MD in premenopausal women may provide meaningful insights into breast cancer prevention in China. [ABSTRACT FROM AUTHOR]

Background: Mammographic density and family history of breast cancer (FHBC) are well-established independent factors affecting breast cancer risk; however, the association between these two risk factors in premenopausal-screened women remains unclear. The aim of this study is to investigate the relationship between mammographic density and FHBC among Saudi premenopausal-screened women. Methods: A total of 446 eligible participants were included in the study. Mammographic density was assessed qualitatively using the Breast Imaging Reporting and Data System (BIRADS 4th edition). Logistic regression models were built to investigate the relationship between mammographic density and FHBC. Results: Women with a family history of breast cancer demonstrated an 87% greater chance of having dense tissue than women without a family history of breast cancer (95% CI: 1.14–3.08; p = 0.01). Having a positive family history for breast cancer in mothers was significantly associated with dense tissue (adjusted odds ratio (OR): 5.6; 95% CI: 1.3–24.1; p = 0.02). Conclusion: Dense breast tissue in Saudi premenopausal women undergoing screening may be linked to FHBC. If this conclusion is replicated in larger studies, then breast cancer risk prediction models must carefully consider these breast cancer risk factors. [ABSTRACT FROM AUTHOR]

Response rates vary among breast cancer patients treated with neoadjuvant systemic therapy (NAST). Thus, there is a need for reliable treatment predictors. Evidence suggests tumor-infiltrating lymphocytes (TILs) predict NAST response. Still, TILs are seldom used clinically as a treatment determinant. Mammographic density (MD) is another potential marker for NAST benefit and its relationship with TILs is unknown. Our aims were to investigate TILs and MD as predictors of NAST response and to study the unexplored relationship between TILs and MD. We studied 315 invasive breast carcinomas treated with NAST between 2013 and 2020. Clinicopathological data were retrieved from medical records. The endpoint was defined as pathological complete response (pCR) in the breast. TILs were evaluated in pre-treatment core biopsies and categorized as high (≥10%) or low (<10%). MD was scored (a–d) according to the breast imaging reporting and data system (BI-RADS) fifth edition. Binary logistic regression and Spearman's test of correlation were performed using SPSS. Out of 315 carcinomas, 136 achieved pCR. 94 carcinomas had high TILs and 215 had low TILs. Six carcinomas had no available TIL data. The number of carcinomas in each BI-RADS category were 37, 122, 112, and 44 for a, b, c, and d, respectively. High TILs were independently associated with pCR (OR: 2.95; 95% CI: 1.59–5.46) compared to low TILs. In the univariable analysis, MD (BI-RADS d vs. a) showed a tendency of higher likelihood for pCR (OR: 2.43; 95% CI: 0.99–5.98). However, the association was non-significant, which is consistent with the result of the multivariable analysis (OR: 2.51; 95% CI: 0.78–8.04). We found no correlation between TILs and MD (0.02; p =.80). TILs significantly predicted NAST response. We could not define MD as a significant predictor of NAST response. These findings should be further replicated. [ABSTRACT FROM AUTHOR]

BackgroundAltered DNA methylation may be an intermediate phenotype between breast cancer risk factors and disease. Mammographic density is a strong risk factor for breast cancer. However, no studies to date have identified an epigenetic signature of mammographic density. We performed an epigenome-wide association study of mammographic density.MethodsWhite blood cell DNA methylation was measured for 385 postmenopausal women using the Illumina Infinium MethylationEPIC BeadChip array. Differential methylation was assessed using genome-wide, probe-level, and regional analyses. We implemented a resampling-based approach to improve the stability of our findings.ResultsOn average, women with elevated mammographic density exhibited DNA hypermethylation within CpG islands and gene promoters compared to women with lower mammographic density. We identified 250 CpG sites for which DNA methylation was significantly associated with mammographic density. The top sites were located within genes associated with cancer, including HDLBP, TGFB2, CCT4, and PAX8, and were more likely to be located in regulatory regions of the genome. We also identified differential DNA methylation in 37 regions, including within the promoters of PAX8 and PF4, a gene involved in the regulation of angiogenesis. Overall, our results paint a picture of epigenetic dysregulation associated with mammographic density.ConclusionMammographic density is associated with differential DNA methylation throughout the genome, including within genes associated with cancer. Our results suggest the potential involvement of several genes in the biological mechanisms behind differences in breast density between women. Further studies are warranted to explore these potential mechanisms and potential links to breast cancer risk.

Background: Despite suggesting many genetic risk markers as the outcome of Genome-wide association studies (GWAS) for breast cancer, replicating the results in different populations has remained the main issue. In this regard, this study assessed the association of two variations in Zinc Finger 365 (ZNF365) in an Iranian population. Methods: In a case-control study conducted at Mashhad University of Medical Sciences, Mashhad, Iran, between 2017 and 2020, ZNF365-rs10822013 and rs10995190 were genotyped using Allele-Specific PCR (AS-PCR). Breast density was assessed using mammography images. PHASE software module version 2 and SPSS version 16.0 were used for haplotype and statistical analyses. Quantitative and qualitative variables were compared between groups using independent t tests and Chi square tests, respectively. Binary logistic regression analysis was performed to calculate odds ratios. Multivariate analysis was then undertaken for the baseline variables, with a P

Abstract Background Women with dense breasts have an increased risk of breast cancer. However, breast density is measured with variability, which may reduce the reliability and accuracy of its association with breast cancer risk. This is particularly relevant when visually assessing breast density due to variation in inter- and intra-reader assessments. To address this issue, we developed a longitudinal breast density measure which uses an individual woman’s entire history of mammographic density, and we evaluated its association with breast cancer risk as well as its predictive ability. Methods In total, 132,439 women, aged 40–73 yr, who were enrolled in Kaiser Permanente Washington and had multiple screening mammograms taken between 1996 and 2013 were followed up for invasive breast cancer through 2014. Breast Imaging Reporting and Data System (BI-RADS) density was assessed at each screen. Continuous and derived categorical longitudinal density measures were developed using a linear mixed model that allowed for longitudinal density to be updated at each screen. Predictive ability was assessed using (1) age and body mass index-adjusted hazard ratios (HR) for breast density (time-varying covariate), (2) likelihood-ratio statistics (ΔLR-χ 2) and (3) concordance indices. Results In total, 2704 invasive breast cancers were diagnosed during follow-up (median = 5.2 yr; median mammograms per woman = 3). When compared with an age- and body mass index-only model, the gain in statistical information provided by the continuous longitudinal density measure was 23% greater than that provided by BI-RADS density (follow-up after baseline mammogram: ΔLR-χ 2 = 379.6 (degrees of freedom (df) = 2) vs. 307.7 (df = 3)), which increased to 35% (ΔLR-χ 2 = 251.2 vs. 186.7) for follow-up after three mammograms (n = 76,313, 2169 cancers). There was a sixfold difference in observed risk between densest and fattiest eight-category longitudinal density (HR = 6.3, 95% CI 4.7–8.7), versus a fourfold difference with BI-RADS density (HR = 4.3, 95% CI 3.4–5.5). Discriminatory accuracy was marginally greater for longitudinal versus BI-RADS density (c-index = 0.64 vs. 0.63, mean difference = 0.008, 95% CI 0.003–0.012). Conclusions Estimating mammographic density using a woman’s history of breast density is likely to be more reliable than using the most recent observation only, which may lead to more reliable and accurate estimates of individual breast cancer risk. Longitudinal breast density has the potential to improve personal breast cancer risk estimation in women attending mammography screening.

Abstract Background Mammogram risk scores based on texture and density defined by different brightness thresholds are associated with breast cancer risk differently and could reveal distinct information about breast cancer risk. We aimed to investigate causal relationships between these intercorrelated mammogram risk scores to determine their relevance to breast cancer aetiology. Methods We used digitised mammograms for 371 monozygotic twin pairs, aged 40–70 years without a prior diagnosis of breast cancer at the time of mammography, from the Australian Mammographic Density Twins and Sisters Study. We generated normalised, age-adjusted, and standardised risk scores based on textures using the Cirrus algorithm and on three spatially independent dense areas defined by increasing brightness threshold: light areas, bright areas, and brightest areas. Causal inference was made using the Inference about Causation from Examination of FAmilial CONfounding (ICE FALCON) method. Results The mammogram risk scores were correlated within twin pairs and with each other (r = 0.22–0.81; all P

Abstract Background Mammographic density (MD) is a strong risk factor for breast cancer. We aimed to evaluate the association between MD and breast cancer related risk factors among average-risk women in rural China. Methods This is a population-based screening study. 12518 women aged 45–64 years with complete MD data from three maternal and childcare hospitals in China were included in the final analysis. ORs and 95%CIs were estimated using generalized logit model by comparing each higher MD (BI-RADS b, c, d) to the lowest group (BI-RADS a). The cumulative logistic regression model was used to estimate the OR trend (95%CI) and P trend by treating MD as an ordinal variable. Results Older age (ORtrend = 0.81, 95%CI: 0.79–0.81, per 2-year increase), higher BMI (ORtrend = 0.73, 95%CI: 0.71–0.75, per 2 kg/m2), more births (ORtrend = 0.47, 95%CI: 0.41–0.54, 3 + vs. 0–1), postmenopausal status (ORtrend = 0.42, 95%CI: 0.38–0.46) were associated with lower MD. For parous women, longer duration of breastfeeding was found to be associated with higher MD when adjusting for study site, age, BMI, and age of first full-term birth (ORtrend = 1.53, 95%CI: 1.27–1.85, 25 + months vs. no breastfeeding; ORtrend = 1.45, 95%CI: 1.20–1.75, 19–24 months vs. no breastfeeding), however, the association became non-significant when adjusting all covariates. Associations between examined risk factors and MD were similar in premenopausal and postmenopausal women except for level of education and oral hormone drug usage. Higher education was only found to be associated with an increased proportion of dense breasts in postmenopausal women (ORtrend = 1.08, 95%CI: 1.02–1.15). Premenopausal women who ever used oral hormone drug were less likely to have dense breasts, though the difference was marginally significant (OR = 0.54, P = 0.045). In postmenopausal women, we also found the proportion of dense breasts increased with age at menopause (ORtrend = 1.31, 95%CI: 1.21–1.43). Conclusions In Chinese women with average risk for breast cancer, we found MD was associated with age, BMI, menopausal status, lactation, and age at menopausal. This finding may help to understand the etiology of breast cancer and have implications for breast cancer prevention in China.

Concurrent cohorts of 644,932 women aged 50–74 screened annually due to family history, dense breasts or biennially in the Ontario Breast Screening Program (OBSP) from 2011–2014 were linked to provincial administrative datasets to determine health system resource utilization and costs. Age-adjusted mean and median total healthcare costs (2018 CAD) and incremental cost differences were calculated by screening outcome and compared by recommendation using regression models. Healthcare costs were compared overall and 1 year after a false positive (n = 46,081) screening mammogram and 2 years after a breast cancer diagnosis (n = 6011). Mean overall healthcare costs by age were highest for those 60–74, particularly with annual screening for family/personal history (CAD 5425; 95% CI: 5308 to 5557) compared to biennial. Although the mean incremental cost difference was higher (23.4%) by CAD 10,235 (95% CI: 6141 to 14,329) per breast cancer for women screened annually for density ≥ 75% compared to biennially, the cost difference was 12.0% lower (−CAD 461; 95% CI: −777 to −114) per false positive result. In contrast, for women screened annually for family/personal history, the mean cost difference per false positive was 19.7% higher than for biennially (CAD 758; 95% CI: 404 to 1118); however, the cost difference per breast cancer was only slightly higher (2.5%) by CAD 1093 (95% CI: −1337 to CAD 3760). Understanding that associated costs of annual compared to biennial screening may balance out by age and outcome can assist decision-making regarding the use of limited healthcare resources.

Regions of high mammographic density (MD) in the breast are characterised by a proteoglycan (PG)-rich fibrous stroma, where PGs mediate aligned collagen fibrils to control tissue stiffness and hence the response to mechanical forces. Literature is accumulating to support the notion that mechanical stiffness may drive PG synthesis in the breast contributing to MD. We review emerging patterns in MD and other biological settings, of a positive feedback cycle of force promoting PG synthesis, such as in articular cartilage, due to increased pressure on weight bearing joints. Furthermore, we present evidence to suggest a pro-tumorigenic effect of increased mechanical force on epithelial cells in contexts where PG-mediated, aligned collagen fibrous tissue abounds, with implications for breast cancer development attributable to high MD. Finally, we summarise means through which this positive feedback mechanism of PG synthesis may be intercepted to reduce mechanical force within tissues and thus reduce disease burden.

Abstract Background Breast density is strongly associated with breast cancer risk. Fully automated quantitative density assessment methods have recently been developed that could facilitate large-scale studies, although data on associations with long-term breast cancer risk are limited. We examined LIBRA assessments and breast cancer risk and compared results to prior assessments using Cumulus, an established computer-assisted method requiring manual thresholding. Methods We conducted a cohort study among 21,150 non-Hispanic white female participants of the Research Program in Genes, Environment and Health of Kaiser Permanente Northern California who were 40–74 years at enrollment, followed for up to 10 years, and had archived processed screening mammograms acquired on Hologic or General Electric full-field digital mammography (FFDM) machines and prior Cumulus density assessments available for analysis. Dense area (DA), non-dense area (NDA), and percent density (PD) were assessed using LIBRA software. Cox regression was used to estimate hazard ratios (HRs) for breast cancer associated with DA, NDA and PD modeled continuously in standard deviation (SD) increments, adjusting for age, mammogram year, body mass index, parity, first-degree family history of breast cancer, and menopausal hormone use. We also examined differences by machine type and breast view. Results The adjusted HRs for breast cancer associated with each SD increment of DA, NDA and PD were 1.36 (95% confidence interval, 1.18–1.57), 0.85 (0.77–0.93) and 1.44 (1.26–1.66) for LIBRA and 1.44 (1.33–1.55), 0.81 (0.74–0.89) and 1.54 (1.34–1.77) for Cumulus, respectively. LIBRA results were generally similar by machine type and breast view, although associations were strongest for Hologic machines and mediolateral oblique views. Results were also similar during the first 2 years, 2–5 years and 5–10 years after the baseline mammogram. Conclusion Associations with breast cancer risk were generally similar for LIBRA and Cumulus density measures and were sustained for up to 10 years. These findings support the suitability of fully automated LIBRA assessments on processed FFDM images for large-scale research on breast density and cancer risk.

Purpose: The European Society on Breast Imaging has recommended supplemental magnetic resonance imaging (MRI) every two to four years for women with mammographically dense breasts. This may not be feasible in many screening programs. Also, the European Commission Initiative on Breast Cancer suggests not implementing screening with MRI. By analyzing interval cancers and time from screening to diagnosis by density, we present alternative screening strategies for women with dense breasts. Methods: Our BreastScreen Norway cohort included 508 536 screening examinations, including 3125 screen-detected and 945 interval breast cancers. Time from screening to interval cancer was stratified by density measured by an automated software and classified into Volpara Density Grades (VDGs) 1–4. Examinations with volumetric density ≤3.4% were categorized as VDG1, 3.5%–7.4% as VDG2, 7.5%–15.4% as VDG3, and ≥15.5% as VDG4. Interval cancer rates were also determined by continuous density measures. Results: Median time from screening to interval cancer was 496 (IQR: 391–587) days for VDG1, 500 (IQR: 350–616) for VDG2, 482 (IQR: 309–595) for VDG3 and 427 (IQR: 266–577) for VDG4. A total of 35.9% of the interval cancers among VDG4 were detected within the first year of the biennial screening interval. For VDG2, 26.3% were detected within the first year. The highest annual interval cancer rate (2.7 per 1000 examinations) was observed for VDG4 in the second year of the biennial interval. Conclusions: Annual screening of women with extremely dense breasts may reduce the interval cancer rate and increase program-wide sensitivity, especially in settings where supplemental MRI screening is not feasible.

Background: Women with dense breasts have an increased risk of breast cancer. However, breast density is measured with variability, which may reduce the reliability and accuracy of its association with breast cancer risk. This is particularly relevant when visually assessing breast density due to variation in inter- and intra-reader assessments. To address this issue, we developed a longitudinal breast density measure which uses an individual woman's entire history of mammographic density, and we evaluated its association with breast cancer risk as well as its predictive ability. Methods: In total, 132,439 women, aged 40–73 yr, who were enrolled in Kaiser Permanente Washington and had multiple screening mammograms taken between 1996 and 2013 were followed up for invasive breast cancer through 2014. Breast Imaging Reporting and Data System (BI-RADS) density was assessed at each screen. Continuous and derived categorical longitudinal density measures were developed using a linear mixed model that allowed for longitudinal density to be updated at each screen. Predictive ability was assessed using (1) age and body mass index-adjusted hazard ratios (HR) for breast density (time-varying covariate), (2) likelihood-ratio statistics (ΔLR-χ2) and (3) concordance indices. Results: In total, 2704 invasive breast cancers were diagnosed during follow-up (median = 5.2 yr; median mammograms per woman = 3). When compared with an age- and body mass index-only model, the gain in statistical information provided by the continuous longitudinal density measure was 23% greater than that provided by BI-RADS density (follow-up after baseline mammogram: ΔLR-χ2 = 379.6 (degrees of freedom (df) = 2) vs. 307.7 (df = 3)), which increased to 35% (ΔLR-χ2 = 251.2 vs. 186.7) for follow-up after three mammograms (n = 76,313, 2169 cancers). There was a sixfold difference in observed risk between densest and fattiest eight-category longitudinal density (HR = 6.3, 95% CI 4.7–8.7), versus a fourfold difference with BI-RADS density (HR = 4.3, 95% CI 3.4–5.5). Discriminatory accuracy was marginally greater for longitudinal versus BI-RADS density (c-index = 0.64 vs. 0.63, mean difference = 0.008, 95% CI 0.003–0.012). Conclusions: Estimating mammographic density using a woman's history of breast density is likely to be more reliable than using the most recent observation only, which may lead to more reliable and accurate estimates of individual breast cancer risk. Longitudinal breast density has the potential to improve personal breast cancer risk estimation in women attending mammography screening. [ABSTRACT FROM AUTHOR]

Background: Despite suggesting many genetic risk markers as the outcome of Genome-wide association studies (GWAS) for breast cancer, replicating the results in different populations has remained the main issue. In this regard, this study assessed the association of two variations in Zinc Finger 365 (ZNF365) in an Iranian population. Methods: In a case-control study conducted at Mashhad University of Medical Sciences, Mashhad, Iran, between 2017 and 2020, ZNF365-rs10822013 and rsl0995190 were genotyped using Allele-Specific PCR (AS-PCR). Breast density was assessed using mammography images. PHASE software module version 2 and SPSS version 16.0 were used for haplotype and statistical analyses. Quantitative and qualitative variables were compared between groups using independent t tests and Chi square tests, respectively. Binary logistic regression analysis was performed to calculate odds ratios. Multivariate analysis was then undertaken for the baseline variables, with a P<0.05 in the univariate analysis. The survival analysis was performed using the Kaplan-Meier method and the log-rank test. Results: In this survey, 732 females, including 342 breast cancer patients and 390 healthy subjects, were enrolled. rs10822013-T allele (P=0.014), rs10995190-G allele (P=0.003), and TG haplotype (P=0.002) were significantly associated with the increased risk of breast cancer. Moreover, rs10995190-GG genotype (P=0.042) and C-G haplotype (P=0.019) revealed a significant association with better overall survival. However, considered polymorphisms and their haplotypes indicated no association with breast density and clinical features of breast cancer. Conclusion: ZNF365 variants might be a potential risk marker of breast cancer in the Iranian population. The interaction between alleles in haplotypes may modulate the amount of the risk conferred by these variants. Further studies on different ethnic groups can validate these results. [ABSTRACT FROM AUTHOR]

Background: Mammogram risk scores based on texture and density defined by different brightness thresholds are associated with breast cancer risk differently and could reveal distinct information about breast cancer risk. We aimed to investigate causal relationships between these intercorrelated mammogram risk scores to determine their relevance to breast cancer aetiology. Methods: We used digitised mammograms for 371 monozygotic twin pairs, aged 40–70 years without a prior diagnosis of breast cancer at the time of mammography, from the Australian Mammographic Density Twins and Sisters Study. We generated normalised, age-adjusted, and standardised risk scores based on textures using the Cirrus algorithm and on three spatially independent dense areas defined by increasing brightness threshold: light areas, bright areas, and brightest areas. Causal inference was made using the Inference about Causation from Examination of FAmilial CONfounding (ICE FALCON) method. Results: The mammogram risk scores were correlated within twin pairs and with each other (r = 0.22–0.81; all P < 0.005). We estimated that 28–92% of the associations between the risk scores could be attributed to causal relationships between the scores, with the rest attributed to familial confounders shared by the scores. There was consistent evidence for positive causal effects: of Cirrus, light areas, and bright areas on the brightest areas (accounting for 34%, 55%, and 85% of the associations, respectively); and of light areas and bright areas on Cirrus (accounting for 37% and 28%, respectively). Conclusions: In a mammogram, the lighter (less dense) areas have a causal effect on the brightest (highly dense) areas, including through a causal pathway via textural features. These causal relationships help us gain insight into the relative aetiological importance of different mammographic features in breast cancer. For example our findings are consistent with the brightest areas being more aetiologically important than lighter areas for screen-detected breast cancer; conversely, light areas being more aetiologically important for interval breast cancer. Additionally, specific textural features capture aetiologically independent breast cancer risk information from dense areas. These findings highlight the utility of ICE FALCON and family data in decomposing the associations between intercorrelated disease biomarkers into distinct biological pathways. [ABSTRACT FROM AUTHOR]

Background: Mammographic density (MD) is a strong risk factor for breast cancer. We aimed to evaluate the association between MD and breast cancer related risk factors among average-risk women in rural China. Methods: This is a population-based screening study. 12518 women aged 45–64 years with complete MD data from three maternal and childcare hospitals in China were included in the final analysis. ORs and 95%CIs were estimated using generalized logit model by comparing each higher MD (BI-RADS b, c, d) to the lowest group (BI-RADS a). The cumulative logistic regression model was used to estimate the ORtrend (95%CI) and Ptrend by treating MD as an ordinal variable. Results: Older age (ORtrend = 0.81, 95%CI: 0.79–0.81, per 2-year increase), higher BMI (ORtrend = 0.73, 95%CI: 0.71–0.75, per 2 kg/m2), more births (ORtrend = 0.47, 95%CI: 0.41–0.54, 3 + vs. 0–1), postmenopausal status (ORtrend = 0.42, 95%CI: 0.38–0.46) were associated with lower MD. For parous women, longer duration of breastfeeding was found to be associated with higher MD when adjusting for study site, age, BMI, and age of first full-term birth (ORtrend = 1.53, 95%CI: 1.27–1.85, 25 + months vs. no breastfeeding; ORtrend = 1.45, 95%CI: 1.20–1.75, 19–24 months vs. no breastfeeding), however, the association became non-significant when adjusting all covariates. Associations between examined risk factors and MD were similar in premenopausal and postmenopausal women except for level of education and oral hormone drug usage. Higher education was only found to be associated with an increased proportion of dense breasts in postmenopausal women (ORtrend = 1.08, 95%CI: 1.02–1.15). Premenopausal women who ever used oral hormone drug were less likely to have dense breasts, though the difference was marginally significant (OR = 0.54, P = 0.045). In postmenopausal women, we also found the proportion of dense breasts increased with age at menopause (ORtrend = 1.31, 95%CI: 1.21–1.43). Conclusions: In Chinese women with average risk for breast cancer, we found MD was associated with age, BMI, menopausal status, lactation, and age at menopausal. This finding may help to understand the etiology of breast cancer and have implications for breast cancer prevention in China. [ABSTRACT FROM AUTHOR]

The identification of risk factors helps radiologists assess the risk of breast cancer. Quantitative factors such as age and mammographic density are established risk factors for breast cancer. Asymmetric breast findings are frequently encountered during diagnostic mammography. The asymmetric area may indicate a developing mass in the early stage, causing a difference in mammographic density between the left and right sides. Therefore, this paper aims to propose a quantitative parameter named bilateral mammographic density difference (BMDD) for the quantification of breast asymmetry and to verify BMDD as a risk factor for breast cancer. To quantitatively evaluate breast asymmetry, we developed a semi-automatic method to estimate mammographic densities and calculate BMDD as the absolute difference between the left and right mammographic densities. And then, a retrospective case-control study, covering the period from July 2006 to October 2014, was conducted to analyse breast cancer risk in association with BMDD. The study included 364 women diagnosed with breast cancer and 364 matched control patients. As a result, a significant difference in BMDD was found between cases and controls (P < 0.001) and the case-control study demonstrated that women with BMDD > 10% had a 2.4-fold higher risk of breast cancer (odds ratio, 2.4; 95% confidence interval, 1.3-4.5) than women with BMDD ≤ 10%. In addition, we also demonstrated the positive association between BMDD and breast cancer risk among the subgroups with different ages and the Breast Imaging Reporting and Data System (BI-RADS) mammographic density categories. This study demonstrated that BMDD could be a potential risk factor for breast cancer. [ABSTRACT FROM AUTHOR]

Purpose: Mammographic density (MD), after accounting for age and body mass index (BMI), is a strong heritable risk factor for breast cancer. Genome-wide association studies (GWAS) have identified 64 SNPs in 55 independent loci associated with MD in women of European ancestry. Their associations with MD in Asian women, however, are largely unknown. Method: Using linear regression adjusting for age, BMI, and ancestry-informative principal components, we evaluated the associations of previously reported MD-associated SNPs with MD in a multi-ethnic cohort of Asian ancestry. Area and volumetric mammographic densities were determined using STRATUS (N = 2450) and Volpara™ (N = 2257). We also assessed the associations of these SNPs with breast cancer risk in an Asian population of 14,570 cases and 80,870 controls. Results: Of the 61 SNPs available in our data, 21 were associated with MD at a nominal threshold of P value < 0.05, all in consistent directions with those reported in European ancestry populations. Of the remaining 40 variants with a P-value of association > 0.05, 29 variants showed consistent directions of association as those previously reported. We found that nine of the 21 MD-associated SNPs in this study were also associated with breast cancer risk in Asian women (P < 0.05), seven of which showed a direction of associations that was consistent with that reported for MD. Conclusion: Our study confirms the associations of 21 SNPs (19/55 or 34.5% out of all known MD loci identified in women of European ancestry) with area and/or volumetric densities in Asian women, and further supports the evidence of a shared genetic basis through common genetic variants for MD and breast cancer risk. [ABSTRACT FROM AUTHOR]

Concurrent cohorts of 644,932 women aged 50–74 screened annually due to family history, dense breasts or biennially in the Ontario Breast Screening Program (OBSP) from 2011–2014 were linked to provincial administrative datasets to determine health system resource utilization and costs. Age-adjusted mean and median total healthcare costs (2018 CAD) and incremental cost differences were calculated by screening outcome and compared by recommendation using regression models. Healthcare costs were compared overall and 1 year after a false positive (n = 46,081) screening mammogram and 2 years after a breast cancer diagnosis (n = 6011). Mean overall healthcare costs by age were highest for those 60–74, particularly with annual screening for family/personal history (CAD 5425; 95% CI: 5308 to 5557) compared to biennial. Although the mean incremental cost difference was higher (23.4%) by CAD 10,235 (95% CI: 6141 to 14,329) per breast cancer for women screened annually for density ≥ 75% compared to biennially, the cost difference was 12.0% lower (−CAD 461; 95% CI: −777 to −114) per false positive result. In contrast, for women screened annually for family/personal history, the mean cost difference per false positive was 19.7% higher than for biennially (CAD 758; 95% CI: 404 to 1118); however, the cost difference per breast cancer was only slightly higher (2.5%) by CAD 1093 (95% CI: −1337 to CAD 3760). Understanding that associated costs of annual compared to biennial screening may balance out by age and outcome can assist decision-making regarding the use of limited healthcare resources. [ABSTRACT FROM AUTHOR]

High mammographic density (MD) increases breast cancer (BC) risk and creates a stiff tissue environment. BC risk is also increased in BRCA1/2 gene mutation carriers, which may be in part due to genetic disruption of the tumour suppressor gene Ras association domain family member 1 (RASSF1A), a gene that is also directly regulated by tissue stiffness. High MD combined with BRCA1/2 mutations further increase breast cancer risk, yet BRCA1/2 mutations alone or in combination do not increase MD. The molecular basis for this additive effect therefore remains unclear. We studied the interplay between MD, stiffness, and BRCA1/2 mutation status in human mammary tissue obtained after prophylactic mastectomy from women at risk of developing BC. Our results demonstrate that RASSF1A expression increased in MCF10DCIS.com cell cultures with matrix stiffness up until ranges corresponding with BiRADs 4 stiffnesses (~16 kPa), but decreased in higher stiffnesses approaching malignancy levels (>50 kPa). Similarly, higher RASSF1A protein was seen in these cells when co-cultivated with high MD tissue in murine biochambers. Conversely, local stiffness, as measured by collagen I versus III abundance, repressed RASSF1A protein expression in BRCA1, but not BRCA2 gene mutated tissues; regional density as measured radiographically repressed RASSF1A in both BRCA1/2 mutated tissues. The combinatory effect of high MD and BRCA mutations on breast cancer risk may be due to RASSF1A gene repression in regions of increased tissue stiffness.

Background: Breast density is strongly associated with breast cancer risk. Fully automated quantitative density assessment methods have recently been developed that could facilitate large-scale studies, although data on associations with long-term breast cancer risk are limited. We examined LIBRA assessments and breast cancer risk and compared results to prior assessments using Cumulus, an established computer-assisted method requiring manual thresholding. Methods: We conducted a cohort study among 21,150 non-Hispanic white female participants of the Research Program in Genes, Environment and Health of Kaiser Permanente Northern California who were 40–74 years at enrollment, followed for up to 10 years, and had archived processed screening mammograms acquired on Hologic or General Electric full-field digital mammography (FFDM) machines and prior Cumulus density assessments available for analysis. Dense area (DA), non-dense area (NDA), and percent density (PD) were assessed using LIBRA software. Cox regression was used to estimate hazard ratios (HRs) for breast cancer associated with DA, NDA and PD modeled continuously in standard deviation (SD) increments, adjusting for age, mammogram year, body mass index, parity, first-degree family history of breast cancer, and menopausal hormone use. We also examined differences by machine type and breast view. Results: The adjusted HRs for breast cancer associated with each SD increment of DA, NDA and PD were 1.36 (95% confidence interval, 1.18–1.57), 0.85 (0.77–0.93) and 1.44 (1.26–1.66) for LIBRA and 1.44 (1.33–1.55), 0.81 (0.74–0.89) and 1.54 (1.34–1.77) for Cumulus, respectively. LIBRA results were generally similar by machine type and breast view, although associations were strongest for Hologic machines and mediolateral oblique views. Results were also similar during the first 2 years, 2–5 years and 5–10 years after the baseline mammogram. Conclusion: Associations with breast cancer risk were generally similar for LIBRA and Cumulus density measures and were sustained for up to 10 years. These findings support the suitability of fully automated LIBRA assessments on processed FFDM images for large-scale research on breast density and cancer risk. [ABSTRACT FROM AUTHOR]

Objective: In order to explore the relationship between mammographic density of breast mass and its surrounding area and benign or malignant breast, this paper proposes a deep learning model based on C2FTrans to diagnose the breast mass using mammographic density. Methods: This retrospective study included patients who underwent mammographic and pathological examination. Two physicians manually depicted the lesion edges and used a computer to automatically extend and segment the peripheral areas of the lesion (0, 1, 3, and 5 mm, including the lesion). We then obtained the mammary glands' density and the different regions of interest (ROI). A diagnostic model for breast mass lesions based on C2FTrans was constructed based on a 7: 3 ratio between the training and testing sets. Finally, receiver operating characteristic (ROC) curves were plotted. Model performance was assessed using the area under the ROC curve (AUC) with 95% confidence intervals (CI), sensitivity, and specificity. Results: In total, 401 lesions (158 benign and 243 malignant) were included in this study. The probability of breast cancer in women was positively correlated with age and mass density and negatively correlated with breast gland classification. The largest correlation was observed for age (r = 0.47). Among all models, the single mass ROI model had the highest specificity (91.8%) with an AUC = 0.823 and the perifocal 5mm ROI model had the highest sensitivity (86.9%) with an AUC = 0.855. In addition, by combining the cephalocaudal and mediolateral oblique views of the perifocal 5 mm ROI model, we obtained the highest AUC (AUC = 0.877 P < 0.001). Conclusions: Deep learning model of mammographic density can better distinguish benign and malignant mass-type lesions in digital mammography images and may become an auxiliary diagnostic tool for radiologists in the future. [ABSTRACT FROM AUTHOR]

The European Society on Breast Imaging has recommended supplemental magnetic resonance imaging (MRI) every two to four years for women with mammographically dense breasts. This may not be feasible in many screening programs. Also, the European Commission Initiative on Breast Cancer suggests not implementing screening with MRI. By analyzing interval cancers and time from screening to diagnosis by density, we present alternative screening strategies for women with dense breasts. Our BreastScreen Norway cohort included 508 536 screening examinations, including 3125 screen-detected and 945 interval breast cancers. Time from screening to interval cancer was stratified by density measured by an automated software and classified into Volpara Density Grades (VDGs) 1–4. Examinations with volumetric density ≤3.4% were categorized as VDG1, 3.5%–7.4% as VDG2, 7.5%–15.4% as VDG3, and ≥15.5% as VDG4. Interval cancer rates were also determined by continuous density measures. Median time from screening to interval cancer was 496 (IQR: 391–587) days for VDG1, 500 (IQR: 350–616) for VDG2, 482 (IQR: 309–595) for VDG3 and 427 (IQR: 266–577) for VDG4. A total of 35.9% of the interval cancers among VDG4 were detected within the first year of the biennial screening interval. For VDG2, 26.3% were detected within the first year. The highest annual interval cancer rate (2.7 per 1000 examinations) was observed for VDG4 in the second year of the biennial interval. Annual screening of women with extremely dense breasts may reduce the interval cancer rate and increase program-wide sensitivity, especially in settings where supplemental MRI screening is not feasible. [ABSTRACT FROM AUTHOR]