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Tamoxifen prevents recurrence of breast cancer and is suggested for preventive risk-reducing therapy. Tamoxifen reduces mammographic density, a proxy for therapy response, but little is known about its effects in remodelling normal breast tissue. Our study, a substudy within the double-blinded dose-determination trial KARISMA, investigated tamoxifen-specific changes in breast tissue composition and histological markers in healthy women. We included 83 healthy women randomised to 6 months daily intake of 20, 10, 5, 2.5, 1 mg of tamoxifen or placebo. The groups were combined to “no dose” (0-1 mg), “low-dose” (2.5-5 mg) or “high-dose” (10-20 mg) of tamoxifen. Ultrasound-guided biopsies were collected before and after tamoxifen exposure. In each biopsy, epithelial, stromal and adipose tissues was quantified, and expression of epithelial and stromal Ki67, oestrogen receptor (ER) and progesterone receptor (PR) analysed. Mammographic density using STRATUS was measured at baseline and end-of-tamoxifen-exposure. We found that different doses of tamoxifen reduced mammographic density and glandular-epithelial area in premenopausal women and associated with reduced epithelium and increased adipose tissue. High-dose tamoxifen also decreased epithelial ER and PR expressions in premenopausal women. Premenopausal women with the greatest reduction in proliferation also had the greatest epithelial reduction. In postmenopausal women, high-dose tamoxifen decreased the epithelial area with no measurable density decrease. Tamoxifen at both low and high doses influences breast tissue composition and expression of histological markers in the normal breast. Our findings connect epithelial proliferation with tissue remodelling in premenopausal women and provide novel insights to understanding biological mechanisms of primary prevention with tamoxifen.

Purpose: Mammographic breast density (BD) is strongly associated to breast cancer (BC) risk; however, its association with survival is unclear.Methods: Using data from the Piedmont Cancer Registry (Registro Tumori Piemonte), we identified 693 women diagnosed with primary invasive BC between 2009-2014. We applied the Kaplan-Meier method to estimate overall survival in strata of BD and the log-rank test to assess survival differences. We evaluated the hazard ratios (HRs) of death using Cox proportional hazards model and HRs of BC-related and other causes of death using the cause-specific hazards regression model. Models included terms for BD (assessed according to the Breast Imaging Reporting and Data System [BI-RADS] density classification) and were adjusted for selected patient and tumour characteristics.Results: There were102 deaths, of which 49 were from BC. After 5 years, the overall survival was 70% in women with BI-RADS 1, 85% in those with BI-RADS 2, about 95% in those with BI-RADS 3-4 (p Conclusion: In women with BC, low BD has a negative prognostic impact.

This systematic review aimed to assess the methods used to classify mammographic breast parenchymal features in relation to prediction of future breast cancer including the time from mammogram to diagnosis of breast cancer, and methods for the identification of texture features and selection of features for inclusion in analysis. The databases including Medline (Ovid) 1946-,Embase.com1947-, CINAHL Plus 1937-, Scopus 1823-, Cochrane Library (including CENTRAL), andClinicaltrials.gov. were searched through October 2021 to extract published articles in English describing the relationship of parenchymal texture features with risk of breast cancer. Twenty-eight articles published since 2016 were included in the final review. Of these, 7 assessed texture features from film mammograms images, 3 did not report details of the image used, and the others used full field mammograms from Hologic, GE and other manufacturers. The identification of parenchymal texture features varied from using a predefined list to machine-driven identification. Reduction in number of features chosen for analysis in relation to cancer incidence then varied across statistical approaches and machine learning methods. The variation in approach and number of features identified for inclusion in analysis precluded generating a quantitative summary or meta-analysis of the value of these to improve predicting risk of future breast cancers. This updated overview of the state of the art revealed research gaps; based on these, we provide recommendations for future studies using parenchymal features for mammogram images to make use of accumulating image data, and external validation of prediction models that extend to 5 and 10 years to guide clinical risk management. By following these recommendations, we expect to improve risk classification and risk prediction for women to tailor screening and prevention strategies to level of risk.

PURPOSE: A dense breast on mammogram is a strong risk factor for breast cancer. Identifying factors that reduce mammographic breast density could thus provide insight into breast cancer prevention. Due to the limited number of studies and conflicting findings, we investigated the associations of medication use (specifically statins, aspirin, and ibuprofen) with mammographic breast density. METHODS: We evaluated these associations in 775 women who were recruited during an annual screening mammogram at Washington University School of Medicine, St. Louis. We measured mammographic breast density using Volpara. We used multivariable-adjusted linear regressions to determine the associations of medication use (statins, aspirin, and ibuprofen) with mammographic breast density. Least squared means were generated and back-transformed for easier interpretation. RESULTS: The mean age of study participants was 52.9 years. Statin use in the prior 12 months was not associated with volumetric percent density or dense volume, but was positively associated with non-dense volume. The mean volumetric percent density was 8.6% among statin non-users, 7.2% among women who used statins 1–3 days/week, and 7.3% among women who used statins ≥4 days/week (p-trend=0.07). The non-dense volume was 1297.1cm(3) among statin non-users, 1368.7cm(3) among women who used statins 1–3 days/week, and 1408.4cm(3) among those who used statins ≥4 days/week (p-trend=0.02). We did not observe statistically significant differences in mammographic breast density by aspirin or ibuprofen use. CONCLUSION: Statin, aspirin, and ibuprofen use was not associated with volumetric percent density and dense volume, but statin use was positively associated with non-dense volume. Any potential associations of these medications with breast cancer risk are unlikely to be mediated through an effect on volumetric percent density.

Mammographic breast density (MBD) is a marker of increased breast cancer (BC) risk, yet much remains to be clarified about the underlying mechanisms. We investigated whether DNA methylation patterns differ between high- vs. low-MBD women who developed BC during an 8.9-year median follow-up in the Florence section of the European Prospective Investigation into Cancer and Nutrition. We analysed 96 pairs of women with BC arising on high- vs. low-MBD breasts (BI-RADS category III–IV vs. I). DNA methylation was determined on pre-diagnostic blood samples using the Illumina Infinium MethylationEPIC BeadChip assay. The statistical analysis was conducted by performing an epigenome-wide association study (EWAS), by searching differentially methylated regions (DMRs) in gene promoters (followed by functional enrichment and gene annotation analysis); and through a “candidate pathways” approach focusing on pre-defined inflammation-related pathways. In EWAS, no single CpG site was differentially methylated between high- and low-MBD women after correction for multiple testing. A total of 140 DMRs were identified, of which 131 were hyper- and 9 hypo-methylated amongst high-MBD women. These DMRs encompassed an annotation cluster of 35 genes coding for proteins implicated in transcription regulation and DNA binding. The “apoptosis signalling” was the only inflammation-related candidate pathway differentially methylated between high- and low-MBD women. Pre-diagnostic methylation patterns differ between high- vs. low-MBD women who subsequently develop BC, particularly, in genes involved in the regulation of DNA transcription and cell apoptosis. Our study provides novel clues about the mechanisms linking MBD and BC.

Federal legislation requires patient notification of dense mammographic breast tissue because increased density is a marker of breast cancer risk and can limit the sensitivity of mammography. As previously described, we clinically implemented our deep learning model at the academic breast imaging practice where the model was developed with high clinical acceptance. Our objective was to externally validate our deep learning model on radiologist breast density assessments in a community breast imaging practice.Our deep learning model was implemented at a dedicated breast imaging practice staffed by both academic and community breast imaging radiologists in October 2018. Deep learning model assessment of mammographic breast density was presented to the radiologist during routine clinical practice at the time of mammogram interpretation. We identified 2174 consecutive screening mammograms after implementation of the deep learning model. Radiologist agreement with the model's assessment was measured and compared across radiologist groups.Both academic and community radiologists had high clinical acceptance of the deep learning model's density prediction, with 94.9% (academic) and 90.7% (community) acceptance for dense versus nondense categories (p0.001). The proportion of mammograms assessed as dense by all radiologists decreased from 47.0% before deep learning model implementation to 41.0% after deep learning model implementation (p0.001).Our deep learning model had a high clinical acceptance rate among both academic and community radiologists and reduced the proportion of mammograms assessed as dense. This is an important step to validating our deep learning model prior to potential widespread implementation.

We examined gut microbiome (GM) profiles in relation to mammographic breast density (BD) and body mass index (BMI) in healthy postmenopausal women. Eligible women were postmenopausal, had a BMI ≤ 35 kg/m2, and had not recently taken oral/IV antibiotics. All women provided a fecal sample and information on breast cancer risk factors. Mammographic BD was classified with the American College of Radiology’s BI-RADS BD classification system. Bacterial DNA was isolated from fecal samples and the V1–V2 hypervariable regions of 16S rRNA were sequenced on the Illumina MiSeq platform. We examined associations of GM with indices of within-sample (alpha) diversity and the ratio of the two main phyla (Firmicutes and Bacteroidetes; F/B ratio) with BD and BMI. Among 69 women with BD data, 39 had low BD (BI-RADS I/II) and 30 had high BD (BI-RADS III/IV). BMI was inversely associated with BD (mean BMI = 23.8 and 28.0 in women with high and low BD, respectively, p = 1.07 × 10–5). Similar levels of GM diversity were found across weight groups according to Shannon (p = 0.83); Inverse Simpson (p = 0.97); and Chao1 (p = 0.31) indices. F/B ratio and microbiota diversity were suggestively greater in women with high vs. low BD (p = 0.35, 0.14, 0.15, and 0.17 for F/B ratio, Shannon, Inverse Simpson and Chao1, respectively). Suggestive differences observed in women with high and low BD with respect to GM alpha diversity and prevalence of specific GM taxa need to be confirmed in larger studies.

Background: Women at high risk for breast cancer due to genetics or risk factor profiles are counseled to adopt lifestyle, behavioral, and dietary changes to help reduce their risk. These recommendations are based on studies of women at average risk, so their effectiveness in high-risk women is unclear. Methods: We evaluated the impact of physical activity, smoking, alcohol consumption, and intake of folate and carotenoids on mammographic breast density—a proxy for breast cancer risk—among 387 high-risk women. Exposures were self-reported on questionnaires. Breast dense area, nondense area, and percent dense area were measured from screening mammograms with Library for Breast Radiodensity Assessment software. Cross-sectional associations were estimated with multivariable quantile regression models. Results: After adjusting for age, adiposity, reproductive history, and use of postmenopausal hormones, no breast density measure was associated with physical activity level, smoking status, alcohol consumption, or estimated intake of folate, alpha-carotene, beta-carotene, lutein/zeaxanthin, and beta-cryptoxanthin. Lycopene intake was associated with lower dense area when comparing the highest and lowest intake categories (adjusted difference in median = −14 cm2, 95% confidence interval: −29 to 1.3 cm2). This association may be explained by incomplete adjustment for adiposity. Conclusions: Recommended lifestyle, behavioral, and dietary changes to mitigate personal risk of breast cancer do not substantially impact mammographic breast density measures. Impact: Alternative strategies, such as increased uptake of chemoprevention, may better serve risk reduction efforts in women at high risk for breast cancer.

The objective of this study is to determine the role of age at first full term pregnancy (FFTP), and mammographic breast density (MBD) in postmenopausal women. For this purpose women, age 50-69y, participating in the Flemish (Belgium) population based breast cancer screening program were invited to participate in the study during their visit at the mammography department in the University Hospital Leuven (UZLeuven) or at the mobile unit of their hometown. A self-administered questionnaire providing detailed information on lifestyle and environmental exposure was completed by the participants. In this analysis 1,034 women were included (mean age of 59.1 years (range: 49.2 to 69.8). The three parameters of MBD were the percentage glandular tissue of the total breast volume (GLAND), the ratio of the glandular tissue volume compared to the whole breast volume or Volumetric Breast Density (VBD) and the BI-RADS density classification (VolparaDataManager®, USA). The first two parameters (GLAND and VBD) were transformed according to the natural logarithm to improve normality. These two parameters were linked with the FFTP via a piecewise linear regression (PLR) model using the NLIN procedure in SAS (version 9.4, SAS Institute, Cary, NC, USA). For the BI-RADS, we used ordinal logistic regression analysis. All models were corrected for a priori chosen variables: age at menarche, the ever use of oral contraceptive pill (OC), hormonal use at menopause, age at MBD measurement, and the body mass index at participation. Estimates are provided as a % change (95% confidence intervals [CI]).The average age at FFTP was 26.1 years (5th-95th percentile: 20 to 34). The PLR model estimated the breakpoint in our analysis at a FFTP of 25.7 years (95% CI: 22.3 to 29.1). For the woman with a FFTP younger than 25.7 years, the association between GLAND or VBD and FFTP is statistically not significant. For woman with a FFTP above 25.7 years, each year increase in FFTP was associated with 1.3% increase in GLAND (95% CI: 0.0% to 2.5%) and 1.5% increase in VBD (95% CI: 0.2% to 2.8%). Analysis of the BI-RADS showed similar results, the odds of belonging to a higher BI-RADS classification (e.g. from class 1 to class 2) increased with 5.4% (95% CI: 0.0% to 11.0%) for each year increase in FFTP age after the age of 25.7 years. Among other reproductive factors such as age at menarche, for every year delay there is 3.5% higher GLAND (95% CI: 1.4 % to 5.6%) and 2.5% higher VBD (95% CI: 0.5% to 4.6%) and the ever use of OC resulted in 10.0 % decrease in GLAND (95% CI: -18.8% to -0.2%) and 10.2% decrease in VBD (95%CI:-19.1% to -0.3%).In conclusion, this study is to our knowledge the first one to show that mammographic breast density is significantly increased when pregnancy takes place after 25.7 years of age. Later age at menarche is increasing and use of oral contraceptive is reducing mammographic breast density at postmenopause. (This study was supported by a Grant of the Breast Cancer Organization Think Pink of Belgium, the Department of Breast Radiology and Mobile Unit of the University Hospital Leuven, the Centre of Environmental Sciences and the Centre of Biostatistics of the Hasselt University, and NIH Grant CA06927 to FCCC, PA, USA). Citation Format: Magda Johanna Vandeloo, Kristof Y. Neven, Liesbeth M. Bruckers, Jose Russo, Liesbeth Vancoillie, Esmee M. Bijnens, Eliane Kellen, Tim S. Nawrot, Chantal Van Ongeval. Age of first full term pregnancy and other reproductive factors affect mammographic breast density in postmenopausal women [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS7-32.

Aim of the study The aim of the study was to analyze three single nucleotide polymorphisms - rs1520220, rs2946834, rs6219 - of the IGF-1 gene in the context of breast mammographic density. Material and methods The research material included 202 samples of the peripheral blood of women with increased mammographic breast density and 238 samples of the epithelium from the oral mucosa of women without diagnosed pathological changes of the breast and with no family history of breast and/or ovarian cancer. The real-time polymerase chain reaction method was applied for analysis of polymorphisms. Results rs1520220 polymorphism was associated with increased mammographic density of the breasts. The presence of the CC genotype in the IGF-1 gene increased the risk of developing higher breast density visible in mammography by 2.43-fold. CC homozygotes (rs1520220) correlated with higher Breast Imaging-Reporting and Data System scale (3 vs. 4 and 5) (OR = 5.6; 95% CI: 1.82-16.3, p = 0.001). In the present study no relationship was detected between rs6219 and rs2946834 polymorphism and mammographic breast density. Conclusions The results suggest that the rs1520220 polymorphism of the IGF-1 gene plays an important role in the occurrence of increased mammographic breast density.

Background Endometriosis is a common benign but painful gynecologic condition. Studies suggest that the risk of some types of malignancies such as breast cancer is higher in women with endometriosis. Mammographic breast density (MBD) is known as an important predictor for breast cancer. The present study aimed to investigate the potential relationship between endometriosis and MBD. Methods This cross-sectional study was conducted on 370 women over 40 years of age. Laparoscopic surgery was carried out for the diagnosis of endometriosis. MBD was classified into four categories according to the ACR BI-RADS classification. Statistical analysis was performed using SPSS software to evaluate the potential association between variables. Results The mean age of all participants was 47.2 ± 6.4 years, and most participants (76.8%) were premenopausal. Multivariate analysis of the potential predictors of MBD, including age, body mass index, oral contraceptive consumption, progesterone consumption, family history of breast cancer and endometriosis showed that age (P value = 0.002), history of progesterone consumption (P value = 0.004) and endometriosis (P value = 0.006) were independent factors for MBD. Conclusion This study indicated that endometriosis had an inverse association with MBD. Age and history of progesterone use were also independent influential factors for MBD. This finding shows that the positive association between breast cancer and endometriosis is not mediated through MBD.

Background The low subject contrast between cancerous and fibroglandular tissue could obscure breast abnormalities. Purpose To investigate radiologists’ performance for detection of breast cancer in low and high mammographic density (MD) when cases are digitally acquired. Material and Methods A test set of 60 digital mammography cases, of which 20 were cancerous, were examined by 17 radiologists. Mammograms were categorized as low (≤50%) or high (>50%) MD and rated for suspicion of malignancy using the Royal Australian and New Zealand College of Radiology (RANZCR) classification system. Radiologist demographics including cases read per year, age, subspecialty, and years of reporting were recorded. Radiologist performance was analyzed by the following metrics: sensitivity; specificity; area under the receiver operating characteristic (ROC) curve (AUC), location sensitivity, and jackknife free-response ROC (JAFROC) figure of merit (FOM). Results Comparing high to low MD cases, radiologists showed a significantly higher sensitivity ( P = 0.015), AUC ( P = 0.003), location sensitivity ( P = 0.002), and JAFROC FOM ( P = 0.001). In high compared to low MD cases, radiologists with Conclusion In this experimental situation, radiologists’ performance was higher when reading cases with high compared to low MD. Experienced radiologists were able to precisely localize lesions in breasts with higher MD. Further studies in unselected screening materials are needed to verify the results.

Introduction Mammographic breast density is associated with a four to six times increased risk for breast cancer. Mammographic breast density varies by ethnicity, geographical region and age. The aim of this study was to document for the first time the mammographic breast density of Jordanian women and to explore its relationship with age. Methods Mammograms completed at King Abdullah University Hospital (Irbid, Jordan) between January 2016 and August 2018 were retrospectively reviewed and classified for breast density using the American College of Radiology (ACR) Breast Imaging‐Reporting and Data System (BI‐RADS). Descriptive analyses and Kurskal–Wallis test were used to examine the association between age and mammographic breast density. Results A total of 659 mammograms were reviewed. A significant inverse relationship was observed between age and breast density (P, The mammographic breast density of Jordanian women has been shown to be high across all age groups. Increased mammographic breast density is associated with increased breast cancer risk and renders mammography a less effective technique for the early detection of breast cancer. Breast cancer screening of Jordanian women should be individualised to develop screening protocols and include additional adjunct imaging to best manage women at high risk.

Progesterone is a proliferative hormone in the breast but the associations of genetic variations in progesterone-regulated pathways with mammographic breast density (MD) in premenopausal women and whether these associations are mediated through circulating progesterone are not clearly defined. We, therefore, investigated these associations in 364 premenopausal women with a median age of 44 years. We sequenced 179 progesterone receptor (PGR)-related single nucleotide polymorphisms (SNPs). We measured volumetric percent density (VPD) and non-dense volume (NDV) using Volpara. Linear regression models were fit on circulating progesterone or VPD/NDV separately. We performed mediation analysis to evaluate whether the effect of a SNP on VPD/NDV is mediated through circulating progesterone. All analyses were adjusted for confounders, phase of menstrual cycle and the Benjamini-Hochberg false discovery (FDR) adjusted p-value was applied to correct for multiple testing. In multivariable analyses, only PGR rs657516 had a direct effect on VPD (averaged direct effect estimate = - 0.20, 95%CI = - 0.38 ~ - 0.04, p-value = 0.02) but this was not statistically significant after FDR correction and the effect was not mediated by circulating progesterone (mediation effect averaged across the two genotypes = 0.01, 95%CI = - 0.02 ~ 0.03, p-value = 0.70). Five SNPs (PGR rs11571241, rs11571239, rs1824128, rs11571150, PGRMC1 rs41294894) were associated with circulating progesterone but these were not statistically significant after FDR correction. SNPs in PGR-related genes were not associated with VPD, NDV and circulating progesterone did not mediate the associations, suggesting that the effects, if any, of these SNPs on MD are independent of circulating progesterone. Supplementary information The online version contains supplementary material available at 10.1007/s12672-021-00438-1.

Background: Air pollution is one of the major public health challenges in many parts of the world possibly has an association with breast cancer. However, the mechanism is still unclear. This study aimed to find an association between exposure to six criteria ambient air pollutants (PM2.5, PM10, SO2, NO2, O3, and CO) and mammographic breast density (MBD), as one of the strongest predictors for developing breast cancer, in women living in Tehran, Iran.Methods: Participants were selected from women attending two university hospitals for screening mammography from 2019 to2021. Breast density was rated by two expert radiologists. Individual exposures to 3-years ambient air pollution levels at the residence were estimated.Results: The final analysis in 791 eligible women showed that low and high breast density was detected in 34.8% and 62.2% of participants, respectively. Logistic regression analysis after considering all possible confounding factors represented that an increase in 10 units of NO2 (ppb) exposure was associated with an increased risk of breast density with an OR equal to 1.47 (95% CI: 1.10 to 1.97). Furthermore, CO level was associated with a decreasing breast density (OR = 0.33, 95% CI = 0.17 to 0.64). None of the other pollutants were associated with breast density.Conclusion: MBD was associated with higher levels of ambient air NO2 and lower levels of CO. Perhaps MBD monitoring as an available tool in each population, can help the prediction of future breast cancer occurrence and finding the high-risk geographic areas.

We compared accuracy for breast cancer (BC) risk stratification of a new fully automated system (DenSeeMammo—DSM) for breast density (BD) assessment to a non-inferiority threshold based on radiologists’ visual assessment. Pooled analysis was performed on 14,267 2D mammograms collected from women aged 48–55 years who underwent BC screening within three studies: RETomo, Florence study and PROCAS. BD was expressed through clinical Breast Imaging Reporting and Data System (BI-RADS) density classification. Women in BI-RADS D category had a 2.6 (95% CI 1.5–4.4) and a 3.6 (95% CI 1.4–9.3) times higher risk of incident and interval cancer, respectively, than women in the two lowest BD categories. The ability of DSM to predict risk of incident cancer was non-inferior to radiologists’ visual assessment as both point estimate and lower bound of 95% CI (AUC 0.589; 95% CI 0.580–0.597) were above the predefined visual assessment threshold (AUC 0.571). AUC for interval (AUC 0.631; 95% CI 0.623–0.639) cancers was even higher. BD assessed with new fully automated method is positively associated with BC risk and is not inferior to radiologists’ visual assessment. It is an even stronger marker of interval cancer, confirming an appreciable masking effect of BD that reduces mammography sensitivity., SCOPUS: ar.j, info:eu-repo/semantics/published

OBJECTIVES. The present paper aims to investigate the effects of both progesterone and progestin treatment mainly related to the occurrence of breast cancer in women. MATERIALS AND METHODS. Extensive systematic bibliographic review of Greek and International articles was conducted through the electronic databases Pubmed, Cinahl, Uptodate, and Google Scholar for the identification of articles related to progesterone, progestins and breast cancer treatment. RESULTS. Hormone therapy with the use of estrogen alone presents a small increased risk or does not present at all an increased risk of breast cancer. With ORs in some studies below 1.0 in current users for 3 plus years and safe option until 7 years, while in other studies the risk was increased with the ORs 1.29. However, the use of estrogen in combination with progestogens, depending on the type of progestogens, shows an increased risk of breast cancer, with the ORs to vary between 1.14- 2.38 from 3 to 5 years and is inversely proportional to the time of its use. This risk varies depending on the combination of the preparations. Other factors that are associated with breast cancer risk when receiving hormone therapy are the years that hormone therapy is taken, directly proportional to the risk. At higher risk are older women, women with low body mass index in menopause (BMI

Objectives High mammographic breast density (MBD) is one of the strongest breast cancer risk factors. The results of some epidemiological studies suggest that one of the lifestyle factors likely altering breast density is cigarette smoking. The aim of this study was to assess the association between active smoking, considering in detail the duration and intensity of smoking, and MBD among Polish women. Material and Methods A cross-sectional study included 467 women aged 40–60 years who underwent screening mammography in Łódź, Poland. Volumetric mammographic density, fibroglandular tissue volume and non-fibroglandular tissue volume were determined based on the analysis of mammographic image (in the “for processing” format) using Volpara Imaging Software. Current and lifetime intensity of cigarette smoking was assessed based on the data from interviews. Linear and logistic regressions were fitted with estimated MBD parameters as the outcomes, and life-long smoking duration and intensity as the determinants, adjusted for major confounders. Results The former smokers had a significantly lower volumetric mammographic density compared to the non-smokers in the crude analysis (p = 0.022). However, the associations became insignificant after adjustments for important confounding factors. The analyses adjusted for important confounders revealed an inverse statistically significant association between the number of pack-years and volumetric mammographic density among the current smokers (p = 0.048). Conclusions The observed result is consistent with the majority of previous studies that analyzed the associations between mammographic density and life-long smoking duration. Int J Occup Med Environ Health. 2021;34(6):805–15

INTRODUCTION: Breast density is associated with an increased risk of developing breast cancer. The present study aims to determine the distribution and interobserver variability of mammographic breast density in patients with invasive breast carcinoma, using the fifth edition of BI-RADS guidelines. It is part of a larger study to ascertain the association between mammographic breast density and breast cancer characteristics. MATERIALS AND METHODS: Two radiologists independently assessed 122 mammograms of patients with histologically confirmed invasive breast carcinoma and assigned the breast density to categories A-D based on the fifth edition of BI-RADS guidelines. The interobserver variability was calculated using the weighted kappa coefficient and the level of agreement was determined using the Landis and Koch guidelines. RESULTS: In this study, 55.7% of patients with invasive breast carcinoma were assigned to category B, followed by category C with 36.1%. Only 4.1% of patients were assigned to categories A and D respectively. There was substantial agreement between the two readers’ judgement, k=0.610 (95% CI, 0.523-0.697), p < 0.001 for specific BI-RADS categories. CONCLUSION: Among patients with invasive breast carcinoma, there were more patients with non-dense breasts than dense breasts. Overall, there is a substantial interobserver agreement when radiologists used the fifth edition of the BI-RADS guideline, which is in line with results found in the literature. This suggests that the BI-RADS density classification is an acceptable method and can be reliably used in clinical practice.

Mammography (MMG) is widely used for the screening and diagnosis of breast cancer. High breast density on MMG prevents breast cancer detection. In the present study, we analyzed the effects of parity and infant feeding method on mammographic breast density.Ninety women diagnosed as having breast cancer were analyzed. Using the results of medical questionnaires, subjects were grouped according to their parity and infant feeding method, into either nulliparity or parity, and formula-feeding or breastfeeding. Each group consisted of 30 consecutive women from 45 to 49 years. Nulliparous group was unaffected by either parity or breastfeeding, formula-feeding group was affected by parity but not breastfeeding, and breastfeeding group was affected by both parity and breastfeeding. Mammographic breast density was evaluated on the contralateral and cancer-free breast, and was classified into ≥ 50% dense as high breast density and 50% dense according to the Boyd quantitative scale. The association of parity and infant feeding method with breast density on MMG was analyzed using the Fisher's exact test.Regarding parity, high breast densities were observed in 40% and 17% of nulliparous and parous breasts, respectively. Nulliparous breasts had significantly higher density than parous breasts (p = 0.010). Regarding infant feeding method, 17% of breasts in both the formula-feeding and breastfeeding groups had high densities. Infant feeding method was not associated with mammographic breast density (p = 1.0).Nulliparity is associated with higher breast density, and parity, regardless of infant feeding method, induces atrophic changes of breast tissue.

Mammographic breast density (BDen), the ratio of glandular volume (GVol) to breast volume (BVol), is the second most prevalent risk factor for breast cancer (BC). Newly developed photon counting technology allows precise and systematic measurements in clinical practice. Our objective is to see how these parameters change with age in women with and without cancer. This retrospective study analyzed results of BDen, GVol, and BVol in 64,182 mammograms performed with photon counting technology on 32,448 consecutive women from April 2014 to December 2015. Only their first study was included. We excluded women with incomplete data or with breast implants. Mean age of women without BC diagnosed during the study period was 52.1 ± 9.9. BC and was found in 263 women (0.81%). Mean age was 53.0 ± 10.4. BDen, GVol, and BVol were 14%, 24%, and 2% greater in women with BC (P

Aim This cross-sectional study aimed to evaluate the associations between low and high-fat dairy food (DF) intake and breast density (BD). Materials and methods A total of 775 premenopausal and 771 postmenopausal women recruited during screening mammography completed a food frequency questionnaire. Adjusted linear regression models were used to assess the associations. Results As frequency quartiles of high-fat DF consumption increased, the adjusted mean of absolute BD increased from 31.5 to 36.1 cm2 for all women (ptrend=0.0034) and from 42.4 to 50.1 cm2 for premenopausal women (ptrend=0.0047). Conversely, as frequency quartiles of low-fat DF consumption increased, the adjusted mean of absolute BD decreased from 34.7 to 29.6 cm2 for all women (ptrend=0.001) and from 49.7 to 40.7 cm2 for premenopausal women (ptrend=0.0012). Conclusion A higher intake of high-fat and low-fat DF is respectively associated with higher and lower BD, particularly in premenopausal women.

There are no previous data on the influence of drospirenone (DRSP) in combination with estradiol (E2) on the breast in postmenopausal women. The objective of this study was to evaluate the effect of DRSP or norethisterone acetate (NETA) in continuous combination with E2 on two surrogate markers for breast cancer - mammographic breast density and proliferation of breast epithelial cells - in healthy postmenopausal women.120 healthy, naturally postmenopausal women were randomized to either 2 mg of DRSP or 0.5 mg of NETA in continuous combination with 1 mg of oral E2. The women underwent mammography and fine-needle aspiration biopsy of the breast at baseline and after six months of treatment.Digitized mammographic breast density and breast cell proliferation.There was a significant increase in mammographic breast density after treatment in both groups (median increase 5.5% for E2/DRSP and 2.3% for E2/NETA, respectively, p 0.001), but with no significant difference between groups. The proliferation of breast epithelial cells also increased in both groups (p 0.001, respectively), with a significantly larger increase in the E2/DRSP group than in the E2/NETA group (2.5% versus 0.7%, respectively, p 0.05). Systolic blood pressure had decreased significantly after 6 months of treatment in the E2/DRSP group (p 0.05) but not in the E2/NETA group.Breast density increased to a similar degree with E2/DRSP and E2/NETA. Proliferation of breast epithelial cells also increased significantly in both groups but was slightly more pronounced in the E2/DRSP group.

PURPOSE: Previous studies suggest that coffee and caffeine intake may be associated with reduced breast cancer risk. 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 caffeine intake with mammographic density in premenopausal women. METHODS: This study included 751 cancer-free women within the Nurses’ Health Study II cohort. 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. Energy-adjusted adolescent caffeine intake was estimated using the data from a food frequency questionnaire. Information regarding breast cancer risk factors was obtained from questionnaires closest to the mammogram date. We used generalized linear regression to quantify associations of caffeine intake with breast density measures. RESULTS: In multivariable analyses, adolescent caffeine intake was not associated with any of the density phenotypes (caffeine 4(th) vs. 1(st) quartile: β=−1.27, 95% CI −4.62; 2.09, p-trend=0.55 for percent density; β=−0.21, 95% CI −0.76, 0.34, p-trend=0.65 for absolute dense area, and β=0.23, 95% CI −0.28, 0.74, p-trend=0.50 for non-dense area). Additional adjustment of the models for body mass index at age 18 resulted in attenuation of the risk estimates. CONCLUSIONS: Our findings do not support the hypothesis that adolescent caffeine intake is associated with premenopausal mammographic breast density.

Mammographic density is an established breast cancer risk factor within many ethnically different populations. The distribution of mammographic density has been shown to be significantly lower in Western Australian Aboriginal women compared to age- and screening location-matched non-Aboriginal women. Whether mammographic density is a predictor of breast cancer risk in Aboriginal women is unknown. We measured mammographic density from 103 Aboriginal breast cancer cases and 327 Aboriginal controls, 341 non-Aboriginal cases, and 333 non-Aboriginal controls selected from the BreastScreen Western Australia database using the Cumulus software program. Logistic regression was used to examine the associations of percentage dense area and absolute dense area with breast cancer risk for Aboriginal and non-Aboriginal women separately, adjusting for covariates. Both percentage density and absolute dense area were strongly predictive of risk in Aboriginal women with odds per adjusted standard deviation (OPERAS) of 1.36 (95% CI 1.09, 1.69) and 1.36 (95% CI 1.08, 1.71), respectively. For non-Aboriginal women, the OPERAS were 1.22 (95% CI 1.03, 1.46) and 1.26 (95% CI 1.05, 1.50), respectively. Whilst mean mammographic density for Aboriginal women is lower than non-Aboriginal women, density measures are still higher in Aboriginal women with breast cancer compared to Aboriginal women without breast cancer. Thus, mammographic density strongly predicts breast cancer risk in Aboriginal women. Future efforts to predict breast cancer risk using mammographic density or standardize risk-associated mammographic density measures should take into account Aboriginal status when applicable.

To investigate how breast density affects screening performance indicators in a digital mammography context.We assessed the effect of breast density over the screen-detected and interval cancers rates, false-positives, specificity, sensitivity, recall rate, positive predictive value of recall (PPV-1), and PPV of invasive tests (PPV-2). Radiologists classified breast density using the BIRADS System. We used generalized estimating equations to account for within-woman correlation by means of the robust Huber-White variance estimator.We included 177,164 women aged 50-69 years who underwent 499,251 digital mammograms from 2004 to 2015 in Spain. According to the fibroglandular tissue percentage, 24.7% of mammograms were classified as BI-RADS 1 (25% glandular), 54.7% as BI-RADS 2 (25-50% glandular), 14.0% as BI-RADS 3 (51-75% glandular) and 6.6% as BI-RADS 4 (75% glandular). Overall, women with BI-RADS 3 had the highest screen-detected cancer rate (5.9 per 1000) and BI-RADS 4 the highest interval cancer rate (2.4 per 1000). Sensitivity decreased from 89.2% in women with BI-RADS 1 to 67.9% in BI-RADS 4. Both PPV-1 and PPV-2 decreased from 10.4% to 5.7% and from 49.8% to 32.4% in women with BI-RADS 1 and BI-RADS 4, respectively. Women aged 60-69 years with BI-RADS 4 had the lowest sensitivity (54.9%) and the highest interval cancer rate (3.8 per 1000).Performance screening measures are negatively affected by breast density falling to a lower sensitivity and PPV, and higher interval cancer rate as breast density increases. Particularly women aged 60-69 years with75% glandular breasts had the worst results and therefore may be candidates for screening using other technologies.

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 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 vs. 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) & (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. Conclusion: 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.

Purpose Mammographic density (MD) is one of the strongest risk factors for breast cancer (BC). However, the influence of MD on the BC prognosis is unclear. The objective of this study was therefore to investigate whether percentage MD (PMD) is associated with a difference in disease-free or overall survival in primary BC patients. Methods A total of 2525 patients with primary, metastasis-free BC were followed up retrospectively for this analysis. For all patients, PMD was evaluated by two readers using a semi-automated method. The association between PMD and prognosis was evaluated using Cox regression models with disease-free survival (DFS) and overall survival (OS) as the outcome, and the following adjustments: age at diagnosis, year of diagnosis, body mass index, tumor stage, grading, lymph node status, hormone receptor and HER2 status. Results After median observation periods of 9.5 and 10.0 years, no influence of PMD on DFS (p = 0.46, likelihood ratio test (LRT)) or OS (p = 0.22, LRT), respectively, was found. In the initial unadjusted analysis higher PMD was associated with longer DFS and OS. The effect of PMD on DFS and OS disappeared after adjustment for age and was caused by the underlying age effect. Conclusions Although MD is one of the strongest independent risk factors for BC, in our collective PMD is not associated with disease-free and overall survival in patients with BC., Highlights • Mammographic density (MD) is one of the strongest risk factors for breast cancer (BC). • Results from a large BC cohort on the impact of percentage MD (PMD) on prognosis. • No association between PMD with disease-free and overall survival was found. • Risk stratification based on PMD seems not feasible for clinical routine.

Background The purpose of this study was to investigate the relationship between breast density, age, and mammographic lesion type among Chinese breast cancer patients included in a large clinical dataset. Methods A review of mammographic images acquired between July 2014 and June 2017 from a total of 9716 retrospectively registered breast cancer patients was conducted. Mammographic breast density was defined according to the American College of Radiology Breast Imaging Reporting and Data System (ACR BI-RADS) 4-class density rating. Mammographic lesion types were defined according to the ACR BI-RADS, including mass, mass with calcifications, calcifications, architectural distortion/asymmetries, and architectural distortion/asymmetries with calcifications. Three experienced breast radiologists interpreted all mammograms. The chi-square (χ2) test and Pearson correlation analyses were performed to assess the relationship between breast density, age, and mammographic lesion type. Results A significant inverse relationship was observed between the BI-RADS breast density rating given by radiologists and patient age (r = − 0.521, p p p Conclusions In general, we identified a relationship between patient breast density, age, and mammographic lesion type. This finding may provide a basis for clinical diagnoses and support development of breast cancer screening programs in China.

It can be acknowledged from the literature that the high density of breast tissue is a root cause for the escalation of breast cancer among the women, imparting its prime role in Cancer Death among women. Moreover, in this era where computer-aided diagnosis systems have become the right hand of the radiologists, the researchers still find room for improvement in the feature selection techniques. This research aspires to propose hybrid versions of Biogeography-Based Optimization and Genetic Algorithm for feature selection in Breast Density Classification, to get rid of redundant and irrelevant features from the dataset; along with it to achieve the superior classification accuracy or to uphold the same accuracy with lesser number of features. For experimentation, 322 mammogram images from mini-MIAS database are chosen, and then Region of Interests (ROI) of seven different sizes are extracted to extract a set of 45 texture features corresponding to each ROI. Subsequently, the proposed algorithms are used to extract an optimal subset of features from the hefty set of features corresponding to each ROI. The results indicate the outperformance of the proposed algorithms when results were compared with some of the other nature-inspired metaheuristic algorithms using various parameters.

Mammographic breast density is a strong risk factor for breast cancer, and therefore cancer screening programs are widely implemented to reduce cancer incidence and mortality. Use of hormone therapy (HT) during menopause and mammographic breast density are both associated with an increased risk of breast cancer. An increase in mammographic breast density might result from an absolute increase in fibrous stroma, ductal tissue, or terminal ductal lobular units or from a decrease in fat content with only relative increases in the other elements. Such changes would reflect alterations in proliferation, apoptosis, or a differentiated function. The increase in breast density is associated with the accumulation of target cells for neoplastic transformation providing a biological mechanism why the increase in breast density is a risk factor. Standardization of the mammographic screening has contributed significantly to assess the importance of mammographic density as a predictor of risk. The American College of Radiology (ACR) has established a uniform way for radiologists to describe mammogram findings. The system, called Breast Imaging Reporting and Data system (BI-RADS), includes seven standardized categories, or levels. Digital mammography has been able to make more reproducible and comparable the breast density and be more quantitative in nature. Although there are many software applications, the one that has been shown to be highly accepted in many radiology centers is the VOLPARA (VolparaDataManager®, USA).

Aim: To investigate patterns of false-positive decisions in mammography. The relationship between frequently falsely recalled mammograms, breast density, assigned confidence score of malignancy and presenting mammographic features of the case were explored. Method: There were 2 phases to this experiment. In Phase 1, 200 cases (180 normal, 20 cancer containing) were read by 5 Australian radiologists under three recall conditions: free recall, 15% recall rate and 10% recall rate. The readers assigned any suspicious cases a confidence score of malignancy. The most frequently recalled normal cases were identified via pattern analysis and assess for chance finding. These cases underwent case feature analysis including mammographic breast density and assigned confidence score of malignancy. In Phase 2: Seven expert breast radiologists were asked to review the cases knowing they were normal and rate rating each case for displaying “normal” and/or “benign” mammographic features and give a difficulty rating. Results: 18 normal cases were identified as repeatedly recalled across all recall rates. There was no correlation between mammographic breast density and FP frequency. Under the 10% recall rate condition, normal cases in the extremely difficult category were associated with significantly high confidence scores of malignancy (X2(2) =6.701, p=0.035). There was moderate correlation between the cohorts on case difficulty and the survey results indicated that benign disease was often marked as malignant. Conclusion: There was a clear pattern of normal mammograms persistently recalled. FP cases were not associated with higher mammographic breast density but did receive high confidence scores of malignancy. Radiologists agreed that highly difficult cases did not fit with their knowledge of normal variants. The phenomenon of repetitive occurrences of FP experience for screened women warrants further research on the analysis of the textural characteristics of normal mammographic cases.

Background The risk of breast cancer related to changes in breast density over time, including its regression and persistence, remains controversial. The authors investigated the relationship between breast density and its changes over time with the development of breast cancer in premenopausal and postmenopausal women. Methods The current cohort study included 74,249 middle-aged Korean women (aged ≥35 years) who were free of breast cancer at baseline and who underwent repeated screening mammograms. Mammographic breast density was categorized according to the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS). A dense breast was defined as heterogeneously dense or extremely dense, and changes in dense breasts between baseline and subsequent follow-up were classified as none, developed, regressed, or persistent dense breast. Results During a median follow-up of 6.1 years (interquartile range, 4.1-8.8 years), a total of 803 incident breast cancers were identified. Baseline breast density was found to be positively associated with incident breast cancer in a dose-response manner, and this association did not significantly differ by menopausal status. The multivariable-adjusted hazard ratios (HRs) for breast cancer comparing "heterogeneously dense" and "extremely dense" categories with the nondense category were 1.96 (95% confidence interval [95% CI], 1.40-2.75) and 2.86 (95% CI, 2.04-4.01), respectively. With respect to changes in dense breasts over time, multivariable-adjusted HRs for breast cancer comparing persistent dense breast with none were 2.37 (95% CI, 1.34-4.21) in premenopausal women and 3.61 (95% CI, 1.78-7.30) in postmenopausal women. Conclusions Both baseline dense breasts and their persistence over time were found to be strongly associated with an increased risk of incident breast cancer in premenopausal and postmenopausal women. Lay summary Both baseline breast density and its changes over time were found to be independently associated with the risk of breast cancer in both premenopausal and postmenopausal women. The risk of incident breast cancer increased in women with persistent dense breasts, whereas the breast cancer risk decreased as dense breasts regressed. The findings of the current study support that both dense breasts at baseline and their persistence over time are independent risk factors for developing breast cancer.

Mammography is the standard for breast cancer screening. The sensitivity of mammography in identifying breast cancer, however, is reduced for women with dense breasts. Thirty-eight states have passed laws requiring that all women be notified of breast tissue density results in their mammogram report. The notification includes a statement that differs by state, encouraging women to discuss supplemental screening options with their health care professionals (HCPs). Several supplemental screening tests are available for women with dense breast tissue, but no established guidelines exist to direct HCPs in their recommendation of preferred supplemental screening test. Tailored screening, which takes into consideration the patient's mammographic breast density and lifetime breast cancer risk, can guide breast cancer screening strategies that are more comprehensive. This review describes the benefits and limitations of the various available supplemental screening tests to guide HCPs and patients in choosing the appropriate breast cancer screening.

Objective: The aim of the study is to compare inter-observer agreement between general radiologists in the classification of mammographic breast density using TABAR’s pattern and BI-RADS classification from two-view Full-Field Digital Mammography (FFDM). Methods: A data set of 400 mammograms was evaluated by three general radiologists. The radiologists independently reviewed the images and classified the parenchymal pattern according to BI-RADS and TABAR classification systems. Inter-observer agreements were analyzed using kappa statistics. Results: Inter-observer agreement for the BI-RADS is slight to fair (Reviewer 1 versus Reviewer 2: k=0.19, Reviewer 1 versus Reviewer 3, k=0.07 and Reviewer 2 versus Reviewer 3, k=0.49) and for TABAR is fair to moderate (Reviewer 1 versus Reviewer 2: k=0.23, Reviewer 1 versus Reviewer 3, k=0.31 and Reviewer 2 versus Reviewer 3, k=0.50). Conclusion: Our study demonstrates a poor level of agreement in breast parenchymal pattern and density based on both BI-RADS and TABAR classifications. Thus, breast density in risk stratification of breast cancer should be used with caution in our local practice.

Background: Increased mammographic breast density (MBD) is known to be associated with an increased risk of developing breast cancer.Aims: In this study, we aimed to research the possible relations...

“Mammographic breast density” is significant biomarker which has strong association with the occurrence of breast cancer. Generally, in dense breast the tissues of breast cancer get hidden and hence the sensitivity of mammogram reduces significantly. It also increases the risk of false positive diagnosis in computer detection system. Development in the cutting-edge technology has the ability to improve the breast density measurement standards, and lead to early prediction of breast cancer by overcoming the restriction of subjective analysis of breast density measurement. In spite of development of different techniques for automatic breast density measurement, very few tools are practically available for breast cancer risk prediction. Basic objective behind this article is to provide an organized overview of selective research articles having specific application of machine learning approach for mammographic breast density measurement. We have selected research articles which follows the pipe-line of image processing with machine learning. Accuracy and computational complexity of selected methods are highlighted in order to decide the best possible future guidelines of research in this area. Hence the modification in preprocessing algorithms to remove pectoral muscle, refining of segmentation as well as feature extraction algorithms in terms of computational complexity and accuracy, selection of more accurate machine learning algorithm can make a positive impact on accuracy enhancement of breast density measurement.

Background Nipple-sparing mastectomy (NSM) is an increasingly popular alternative to more traditional mastectomy approaches. However, estimating the implant volume during direct-to-implant (DTI) reconstruction following NSM is difficult for surgeons with little-to-moderate experience. We aimed to provide a fast, easy to use, and accurate method to aid in the estimation of implant size for DTI reconstruction using the specimen weight and breast volume. Methods A retrospective analysis was performed using data from 145 NSM patients with specific implant types. Standard two-dimensional digital mammograms were obtained in 118 of the patients. Breast morphological factors (specimen weight, mammographic breast density and volume, and implant size and type) were recorded. Curve-fitting and linear regression models were used to develop formulas predicting the implant volume, and the prediction performance of the obtained formulas was evaluated using the prospective data set. Results Two formulas to estimate the implant size were obtained, one using the specimen weight and one using the breast volume. The coefficients of correlation (R2) in these formulas were over 0.98 and the root mean squared errors were approximately 13. Conclusions These implant volume estimate formulas benefit surgeons by providing a preoperative implant volume assessment in DTI reconstruction using the breast volume and an intraoperative assessment using the specimen weight. The implant size estimation formulas obtained in the present study may be applied in a majority of patients.

Background: Breast cancer is the most frequent malignant disease in women. Neoadjuvant chemotherapy of breast cancer leads to high clinical response rates of 70% to 90%. Breast density (BD) is one of the most important factor that affect pathological complete response. Low BD is associated with more likelihood of pCR. Similarly low MBD (

Mammographic breast density and various breast MRI features are imaging biomarkers that can predict a woman's future risk of breast cancer. While mammographic density (MD) has been established as an independent risk factor for the development of breast cancer, MD assessment methods need to be accurate and reproducible for widespread clinical use in stratifying patients based on their risk. In addition, a number of breast MRI biomarkers using contrast-enhanced and noncontrast-enhanced techniques are also being investigated as risk predictors. The validation and standardization of these breast MRI biomarkers will be necessary for population-based clinical implementation of patient risk stratification, as well. This review provides an update on MD assessment methods, breast MRI biomarkers, and their ability to predict breast cancer risk.

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

BACKGROUND AND OBJECTIVES Accurately assessing breast volume (BV) relative to the volume of breast tissue to be removed could help objectively determine the optimal surgical candidates for breast conserving surgery. The objective of this study was to determine the optimal mammography-based method of BV estimation. METHODS Mammography data was obtained for patients who underwent mastectomy for breast cancer from 2005 to 2015. This data was used to calculate BV using four previously published equations. Results were compared to mastectomy specimen volumes calculated from specimen weights and breast density. Five practitioners then independently assessed reproducibility and ease of use. RESULTS Complete mammographic measurements were available for 65 breasts from 45 patients. Median age was 58 years (range 19-82). Mammographic breast density scores were available for 62 breasts. Of the 65 mastectomies performed, 16 (36%) were simple mastectomies. The equation BV = 1/3πRcc Rmlo Hmlo most closely approximated actual breast specimen volumes (R = 0.89, P

Mammographic breast density is an established risk factor for breast cancer and significantly reduces the sensitivity of mammography, yet its use within breast cancer screening programs in Australia is limited. We provide a contemporary snapshot of the role of breast density measurement in screening for breast cancer and review the recent evidence for an increasing role of breast density measurement as: a predictor of breast cancer risk, a predictor of 'masking', and a biomarker to monitor effectiveness of intervention strategies for primary prevention or improved breast cancer outcomes.

Background: Bariatric surgery decreases breast cancer risk, but its impact on mammographic findings is not well understood. Obesity and breast density both increase breast cancer risk, but paradoxically are inversely related. We investigated how mammographic density changes after bariatric surgery, and whether or not that change is related to amount of weight loss. Methods: We reviewed records for 349 prospectively collected patients who underwent bariatric surgery between 2013-2015, and identified 45 women with pre- and post-operative screening mammograms within 1.5 years of surgery. We recorded body mass index (BMI), Breast Imaging-Reporting and Data System density, and calculated excess BMI loss. Data were analyzed in Stata 14.2. Results: Average age was 54 years, mean pre-operative BMI was 44 (range 36-72), and mean percentage excess BMI lost was 73% at 1.3 years. One third had a change in mammographic breast density, which increased 93% of the time (p Conclusions: The majority of women with a mammographic change had an increase in breast density, despite bariatric surgery being associated with reduced breast cancer risk. Interestingly, the amount of weight loss was not associated with change in breast density. These findings suggest the metabolic effects of bariatric surgery have an effect on breast cancer risk independent of BMI reduction. Future work will include studying mammographic changes associated with non-surgical weight loss. Citation Format: Hosseini A, Khoury AL, Carter J, Wong JM, Alvarado MD, Ewing C, Esserman LJ, Mukhtar RA. The impact of bariatric surgery on mammographic breast density [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P5-12-04.

PURPOSE: Previous studies suggest that coffee and caffeine intake may be associated with reduced breast cancer risk. We investigated the association of coffee and caffeine intake with mammographic breast density by woman’s menopausal status and, in postmenopausal women, by hormone therapy (HT). METHODS: This study included 4,130 cancer-free women within the Nurses’ Health Study and Nurses’ Health Study II cohorts. Percent breast density (PD) was measured from digitized film mammograms using a computer-assisted thresholding technique and square-root transformed for the analysis. Average cumulative coffee/caffeine consumption was calculated using data from all food frequency questionnaires preceding the mammogram date. Information regarding breast cancer risk factors was obtained from questionnaires closest to the mammogram date. We used generalized linear regression to quantify associations of regular, decaffeinated, and total coffee, and energy-adjusted caffeine intake with percent density. RESULTS: In multivariable analyses, decaffeinated coffee was positively associated with PD in premenopausal women (2+ cups/day: β=0.23, p-trend=0.03). In postmenopausal women, decaffeinated and total coffee were inversely associated with PD (decaffeinated 2+ cups/day: β=−0.24, p-trend=0.04; total 4+ cups/day: β=−0.16, p-trend=0.02). Interaction of decaffeinated coffee with menopausal status was significant (p-interaction