Abstract ES7-3:/AI

Postmenopausal hormone therapy (HT) increases breast density, an effect that is rapidly reversed upon treatment discontinuation. Increased breast density is recognised to be a powerful risk factor for breast cancer. It is therefore important to know whether HT-induced changes in mammographic breast density can provide information about an individual's future risk of breast cancer; or whether an absence of any change in breast density following HT should be viewed as reassuring. Mammographic breast density therefore needs to be examined in further detail in order to better assess the risks of breast cancer in postmenopausal women and further individualize prescribing practices.

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. 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. 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. 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. 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.

Purpose: Mammographic density is one of the strongest risk factors for breast cancer. However, very little is known about how other breast cancer risk factors may modify the association between breast density and breast cancer. We investigated if associations of breast density and breast cancer differ according to the level of other known breast cancer risk factors. Methods: This study included 1,044 incident breast cancer cases diagnosed within the Nurses' Health Study cohort and 1,794 matched controls. Percent breast density, absolute dense and non-dense areas were measured from digitized film images with computer-assisted methods. Information on breast cancer risk factors was obtained prospectively from biennial questionnaires completed before the date of the cancer diagnosis for cancer cases and their matched controls. We used multivariate logistic regression to describe the association between breast density measures and breast cancer risk. The risk estimates were presented as odds ratios (ORs) and their corresponding 95% Confidence Intervals (95% CIs). To assess if the association between breast density and breast cancer risk was significantly different according to the levels of known risk factors, we conducted statistical tests of interaction by using respective medians within each category of the variables that were continuous in nature (alcohol consumption, age at menarche, age at menopause BMI, physical activity, and density measures) to model interactions. To test interactions of parity/age at first child's birth with density, parity/age at first child's birth was treated as an ordinal variable. Interactions of parity and age at first child's birth were also investigated separately in a secondary analysis restricted to parous women. Results: Percent breast density and absolute non-dense area were more strongly associated with breast cancer risk in current postmenopausal hormone users as compared to women with past or no hormone use history (percent density≥50% vs.10%: OR=5.34 [95% CI: 3.36-8.49], OR=2.69 [95% CI: 1.32-5.49], OR=2.57 [95% CI: 1.18-5.60], respectively, p for interaction=0.04; non-dense area 4th vs.1st quartile: OR=0.42 [95% CI: 0.30-0.61], OR=0.50 [95% CI: 0.28-0.92], OR=0.72 [95% CI: 0.41-1.29], respectively, p for interaction =0.04). Associations of percent density with breast cancer risk were stronger in parous women with younger age at first child's birth, as compared to women with age at first child's birth>25 years or nulliparous women (percent density ≥50% vs.10%: OR=4.11 [95% CI: 2.54-6.66], OR=3.29 [95% CI: 2.05-5.29], and OR=2.30 [95% CI: 0.61-8.73], respectively, p for interaction=0.02). However, when the analysis was restricted to parous women, there were no significant interactions of either parity or age at first child's birth alone with percent density, absolute dense and non-dense area on the risk of breast cancer (p for interaction>0.05). The associations of density with breast cancer risk did not differ by the levels of body mass index, age at menarche, age at menopause, alcohol consumption, a family history of breast cancer, a personal history of benign breast disease, and physical activity. Conclusions: Women with dense breasts who currently use postmenopausal hormone therapy are at a particularly high risk of breast cancer. Citation Format: Lusine Yaghjyan, Graham Colditz, Bernard Rosner, Rulla Tamimi. Mammographic breast density and breast cancer risk: Interactions of percent density, absolute dense and non-dense areas with breast cancer risk factors. [abstract]. In: Proceedings of the Thirteenth Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2014 Sep 27-Oct 1; New Orleans, LA. Philadelphia (PA): AACR; Can Prev Res 2015;8(10 Suppl): Abstract nr A51.

As of September 2024, federal legislation mandates that patients be informed of their breast density, a modest breast cancer risk factor and known cancer-masking agent. This binary metric, dense vs nondense, applies to 40% to 50% of women and is subjectively assessed with interreader variability, limiting its utility for guiding supplemental imaging. To compare the performance of a deep learning (DL) breast cancer risk model vs radiologist-assessed breast density in estimating future breast cancer and false-negative (FN) screening results. This retrospective cohort study included consecutive bilateral screening mammograms from women 30 years or older performed from January 1, 2009, to December 31, 2018, across 5 sites of a large academic health system, with follow-up through December 31, 2023, to allow ascertainment of 5-year breast cancer outcomes. A DL risk model applied to standard screening mammograms and radiologist-assessed breast density categorized using the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) Atlas. Primary outcomes were breast cancer diagnoses within 5 years of mammography and FN screening results, defined as BI-RADS 1 or 2 examinations followed by a cancer diagnosis within 1 year. DL risk scores were stratified as low (<1.7%), intermediate (1.7%-3.0%), or high (>3.0%). Cancer and FN rates were compared across DL risk groups and breast density categories. Discriminatory performance was assessed using the area under the receiver operating characteristic curve (AUROC) and compared using the DeLong test. Among 123 091 mammograms in 67 019 women (median [IQR] age, 58.0 [50.0-67.0] years), 50 974 (41.4%) were classified as dense. The DL model demonstrated significantly higher discriminatory accuracy than breast density in predicting future cancer (AUROC, 0.71 [95% CI, 0.70-0.72] vs 0.53 [95% CI, 0.52-0.54]; P < .001). FN rates increased across DL risk groups (2.1 per 1000 examinations in high-risk vs 1.0 and 0.6 in intermediate and low-risk groups, respectively). Women with dense breasts had higher FN rates than those with nondense breasts (1.7 vs 0.6 per 1000 examinations; P < .001). Adding breast density to the DL model did not improve performance. In this cohort study of screening mammography, a DL risk model outperformed breast density in estimating risk of future breast cancer and stratified FN screening results across risk groups. These findings support transitioning from density-based policy triggers toward more precise image-derived risk models to guide access to supplemental imaging.

INTRODUCTION: High Mammographic density is a breast cancer risk factor and it is present over one third of cases of breast cancer. The 17s-hydroxysteroid dehydrogenase (17s-HSD) enzyme has possible local effects on breast tissue by stimulating growth of breast epithelial cells and is associated with mammographic density in women in pre and perimenopause. After a review in the literature, we see the importance of the enzyme 17sHSD1 in the etiology of breast cancer by both local and peripheral steroidogenesis, which may increase breast density. OBJECTIVE: For this reason we studied the correlation between mammographic breast density and immunohistochemical expression of 17s hydroxysteroid desydrogenase type 1 (17sHSD1). Casuistic and METHODS: 48 cases were evaluated with mammographic image classified by BI-RADS® system in four categories. The pattern of density 1 and 2 (measure breast tissue until 50%) was classified like low density and the pattern 3 and 4 (measure breast tissue upper 50%) like high density. Expression of the enzymes were evaluated by tissue microarray (TMA) screened with anti-17sHSD1 monoclonal rabbit antibodies (LS-C49955, Lifespan) diluted 1/200. Specimens were assigned scores on a scale from zero to three according to intensity of staining and number of stained cells. Scores zero and one were considered negative results and scores two and three indicated positive enzyme expression. RESULTS: In relation to breast density, in absolute numbers and percentages, we found the following results in category 1, 17 patients (35.42%), category 2, 17 patients (35.42%), category 3, 6 patients (12 50%) and category 4, 8 patients (16.67%). The 17sHSD1 expression was positive in 18 (37.5%). Statistical analysis showed no significant correlation between the expression of 17sHSD1 (p = 0.00048) with breast density (p = 0.666). No significant correlation between the expression of 17sHSD1 with breast density was found to be unchanged even when the analysis of the proposed subgroups (BMI greater than or less than 25, aged 50 years or less and high and low mammographic density). CONCLUSION: The results of our studies showed that there was no positive association between mammographic breast density and the expression of the enzyme 17sHSD1.

Background- The metabolic syndrome (MetS) is prevalent among post-menopausal breast cancer patients and is associated with increased breast cancer risk. Mammographic breast density (BD) is also positively associated with increased breast cancer risk. The relationship between MetS and mammographic BD is unclear and requires further investigation. Aim- The aim of this study was to examine the relationship between the MetS and its component features with breast density. Methods - 112 post-menopausal women with breast cancer were recruited. Body composition (Body Mass Index (BMI), waist circumference (WC)) was measured objectively in participants prior to surgery. Metabolic profiles were measured in blood taken from participants prior to surgery. MetS was defined according to the International Diabetes Federation (IDF) criteria. BD was classified according to the Breast Imaging Reporting and Data System (BI-RADS). Participants were categorised into those with 'Dense' (BI-RADS score 3 or 4) or 'Less Dense' (BI-RADS score 1 or 2) breasts. Group means were compared using unpaired t-tests for parametric or Mann Whitney tests for non-parametric data. Categorical data was analysed using Fisher's exact test or Chi squared test as appropriate. Results - An inverse relationship was observed between measures of adiposity and BD. Participants with 'dense' (BI-RADS 3/4) breasts had significantly lower BMI(p=0.0034), waist circumference(p=0.0007), systolic blood pressure(p= 0.03), circulating insulin level(p=0.009) and glycated haemoglobin(p=0.008) than those with 'less dense' (BI-RADS 1/2) breasts. HDL was significantly higher in those with 'dense' versus those with 'less dense' breasts(p= 0.03). participants with 'less dense' breasts were significantly more likely to be insulin resistant (HOMA-IR ≥2) than those with 'dense breasts' (50.6% versus 20% respectively); p=0.01. Other components of the MetS (Serum triglycerides, glucose and diastolic blood pressure) did not differ significantly between participants with 'dense' and 'less dense' breasts. No differences in overall survival were observed between participants with 'Dense' versus those with 'Less Dense' breasts (P=0.93). Conclusion - Although both MetS and BD are positively associated with breast cancer risk; it is unlikely that the MetS is related to an increase in breast cancer risk through a mechanism involving BD. Further work on this study is currently underway and will involve adjusting for potential confounders including age and BMI as well as examining the relationship between MetS and BD in pre-menopausal breast cancer patients. Citation Format: Shokuhi P, McGarrigle SA, Sullivan CJ, Boyle T, Al-azawi D, O'Keeffe S, Kennedy J, Connolly EM. Breast density, metabolic syndrome and body composition in breast cancer [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 P1-02-07.

Background: Mammographic density (MD), a strong marker of breast cancer risk, is influenced by genetic, environmental, and hormonal factors. Cadmium, a persistent and widespread environmental pollutant, has been associated with risk of breast cancer, and laboratory evidence suggests cadmium is a carcinogen in the breast. Urinary cadmium concentration is a measure of exposure to cadmium over a prior 20-30 years. Methods: In a cross-sectional study of MD and urinary cadmium concentration, percentage MD (MD%) and Breast Imaging-Reporting and Data Systems (BI-RADS) density category were determined from screening mammograms of 190 premenopausal women ages 40-45 years recruited from a breast cancer screening program at a large integrated healthcare system. Women completed a health questionnaire, and the cadmium content of spot urine samples was measured with inductively-coupled plasma mass spectrometry and corrected for urine creatinine. Multivariable linear regression and logistic regression were used to estimate the strength of association between urinary cadmium and mammographic breast density, adjusted for age, parity, smoking history, race, and body mass index. Results: Adjusted mean MD% among women in the upper tertile of creatinine-corrected urine cadmium was 4.6% higher (95% confidence interval (CI): −2.3 to 11.6%) than in women in the lowest cadmium tertile. Each twofold increase in urine cadmium was associated with higher odds of MD% in the upper tertile (odds ratio (OR): 1.29, 95% CI: 0.82-2.02) or a BIRADS category rating of “extremely dense” (OR: 1.75, 95% CI: 1.14-2.70). Conclusions: Exposure to cadmium may be associated with increased breast density in premenopausal women. These results support earlier findings that cadmium may increase risk of breast cancer. Citation Information: Cancer Prev Res 2010;3(12 Suppl):A3.

Background: Mammographic density (MD) of the breast and body mass index (BMI) are positively associated with the risk of breast cancer in postmenopausal women, but they are inversely associated with each other. We supposed that the reason for this paradox may be due to the heterogeneity of BMI. Therefore, we calculated the skeletal muscle mass index (SMI), and evaluated whether SMI was an independent predictor for MD. Methods: A cross-sectional study was performed in 143,456 women who underwent comprehensive examinations from 2012 to 2016. Mammographic density was assessed using Breast Imaging Reporting and Data System, and the breasts were classified as dense or non-dense. The association between SMI, anthropometric factors, and MD were estimated using logistic regression models after adjustment for potential confounders. Results: In all, 115,013 premenopausal women (80.2%) and 28,443 postmenopausal women (19.8%) were included in the analysis. In both pre and postmenopausal women, weight, BMI, SMI and waist circumference were associated with MD. After adjustment for confounding factors including BMI, the odds ratios (ORs) for MD with 95% confidence interval for the dense breasts was between the highest and lowest quartiles of SMI at 2.65 (2.52–2.79) for premenopausal women and at 2.39 (2.02–2.82) for postmenopausal women. Conclusions: SMI was related to MD independent of BMI, which could be due to the similar growth mechanism of the skeletal muscle and breast parenchymal tissue. The positive correlation between the muscularity and breast density might explain the reason for the paradoxical relationship between BMI, MD and breast cancer risk in previous studies. Table 1. Characteristics and anthropometric measurements of participants by menopausal status and mammographic densityVariablesAll subjects (n=143 456)Premenopausal womenPostmenopausal womenNon-dense breast (n=60 180)Dense breast (n=54 833)p valueNon-dense breast (n=25 570)Dense breast (n=2873)p valueAge (years)143 45639.3 ± 5.737.3 ± 5.2&amp;lt;0.00158.0 ± 7.550.7 ± 8.2&amp;lt;0.001Height (cm)143 456160.7 ± 5.1161.0 ± 5.0&amp;lt;0.001156.2 ± 5.3158.0 ± 5.2&amp;lt;0.001Weight (kg)143 45658.4 ± 8.853.9 ± 6.9&amp;lt;0.00157.9 ± 8.054.4 ± 7.6&amp;lt;0.001Body mass index (kg/m2)143 45622.6 ± 3.220.8 ± 2.5&amp;lt;0.00123.7 ± 3.121.8 ± 2.9&amp;lt;0.001Age at menarche (years)139 54513.8 ± 1.513.8 ± 1.5&amp;lt;0.00115.4 ± 1.814.8 ± 1.7&amp;lt;0.001Waist circumference143 45677.7 ± 8.273.0 ± 7.0&amp;lt;0.00181.5 ± 8.475.6 ± 8.2&amp;lt;0.001Skeletal mass index143 45637.2 ± 3.139.2 ± 3.0&amp;lt;0.00135.7 ± 3.238.1 ± 3.3&amp;lt;0.001Calcium level adjusted to albumin (mmol/L)140 2788.8 ± 0.38.8 ± 0.3&amp;lt;0.0019.0 ± 0.39.0 ± 0.3&amp;lt;0.001Parous135 92586.680.0&amp;lt;0.00197.793.3&amp;lt;0.001History of estrogen replacement therapy143 4561.11.10.1494.67.0&amp;lt;0.001Data are presented as mean ± standard deviation or %. Table 2. Multivariate analysis for the association of higher mammographic density with skeletal mass index after further adjustment for BMIPremenopausal womenPostmenopausal womenMultivariate (+BMI) ORa (95% CI)p valueMultivariate (+BMI) ORa (95% CI)p valueSkeletal mass indexQ1 (&amp;lt; 35.5)RefRefQ2 (35.5–37.8)1.26 (1.21 to 1.32)&amp;lt;0.0011.16 (1.01 to 1.33)0.034Q3 (37.8–40.1)1.63 (1.55 to 1.70)&amp;lt;0.0011.55 (1.33 to 1.80)&amp;lt;0.001Q4 (&amp;gt; 40.1)2.65 (2.52 to 2.79)&amp;lt;0.0012.39 (2.02 to 2.82)&amp;lt;0.001aORs and 95% confidence interval (95% CI) from the multivariate model including age, BMI and variables (age at menarche, parous, history of estrogen replacement therapy, calcium level adjusted to albumin. Citation Format: KwanHo Lee, HyeWon Bang, SeungHye Choi. Association between skeletal muscle mass and mammographic breast density [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P2-08-30.

Breast density is an independent risk factor for breast cancer, although variability exists in measurements. This study sought to evaluate the agreement between radiologists and automated breast density assessment software and assess the impact of breast density measures on breast cancer risk estimates using the Breast Cancer Surveillance Consortium (BCSC) model (v.2). A retrospective database search identified women who had undergone mammography between December 2021 and June 2022. The Breast Imaging Reporting and Data System (BI-RADS) breast composition index assigned by a radiologist (R) was recorded and analyzed using three commercially available software programs (S1, S2, and S3). The agreement rate and Cohen's kappa (κ) were used to evaluate inter-rater agreements concerning breast density measures. The 5-year risk of invasive breast cancer in women was calculated using the BCSC model (v.2) with breast density inputs from various density estimation methods. Absolute differences in risk between various density measurements were evaluated. Overall, 1,949 women (mean age, 53.2 years) were included. The inter-rater agreement between R, S1, and S2 was 75.0-75.6%, while that between S3 and the others was 60.2%-63.3%. Kappa was substantial between R, S1, and S2 (0.66-0.68), and moderate (0.49-0.50) between S3 and the others. S3 placed fewer women in mammographic density d (14.9%) than R, S1, and S2 (40.5%-44.0%). In BCSC risk assessment (v.2), S3 assessed fewer women with a high 5-year risk of invasive breast cancer than the other methods, resulting in an absolute difference of 0% between R, S1, and S2 in 75.0%-75.6% of cases, whereas the difference between S3 and the other methods occurs in 60.2%-63.3% of cases. Breast density assessment using various methods showed moderate-to-substantial agreement, potentially affecting risk assessments. Precise and consistent breast density measurements may lead to personalized and effective strategies for breast cancer prevention.

Digitized assessment of mammographic breast density – Effects of continuous combined hormone therapy, tibolone and black cohosh compared to placebo

Background: The number of individuals who identify as transgender and/or gender-diverse (TGD) is steadily increasing and the number of individuals seeking chest masculinization surgery, commonly called “top-surgery,” is also increasing. In persons assigned female at birth, top surgery removes most breast tissue to allow the chest to appear masculine. However, top surgery is not equivalent to oncologic risk-reducing mastectomies (RRM), which aim to remove all breast tissue to reduce future breast cancer (BC) risk. While top surgery has significant psychological and health benefits, its impact on future BC risk is unclear, especially for those individuals who are at high-risk for future BC development (defined as ≥17% by international consensus guidelines) and/or have a pathogenic germline mutation for BC. The aims of this study are to 1) determine the percentage of TGD persons assigned female at birth who are considering undergoing top surgery and are at elevated BC risk using validated risk models, 2) determine the percentage of TGD persons at elevated BC risk who choose to undergo RRM as part of top surgery, and 3) assess and compare self-perceived BC risk compared to calculated risk. Methods: A single-institution prospective single-arm pilot study is currently underway from March 2023 through January 2024. Individuals assigned female at birth, who are age ≥18 years, and considering undergoing top surgery, but have not yet undergone top surgery, are eligible to enroll (goal accrual N=35). Participants are being actively recruited form our institution’s Comprehensive LGBTQIA+ Inclusion Health Clinic and Plastic Surgery Clinic. Consented participants undergo a personalized BC risk assessment the validated IBIS and Gail (for those ≥35 years old) BC risk models. Those with an average lifetime BC risk (&amp;lt; 17%) are recommended to continue top surgery as planned/no intervention, participants with a moderate lifetime BC risk (17-30%) are counseled they may consider RRM as part of their top surgery operation given that sparse data exists in this space, while those with a high lifetime BC risk ( &amp;gt;30% or pathogenic germline variant) are counseled on the potential benefits of RRM. Participants with a family history suggestive of a hereditary cancer syndrome meet with a certified Genetics counselor for consideration of genetic testing, per NCCN guidelines. Results: We anticipate reporting 1) general demographic description of the cohort, 2) the percentage who have an elevated lifetime BC risk and/or a pathogenic genetic mutation, 3) the percentage of those with an elevated risk who choose to undergo RRM as part of top surgery, and 4) report the differences between self-perceived vs calculated lifetime BC risk. Conclusion: Our findings will inform TGD persons and surgeons on the utility of a personalized BC risk assessment prior to top surgery to assist in surgical decision-making, report the accuracy of self-perceived BC risk in TGD persons, and potentially change the standard pre-operative evaluation for TGD persons considering top surgery to include a formal BC risk assessment to ensure high-risk individuals have the option to undergo RRM to appropriately reduce their future BC risk. Citation Format: Chandler Cortina, Anna Purdy, Samantha Stachowiak, Kristen Klement, Sarah Sasor, Kate Krucoff, Andrew Petroll, Kevin Robertson, Annie Lakatos, Erin Doren. A Prospective Single-Arm Pilot Interventional Trial to Assess Individual Breast Cancer Risk Prior to Gender-Affirming Chest Masculinization Surgery [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-20-01.

47 Background: Mammographic breast density (BD) is known to affect breast imaging. MarginProbe is an RF Spectroscopy intraoperative margin assessment tool. We looked at the effect of mammographic breast density (BD) on the performance of the device. Mammographic density was assigned by Breast Imaging-Reporting and Data System (BI-RADS) classification prior to breast conserving surgery with the MarginProbe. Methods: For the current study we analyzed MarginProbe device arm data from the MarginProbe Pivotal Trial for which BD data was available. Clear margins of the main specimen, which is where the MarginProbe was applied, were defined as no tumor on ink. Clinical benefit was defined as clearing positive main specimen margins or cancer in the shaving. Analysis was performed based on logistic and linear regression, with univariate analysis. Results: The dataset for the analysis included 196 patients for which preoperative BD data was available. With higher BD, there was an increase in the rate of main lumpectomy specimen with positive margins (OR 1.65 per change in density category, p=0.025). The rate of shavings with clinical benefit showed an upward trend, with increasing BD. The number of shavings per patient increased with BD, however the overall volume of shavings did not increase. There was an increase in the per patient relative clinical benefit as BD increased (OR 1.6 per change in density category, p=0.024). Conclusions: With higher breast density patients and surgeons face an increase in risk for main specimen positive margins. With the increased BD and main specimen positivity more shavings were removed, but shavings volume did not increase as average shaving volume decreased with higher BD. Higher level of patient clinical benefit was observed with higher BD.

This study examined whether breast density is associated with risk of breast cancer in women age ≥65 years undergoing screening mammography in community practice. Methods: We used prospective cohort data between 1996 and 2012 from the Breast Cancer Surveillance Consortium (BCSC). We calculated separate cumulative incidence models for breast cancer incidence according to Breast Imaging Reporting and Data System (BI-RADS) breast density for women ages 65–74 and ages ≥75. Multivariable Cox proportional hazards regression models were fitted to determine the risk of invasive breast cancer adjusted for BCSC registry, race/ethnicity, BMI, hormone therapy use and benign breast disease. Results: Among the 403,268 women included in the study, approximately 40% were ages ≥75. The annual incidence rate of invasive breast cancer increased with increasing breast density among women ages 65–74 [BI- RADS fatty breasts: 2.2% (95% CI, 2.1%–2.4%) vs. heterogeneously or extremely dense breasts: 4.7% (95% CI, 4.6%–4.9%)] and women ages 75+ [BI-RADS fatty breasts: 2.3% (95% CI, 2.1%–2.5%) vs. heterogeneously or extremely dense: 4.3% (95% CI, 4.1%–4.5%)]. Women with BI-RADS fatty breasts had a decreased risk of breast cancer among women ages 65–74 [HR: 0.66 (95% CI: 0.58%–0.78%) and women ages ≥75 [HR: 0.73 (95% CI: 0.62%–0.87%). Women with BI-RADS heterogeneously or extremely dense breasts were found to have increased risk of breast cancer among women ages 65–74 [HR: 1.39 (95% CI: 1.28%–1.51%)] and women ages ≥75 [HR: 1.23 (95% CI: 1.10%–1.37%)]. Conclusions: Older women with higher BI-RADS density had a significantly increased risk of breast cancer. These findings add further evidence that breast density continues to be associated with an increased risk of breast cancer, even among women age ≥75 years.

Towards consensus on managing high mammographic density in population breast screening?

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. 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. In multivariable analyses, adolescent caffeine intake was not associated with any of the density phenotypes (caffeine 4th vs. 1st 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. Our findings do not support the hypothesis that adolescent caffeine intake is associated with premenopausal mammographic breast density.