Changes In Breast Density Research Articles

Breast and endometrial safety of micronised progesterone versus norethisterone acetate in menopausal hormone therapy (PROBES): study protocol of a double-blind randomised controlled trial

IntroductionData suggest that micronised progesterone (mP) in menopausal hormone therapy is safer for the breast than synthetic progestins, while protection of the endometrium appears to be less effective. However, comparative...

Breast Density Status Changes: Frequency, Sequence, and Practice Implications.

Changes in a patient's reported breast density status (dense vs nondense) trigger modifications in their cancer risk profile and supplemental screening recommendations. This study tracked the frequency and longitudinal sequence of breast density status changes among patients who received serial mammograms. This IRB-approved, HIPAA-compliant retrospective cohort study tracked breast density changes among patients who received at least 2 mammograms over an 8-year study period. BI-RADS density assessment categories A through D, visually determined at the time of screening, were abstracted from electronic medical records and dichotomized into either nondense (categories A or B) or dense (categories C or D) status. A sequence analysis of longitudinal changes in density status was performed using Microsoft SQL. A total of 58 895 patients underwent 231 997 screening mammograms. Most patients maintained the same BI-RADS density category A through D (87.35% [51 444/58 895]) and density status (93.35% [54 978/58 859]) throughout the study period. Among patients whose density status changed, the majority (97% [3800/3917]) had either scattered or heterogeneously dense tissue, and over half (57% [2235/3917]) alternated between dense and nondense status multiple times. Our results suggest that many cases of density status change may be attributable to intra- and interradiologist variability rather than to true underlying changes in density. These results lend support to consideration of automated density assessment because breast density status changes can significantly impact cancer risk assessment and supplemental screening recommendations.

Development and validation of the surmising model for volumetric breast density using X-ray exposure conditions in digital mammography.

The use of breast density as a biomarker for breast cancer treatment has not been well established owing to the difficulty in measuring time-series changes in breast density. In this study, we developed a surmising model for breast density using prior mammograms through a multiple regression analysis, enabling a time series analysis of breast density. We acquired 1320 mediolateral oblique view mammograms to construct the surmising model using multiple regression analysis. The dependent variable was the breast density of the mammary gland region segmented by certified radiological technologists, and independent variables included the compressed breast thickness (CBT), exposure current times exposure second (mAs), tube voltage (kV), and patients' age. The coefficient of determination of the surmising model was 0.868. After applying the model, the correlation coefficients of the three groups based on the CBT (thin group, 18-36mm; standard group, 38-46mm; and thick group, 48-78mm) were 0.913, 0.945, and 0.867, respectively, suggesting that the thick breast group had a significantly low correlation coefficient (p = 0.00231). In conclusion, breast density can be accurately surmised using the CBT, mAs, tube voltage, and patients' age, even in the absence of a mammogram image.

Comparison of the Ultrasonography Features of the Breast in Women with Fibroadenoma and Those with Other Breast Lumps.

Fibroadenoma (FA) is documented as the most common benign breast disease typically presenting as a lump. A wide variety of other diseases including breast cancer can similarly present as lumps hence the need for further differentiation. Ultrasonography plays a vital role in the evaluation and treatment of breast lumps with histological analysis as the gold standard. This study compared the physical and sonographic features of the breast in women with FA and women with breast lumps due to other diseases. This is a single-centre comparative study. Clinical and sonographic breast evaluations of the recruited patients with lumps were done and reported using the American College of Radiology Breast Imaging Reporting and Data System score. The lumps were biopsied, and histological diagnosis was documented. Clinical and imaging features of the breasts of women with FA were then compared with those of women with lumps from other breast diseases, and collated data were analysed using SPSS Statistical version 23.0. Data from 118 subjects (59 in each group) were used for this study. There was a significant difference in the physical and sonographic appearance of FA concerning the patient's age, parity, change in lesion size, perilesional architecture, echogenicity, borders, capsule and background breast density. No FA was found in women with less dense breasts. The sonographic features of breasts showed some differences from the corresponding features of FA and other breast lesions. This has the potential to increase the efficiency of pre-operative diagnosis of FA and could be further applied in developing diagnostic criteria for FA in our environment.

Abstract TC-01: Baby-TAM Impact on disease progression

Abstract Professor Hall will have reviewed his studies on the impact of lower doses of tamoxifen on changes in breast density, an important risk factor. Here I will review the work of Professor Decensi’s team on the ability of 5mg/d of tamoxifen (baby-Tam) on the progression rate of DCIS (69%) and other intraepithelial lesions (11% LCIS, 20% atypical ductal hyperplasia). This trial involved 500 pre- and postmenopausal women randomized equally between baby-Tam and placebo with a 9.7 year median follow up. All breast cancer progression (invasive (77%) and DCIS (23%)) was reduced by 42% (25 vs 41 events, P = 0.03) and contralateral tumours by 64% (6 vs 16 events, P = 0.025). No significant increase was seen in any side effect (Table). I will also briefly discuss their new award for a project to compare baby Tam to baby exemestane in a randomized trial in a similar population as above, initially for side effects, but also subsequently for efficacy. Professor Hall will have reviewed his studies on the impact of lower doses of tamoxifen on changes in breast density, an important risk factor. Here I will review the work of Professor Decensi’s team on the ability of 5mg/d of tamoxifen (baby-Tam) on the progression rate of DCIS (69%) and other intraepithelial lesions (11% LCIS, 20% atypical ductal hyperplasia). This trial involved 500 pre- and postmenopausal women randomized equally between baby-Tam and placebo with a 9.7 year median follow up. All breast cancer progression (invasive (77%) and DCIS (23%)) was reduced by 42% (25 vs 41 events, P = 0.03) and contralateral tumours by 64% (6 vs 16 events, P = 0.025). No significant increase was seen in any side effect (Table). I will also briefly discuss their new award for a project to compare baby Tam to baby exemestane in a randomized trial in a similar population as above, initially for side effects, but also subsequently for efficacy. Citation Format: J. Cuzick, P. Hall. Baby-TAM Impact on disease progression [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 TC-01.

Temporal changes in mammographic breast density and breast cancer risk among women with benign breast disease

IntroductionBenign breast disease (BBD) and high mammographic breast density (MBD) are prevalent and independent risk factors for invasive breast cancer. It has been suggested that temporal changes in MBD may impact future invasive breast cancer risk, but this has not been studied among women with BBD.MethodsWe undertook a nested case–control study within a cohort of 15,395 women with BBD in Kaiser Permanente Northwest (KPNW; 1970–2012, followed through mid-2015). Cases (n = 261) developed invasive breast cancer > 1 year after BBD diagnosis, whereas controls (n = 249) did not have breast cancer by the case diagnosis date. Cases and controls were individually matched on BBD diagnosis age and plan membership duration. Standardized %MBD change (per 2 years), categorized as stable/any increase (≥ 0%), minimal decrease of less than 5% or a decrease greater than or equal to 5%, was determined from baseline and follow-up mammograms. Associations between MBD change and breast cancer risk were examined using adjusted unconditional logistic regression.ResultsOverall, 64.5% (n = 329) of BBD patients had non-proliferative and 35.5% (n = 181) had proliferative disease with/without atypia. Women with an MBD decrease (≤ − 5%) were less likely to develop breast cancer (Odds Ratio (OR) 0.64; 95% Confidence Interval (CI) 0.38, 1.07) compared with women with minimal decreases. Associations were stronger among women ≥ 50 years at BBD diagnosis (OR 0.48; 95% CI 0.25, 0.92) and with proliferative BBD (OR 0.32; 95% CI 0.11, 0.99).DiscussionAssessment of temporal MBD changes may inform risk monitoring among women with BBD, and strategies to actively reduce MBD may help decrease future breast cancer risk.

Abstract 4867: Longitudinal latent class analysis to further understand trajectory of density over time and risk of breast cancer

Abstract Background: It is clinically important to refine strategies to manage women with dense breasts as they represent approximately 50% of all women screened. Change in breast density is related to risk of breast cancer.1 We also must understand this change in density over time in women with dense and non-dense breasts. While population level data have been reported to estimate decrease in density with age, this has largely used digitized film images.2 Expanding use of digital mammography and repeated screening generates a large library of images for each woman, offering the potential to use more of the image data. Methods: We use longitudinal latent class analysis to cluster women and estimate their change in density over time. To evaluate the groups of women with different patterns of change in density we fit latent class models to our previously published data.1 This includes 289 pathology confirmed cases of breast cancer and 658 controls among women followed from November 3, 2008 to October 31, 2020. Results: Among women with dense breasts, there are 107 who developed breast cancer during follow-up and 267 control women who remained free from breast cancer. In the women without dense breasts, 173 cases and 383 controls were evaluated and showed similar age and BMI within the controls as seen in women with dense breasts. Prevalence of family history and history of benign breast biopsy did not differ between the two groups. We observe this latent class and trajectory of density phenomenal separately for dense and non-dense groups in our data. We show that the data define 2 classes, those who have decrease in density over time and those who have increase over time. The odds ratio (OR) for cancer comparing decline vs. incline is 4.9 in women with dense breasts (BI-RADS C, D) and 6.17 on women with non-dense breasts (BI-RADS A, B). Discussion: To refine our understanding of patterns of change in breast density over time in relation to breast cancer risk we fit latent class models. Longitudinal change matters as reflected in the 2-class model. This is independent of starting density and confirms that a one-time measure of density is not enough to define level of risk for subsequent breast cancer. Further work is needed to better define the drivers and inhibitors of decline in breast density over time given this change over time as women age is a universal phenomenon.2 1. Jiang S, Bennett DL, Rosner BA, Colditz GA. Longitudinal Analysis of Change in Mammographic Density in Each Breast and Its Association With Breast Cancer Risk. JAMA Oncol 2023;9(6):808-814. (In English). DOI: 10.1001/jamaoncol.2023.0434. 2. Burton A, Maskarinec G, Perez-Gomez B, et al. Mammographic density and ageing: A collaborative pooled analysis of cross-sectional data from 22 countries worldwide. PLoS Med 2017;14(6):e1002335. DOI: 10.1371/journal.pmed.1002335. Citation Format: Graham A. Colditz, Debbie L. Bennett, Shu Jiang. Longitudinal latent class analysis to further understand trajectory of density over time and risk of breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4867.

Factors Associated with Longitudinal Changes in Mammographic Density in a Multiethnic Breast Screening Cohort of Postmenopausal Women.

Breast density is an important risk factor for breast cancer and is known to be associated with characteristics such as age, race, and hormone levels; however, it is unclear what factors contribute to changes in breast density in postmenopausal women over time. Understanding factors associated with density changes may enable a better understanding of breast cancer risk and facilitate potential strategies for prevention. This study investigated potential associations between personal factors and changes in mammographic density in a cohort of 3,392 postmenopausal women with no personal history of breast cancer between 2011 and 2017. Self-reported information on demographics, breast and reproductive history, and lifestyle factors, including body mass index (BMI), alcohol intake, smoking, and physical activity, was collected by an electronic intake form, and breast imaging reporting and database system (BI-RADS) mammographic density scores were obtained from electronic medical records. Factors associated with a longitudinal increase or decrease in mammographic density were identified using Fisher's exact test and multivariate conditional logistic regression. 7.9% of women exhibited a longitudinal decrease in mammographic density, 6.7% exhibited an increase, and 85.4% exhibited no change. Longitudinal changes in mammographic density were correlated with age, race/ethnicity, and age at menopause in the univariate analysis. In the multivariate analysis, Asian women were more likely to exhibit a longitudinal increase in mammographic density and less likely to exhibit a decrease compared to White women. On the other hand, obese women were less likely to exhibit an increase and more likely to exhibit a decrease compared to normal weight women. Women who underwent menopause at age 55 years or older were less likely to exhibit a decrease in mammographic density compared to women who underwent menopause at a younger age. Besides obesity, lifestyle factors (alcohol intake, smoking, and physical activity) were not associated with longitudinal changes in mammographic density. The associations we observed between Asian race/obesity and longitudinal changes in BI-RADS density in postmenopausal women are paradoxical in that breast cancer risk is lower in Asian women and higher in obese women. However, the association between later age at menopause and a decreased likelihood of decreasing in BI-RADS density over time is consistent with later age at menopause being a risk factor for breast cancer and suggests a potential relationship between greater cumulative lifetime estrogen exposure and relative stability in breast density after menopause. Our findings support the complexity of the relationships between breast density, BMI, hormone exposure, and breast cancer risk.

Influence of mammographic density and compressed breast thickness on true mammographic sensitivity: a cohort study

Understanding the detectability of breast cancer using mammography is important when considering nation-wide screening programmes. Although the role of imaging settings on image quality has been studied extensively, their role in detectability of cancer at a population level is less well studied. We wish to quantify the association between mammographic screening sensitivity and various imaging parameters. Using a novel approach applied to a population-based breast cancer screening cohort, we specifically focus on sensitivity as defined in the classical diagnostic testing literature, as opposed to the screen-detected cancer rate, which is often used as a measure of sensitivity for monitoring and evaluating breast cancer screening. We use a natural history approach to model the presence and size of latent tumors at risk of detection at mammography screening, and the screening sensitivity is modeled as a logistic function of tumor size. With this approach we study the influence of compressed breast thickness, x-ray exposure, and compression pressure, in addition to (percent) breast density, on the screening test sensitivity. When adjusting for all screening parameters in addition to latent tumor size, we find that percent breast density and compressed breast thickness are statistically significant factors for the detectability of breast cancer. A change in breast density from 6.6 to 33.5% (the inter-quartile range) reduced the odds of detection by 61% (95% CI 48–71). Similarly, a change in compressed breast thickness from 46 to 66 mm reduced the odds by 42% (95% CI 21–57). The true sensitivity of mammography, defined as the probability that an examination leads to a positive result if a tumour is present in the breast, is associated with compressed breast thickness after accounting for mammographic density and tumour size. This can be used to guide studies of setups aimed at improving lesion detection. Compressed breast thickness—in addition to breast density—should be considered when assigning complementary screening modalities and personalized screening intervals.

Effects of bariatric surgery on breast density in adult obese women: systematic review and meta-analysis.

Bariatric surgery is one of the most effective methods for treating obesity. It can effectively reduce body weight and reduce the incidence of obesity-related breast cancer. However, there are different conclusions about how bariatric surgery changes breast density. The purpose of this study was to clarify the changes in breast density from before to after bariatric surgery. The relevant literature was searched through PubMed and Embase to screen for studies. Meta-analysis was used to clarify the changes in breast density from before to after bariatric surgery. A total of seven studies were included in this systematic review and meta-analysis, including a total of 535 people. The average body mass index decreased from 45.3 kg/m2 before surgery to 34.4 kg/m2 after surgery. By the Breast Imaging Reporting and Data System score, the proportion of grade A breast density from before to after bariatric surgery decreased by 3.83% (183 vs. 176), grade B (248 vs. 263) increased by 6.05%, grade C (94 vs. 89) decreased by 5.32%, and grade D (1 vs. 4) increased by 300%. There was no significant change in breast density from before to after bariatric surgery (OR=1.27, 95% confidence interval (CI) [0.74, 2.20], P=0.38). By the Volpara density grade score, postoperative volumetric breast density increased (standardized mean difference = -0.68, 95% CI [-1.08, -0.27], P = 0.001). Breast density increased significantly after bariatric surgery, but this depended on the method of detecting breast density. Further randomized controlled studies are needed to validate our conclusions.

Longitudinal Analysis of Change in Mammographic Density in Each Breast and Its Association With Breast Cancer Risk

Although breast density is an established risk factor for breast cancer, longitudinal changes in breast density have not been extensively studied to determine whether this factor is associated with breast cancer risk. To prospectively evaluate the association between change in mammographic density in each breast over time and risk of subsequent breast cancer. This nested case-control cohort study was sampled from the Joanne Knight Breast Health Cohort of 10 481 women free from cancer at entry and observed from November 3, 2008, to October 31, 2020, with routine screening mammograms every 1 to 2 years, providing a measure of breast density. Breast cancer screening was provided for a diverse population of women in the St Louis region. A total of 289 case patients with pathology-confirmed breast cancer were identified, and approximately 2 control participants were sampled for each case according to age at entry and year of enrollment, yielding 658 controls with a total number of 8710 craniocaudal-view mammograms for analysis. Exposures included screening mammograms with volumetric percentage of density, change in volumetric breast density over time, and breast biopsy pathology-confirmed cancer. Breast cancer risk factors were collected via questionnaire at enrollment. Longitudinal changes over time in each woman's volumetric breast density by case and control status. The mean (SD) age of the 947 participants was 56.67 (8.71) years at entry; 141 were Black (14.9%), 763 were White (80.6%), 20 were of other race or ethnicity (2.1%), and 23 did not report this information (2.4%). The mean (SD) interval was 2.0 (1.5) years from last mammogram to date of subsequent breast cancer diagnosis (10th percentile, 1.0 year; 90th percentile, 3.9 years). Breast density decreased over time in both cases and controls. However, there was a significantly slower decrease in rate of decline in density in the breast that developed breast cancer compared with the decline in controls (estimate = 0.027; 95% CI, 0.001-0.053; P = .04). This study found that the rate of change in breast density was associated with the risk of subsequent breast cancer. Incorporation of longitudinal changes into existing models could optimize risk stratification and guide more personalized risk management.

Abstract 776: Temporal changes in mammographic breast density and breast cancer risk among women with benign breast disease

Abstract Introduction: Benign breast disease (BBD) is associated with increased breast cancer risk, and the magnitude of this risk is characterized by the severity of the histological classification of the biopsy lesion. High mammographic density (MBD) is an independent risk factor for invasive breast cancer. Given that MBD is altered by endogenous and exogenous factors, its temporal changes may impact future breast cancer risk, but this is poorly studied, particularly among high risk BBD patients. In this study, we examined whether MBD changes following a BBD diagnosis were associated with subsequent breast cancer risk. Methods: We conducted a case-control study within a cohort of 15,395 women aged 18-86 years who were members of the Kaiser Permanente Northwest Region health care system, had a diagnosis of BBD between 1970 and 2012 and were followed through mid-2015. Cases (n=261) were BBD patients who developed invasive breast cancer at least one year after the index BBD diagnosis. Controls were matched (1:1), on age at BBD diagnosis and health plan membership duration and did not develop breast cancer during the follow-up duration. Standardized change in percent MBD per 2 years, categorized as an increase (≥0%), stable/minimal decrease (-5%< to <0) or decrease (≤-5%), was determined from baseline (pre-biopsy) and follow-up (prior to breast cancer diagnosis for cases or matched selection date for controls) mammograms, using computer-assisted software. Associations between MBD change and breast cancer risk overall and stratified by BBD diagnosis age and histology were determined using unconditional logistic regression adjusted for matching factors and other covariates. Results: At BBD diagnosis (median age (range)=54.6 years (32.4, 86.6)), 64.5% (n=329: n=151 cases and n=178 controls) of women had non-proliferative and 35.5% (n=181: n=110 cases and n=71 controls) had proliferative BBD with or without atypia. Compared to women with stable/minimal MBD decrease, those who experienced a decline ≥5% per 2 years were less likely to develop breast cancer (odds ratio [OR]: 0.64; 95% confidence interval [CI]: 0.38, 1.07). However, among women aged ≥50 years at BBD diagnosis, an MBD decrease ≥5% was significantly associated with reduced breast cancer risk (OR: 0.48; 95%CI: 0.25, 0.92), with the protective effect most apparent for those with proliferative (OR: 0.32; 95%CI: 0.11, 0.99) versus non-proliferative (OR: 0.70; 95%CI: 0.30, 1.64) BBD. Discussion: Temporal MBD declines were associated with reduced risk of subsequent breast cancer particularly among BBD patients aged ≥50 years and with proliferative BBD diagnoses. These findings suggest that monitoring MBD may be useful in determining risk and that strategies to actively reduce MBD may be helpful in reducing breast cancer risk among high-risk BBD patients. Funding: Dr. Rohan is supported in part by the Breast Cancer Research Foundation (BCRF-22-140). Citation Format: Maeve Mullooly, Shaoqi Fan, Ruth M. Pfeiffer, Erin Aiello Bowles, Máire A. Duggan, Roni T. Falk, Kathryn Richert-Boe, Terry Kimes, Jonine D. Figueroa, Thomas E. Rohan, Mustapha Abubakar, Gretchen L. Gierach. Temporal changes in mammographic breast density and breast cancer risk among women with benign breast disease [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 776.

Lifetime changes in body fatness and breast density in postmenopausal women: the FEDRA study

BackgroundHigh mammographic breast density (MBD) is an established risk factor for breast cancer (BC). Body fatness conveys an increased BC risk in postmenopause but is associated with less dense breasts. Here, we studied the relationship between body fatness and breast composition within the FEDRA (Florence-EPIC Digital mammographic density and breast cancer Risk Assessment) longitudinal study.MethodsRepeated anthropometric data and MBD parameters (obtained through an automated software on BC screening digital mammograms) were available for all participants, as well as information on other BC risk factors. Multivariate linear regression and functional data analysis were used to longitudinally evaluate the association of body fatness, and changes thereof over time, with dense (DV) and non-dense (NDV) breast volumes and volumetric percent density (VPD).ResultsA total of 5,262 women were included, with anthropometric data available at 20 and 40 years of age, at EPIC baseline (mean 49.0 years), and an average of 9.4 years thereafter. The mean number of mammograms per woman was 3.3 (SD 1.6). Body fatness (and increases thereof) at any age was positively associated with DV and NDV (the association being consistently stronger for the latter), and inversely associated with VPD. For instance, an increase by 1 kg/year between the age of 40 years and EPIC baseline was significantly associated with 1.97% higher DV, 8.85% higher NDV, and 5.82% lower VPD.ConclusionBody fatness and its increase from young adulthood until midlife are inversely associated with volumetric percent density, but positively associated with dense and non-dense breast volumes in postmenopausal women.

Short-term changes in ultrasound tomography measures of breast density and treatment-associated endocrine symptoms after tamoxifen therapy

Although breast density decline with tamoxifen therapy is associated with greater therapeutic benefit, limited data suggest that endocrine symptoms may also be associated with improved breast cancer outcomes. However, it is unknown whether endocrine symptoms are associated with reductions in breast density after tamoxifen initiation. We evaluated treatment-associated endocrine symptoms and breast density change among 74 women prescribed tamoxifen in a 12-month longitudinal study. Treatment-associated endocrine symptoms and sound speed measures of breast density, assessed via novel whole breast ultrasound tomography (m/s), were ascertained before tamoxifen (T0) and at 1–3 (T1), 4–6 (T2), and 12 months (T3) after initiation. CYP2D6 status was genotyped, and tamoxifen metabolites were measured at T3. Using multivariable linear regression, we estimated mean change in breast density by treatment-associated endocrine symptoms adjusting for age, race, menopausal status, body mass index, and baseline density. Significant breast density declines were observed in women with treatment-associated endocrine symptoms (mean change (95% confidence interval) at T1:−0.26 m/s (−2.17,1.65); T2:−2.12 m/s (−4.02,−0.22); T3:−3.73 m/s (−5.82,−1.63); p-trend = 0.004), but not among women without symptoms (p-trend = 0.18) (p-interaction = 0.02). Similar declines were observed with increasing symptom frequency (p-trends for no symptoms = 0.91; low/moderate symptoms = 0.03; high symptoms = 0.004). Density declines remained among women with detectable tamoxifen metabolites or intermediate/efficient CYP2D6 metabolizer status. Emergent/worsening endocrine symptoms are associated with significant, early declines in breast density after tamoxifen initiation. Further studies are needed to assess whether these observations predict clinical outcomes. If confirmed, endocrine symptoms may be a proxy for tamoxifen response and useful for patients and providers to encourage adherence.

Association of Breast Cancer Family History With Breast Density Over Time in Korean Women

Evidence suggests that women with a family history of breast cancer (FHBC) in first-degree relatives have a higher level of breast density; however, studies of premenopausal women remain limited. To investigate the association between FHBC and mammographic breast density and breast density changes among premenopausal women. This retrospective cohort study used population-based data obtained from the National Health Insurance Service-National Health Information Database of Korea. We included premenopausal women aged 40 to 55 years who underwent mammography for breast cancer screening once between January 1, 2015, and December 31, 2016 (n = 1 174 214), and women who underwent mammography twice (first in 2015-2016 and again between January 1, 2017 and December 31, 2018) (n = 838 855). Family history of breast cancer was assessed using a self-reported questionnaire, which included information on FHBC in the mother and/or sister. Breast density, based on the Breast Imaging Reporting and Data System, was categorized as dense (heterogeneously or extremely dense) and nondense (almost entirely fat or scattered fibroglandular areas). Multivariate logistic regression was used to assess the association among FHBC, breast density, and changes in breast density from the first to second screening. Data analysis was performed from June 1 to September 31, 2022. Of the 1 174 214 premenopausal women, 34 003 (2.4%; mean [SD] age, 46.3 [3.2] years) reported having FHBC among their first-degree relatives, and 1 140 211 (97.1%; mean [SD] age, 46.3 [3.2] years) reported no FHBC. Odds of having dense breasts was 22% higher (adjusted odds ratio [aOR], 1.22; 95% CI, 1.19-1.26) in women with FHBC than in women without FHBC, and the association varied by affected relatives: mother alone (aOR, 1.15; 95% CI, 1.10-1.21), sister alone (aOR, 1.26; 95% CI, 1.22-1.31), and both mother and sister (aOR, 1.64; 95% CI, 1.20-2.25). Among women with fatty breasts at baseline, the odds of developing dense breasts was higher in women with FHBC than in those without FHBC (aOR, 1.19; 95% CI, 1.11-1.26), whereas among women with dense breasts, higher odds of having persistently dense breasts were observed in women with FHBC (aOR, 1.11; 95% CI, 1.05-1.16) than in those without FHBC. In this cohort study of premenopausal Korean women, FHBC was positively associated with an increased incidence of having increased or persistently dense breasts over time. These findings suggest the need for a tailored breast cancer risk assessment for women with FHBC.

Abstract PD12-06: PD12-06 Longitudinal analysis of breast density change assessed by digital mammogram is associated with breast cancer

Abstract Mammographic breast density is a well-established and strong risk factor for breast cancer. Widespread use of digital mammography has opened new potential for assessment of density changes over time. The underlying premise is that changes in breast tissue due to evolving structures that support cancer development should translate to quantifiable differences between the two breasts over time. To address this hypothesis, we draw on extensive digital mammography data and bring repeated measures over up to 10 years to evaluate the association between change in mammographic breast density and risk of breast cancer. Women were recruited from November 2008 to April 2012 through the mammography service at the Joanne Knight Breast Health Center at Washington University in St Louis. Baseline questionnaire risk factors and screening mammograms were collected from 12,153 women. Of these, 1,672 were excluded for prior history of any cancer (except non-melanoma skin) or diagnosis of breast cancer within 6 months of registration for the study, for a total of 10,481 women. Follow-up is through linking to electronic health records, tumor registry and death register. Routine screening mammograms are collected every 1 to 2 years. Follow-up of cohort participants through December 2020 was: 78% seen in 2019 or 2020; a further 4.4% seen most recently in 2018 and a further 2.4% in 2017 giving over 80% active follow-up for women seen within the last 36 months. The median number of mammograms is 5 (min = 1, max = 10; sd = 2.43). The average person-years of follow-up through most recent contact is 9.2 person-years. We have excluded women who are diagnosed within the first 6 month of baseline mammogram date in all analyses, leaving 259 cases and 695 controls with a total number of 8,966 crandiocaudal (CC) view mammograms for analysis. For these 954 women, the mean number of years between mammograms is 1.3 (10th percentile: 1.0, 90th percentile: 2.0). For the cases, the mean number of years from last mammogram date to diagnosis date was 2.0 years (10th percentile: 1.0, 90th percentile: 3.9) after excluding mammograms that are within 6 months of diagnosis. The percentage volumetric density (MD) within each digital CC-view mammogram is estimated with an automated pixel-thresholding algorithm ADAPT implemented within the Division of Public Health at WashU. The skin around the breast is automatically removed using a boundary detection algorithm prior to estimating the dense areas. The percent density (MD) is then estimated using the dense area divided by the total breast area which normalizes the difference in breast size across women. The correlation between average MD generated from our automated algorithm with Volpara is 0.82. Initial analyses use linear mixed effects with average density between breasts (fitted with R package lmer). We performed test of assumptions for all linear mixed effects models, including the normality of residuals, linearity, and homogeneity of residual variance. Assessing the averaging density between 2 breasts controlling for age, BMI, histology confirmed benign breast disease, family history, parity, and alcohol, MD decreases significantly over follow-up (time in years) (P < 0.01). At baseline, postmenopausal women had lower density than premenopausal women after controlling for the same set of risk factors. The average MD over all time points was significantly different for the case vs. control women (P < 0.01). For overweight women, the trajectory of MD over time was significantly different for the case vs. control women (P < 0.01). Drawing on over 10 years of follow-up we observe, for the first time, a dynamic effect for breast density such that divergence in density over time is related to risk for breast cancer. Citation Format: Graham A. Colditz, Shu Jiang. PD12-06 Longitudinal analysis of breast density change assessed by digital mammogram is associated with breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD12-06.

Changes in mammographic density and risk of breast cancer among a diverse cohort of women undergoing mammography screening.

Mammographic density (MD) is a strong breast cancer risk factor. MD may change over time, with potential implications for breast cancer risk. Few studies have assessed associations between MD change and breast cancer in racially diverse populations. We investigated the relationships between MD and MD change over time and breast cancer risk in a large, diverse screening cohort. We retrospectively analyzed data from 8462 women who underwent ≥ 2 screening mammograms from Sept. 2010 to Jan. 2015 (N = 20,766 exams); 185 breast cancers were diagnosed 1-7years after screening. Breast percent density (PD) and dense area (DA) were estimated from raw digital mammograms (Hologic Inc.) using LIBRA (v1.0.4). For each MD measure, we modeled breast density change between two sequential visits as a function of demographic and risk covariates. We used Cox regression to examine whether varying degrees of breast density change were associated with breast cancer risk, accounting for multiple exams per woman. PD at any screen was significantly associated with breast cancer risk (hazard ratio (HR) for PD = 1.03 (95% CI [1.01, 1.05], p < 0.0005), but neither change in breast density nor more extreme than expected changes in breast density were associated with breast cancer risk. We found no evidence of differences in density change or breast cancer risk due to density change by race. Results using DA were essentially identical. Using a large racially diverse cohort, we found no evidence of association between short-term change in MD and risk of breast cancer, suggesting that short-term MD change is not a strong predictor for risk.

Age-related change in mammographic breast density of women without history of breast cancer over a 10-year retrospective study.

Women with higher breast density are at higher risk of developing breast cancer. Breast density is known to affect sensitivity to mammography and to decrease with age. However, the age change and associated factors involved are still unknown. This study aimed to investigate changes in breast density and the associated factors over a 10-year period. The study included 221 women who had undergone eight or more mammograms for 10 years (2011-2020), were between 25 and 65 years of age, and had no abnormalities as of 2011. Breast density on mammographic images was classified into four categories: fatty, scattered, heterogeneously dense, and extremely dense. Breast density was determined using an image classification program with a Microsoft Lobe's machine-learning model. The temporal changes in breast density over a 10-year period were classified into three categories: no change, decrease, and increase. An ordinal logistic analysis was performed with the three groups of temporal changes in breast density categories as the objective variable and the four items of breast density at the start, BMI, age, and changes in BMI as explanatory variables. As of 2011, the mean age of the 221 patients was 47±7.3 years, and breast density category 3 scattered was the most common (67.0%). The 10-year change in breast density was 64.7% unchanged, 25.3% decreased, and 10% increased. BMI was increased by 64.7% of women. Breast density decreased in 76.6% of the category at the start: extremely dense breast density at the start was correlated with body mass index (BMI). The results of the ordinal logistic analysis indicated that contributing factors to breast density classification were higher breast density at the start (odds ratio = 0.044; 95% CI [0.025-0.076]), higher BMI at the start (odds ratio = 0.76; 95% CI [0.70-0.83]), increased BMI (odds ratio = 0.57; 95% CI [0.36-0.92]), and age in the 40s at the start (odds ratio = 0.49; 95% CI [0.24-0.99]). No statistically significant differences were found for medical history. Breast density decreased in approximately 25% of women over a 10-year period. Women with decreased breast density tended to have higher breast density or higher BMI at the start. This effect was more pronounced among women in their 40s at the start. Women with these conditions may experience changes in breast density over time. The present study would be useful to consider effective screening mammography based on breast density.

Changes in Automated Mammographic Breast Density Can Predict Pathological Response After Neoadjuvant Chemotherapy in Breast Cancer.

Mammographic density is an independent risk factor for breast cancer that can change after neoadjuvant chemotherapy (NCT). This study aimed to evaluate percent changes in volumetric breast density (ΔVbd%) before and after NCT measured automatically and determine its value as a predictive marker of pathological response to NCT. A total of 357 patients with breast cancer treated between January 2014 and December 2016 were included. An automated volumetric breast density (Vbd) measurement method was used to calculate Vbd on mammography before and after NCT. Patients were divided into three groups according to ΔVbd%, calculated as follows: Vbd (post-NCT - pre-NCT)/pre-NCT Vbd × 100 (%). The stable, decreased, and increased groups were defined as -20% ≤ ΔVbd% ≤ 20%, ΔVbd% < -20%, and ΔVbd% > 20%, respectively. Pathological complete response (pCR) was considered to be achieved after NCT if there was no evidence of invasive carcinoma in the breast or metastatic tumors in the axillary and regional lymph nodes on surgical pathology. The association between ΔVbd% grouping and pCR was analyzed using univariable and multivariable logistic regression analyses. The interval between the pre-NCT and post-NCT mammograms ranged from 79 to 250 days (median, 170 days). In the multivariable analysis, ΔVbd% grouping (odds ratio for pCR of 0.420 [95% confidence interval, 0.195-0.905; P = 0.027] for the decreased group compared with the stable group), N stage at diagnosis, histologic grade, and breast cancer subtype were significantly associated with pCR. This tendency was more evident in the luminal B-like and triple-negative subtypes. ΔVbd% was associated with pCR in breast cancer after NCT, with the decreased group showing a lower rate of pCR than the stable group. Automated measurement of ΔVbd% may help predict the NCT response and prognosis in breast cancer.

Association of Longitudinal Mammographic Breast Density Changes with Subsequent Breast Cancer Risk.

Background Although Breast Imaging Reporting and Data System (BI-RADS) density classification has been used to assess future breast cancer risk, its reliability and validity are still debated in literature. Purpose To determine the association between overall longitudinal changes in mammographic breast density and breast cancer risk stratified by menopausal status. Materials and Methods In a retrospective cohort study using the Korean National Health Insurance Service database, women aged at least 40 years without a history of cancer who underwent three consecutive biennial mammographic screenings in 2009-2014 were followed up through December 2020. Participants were divided according to baseline breast density: fatty (BI-RADS categories a, b) versus dense (BI-RADS categories c, d) and then into subgroups on the basis of changes from the first to second and from second to third screenings. Women without change in breast density were used as the reference group. Main outcomes were incident breast cancer events, both invasive breast cancer and ductal carcinoma in situ. Cox proportion hazard regression was used to calculate the hazard ratio (HR) with adjustment for other covariables. Results Among 2 253 963 women (mean age, 59 years ± 9) there were 22 439 detected breast cancers. Premenopausal women with fatty breasts at the first screening had a higher risk of breast cancer as density increased in the second and third screenings (fatty-to-dense HR, 1.45 [95% CI: 1.27, 1.65]; dense-to-fatty HR, 1.53 [95% CI: 1.34, 1.74]; dense-to-dense HR, 1.93 [95% CI: 1.75, 2.13]). In premenopausal women with dense breasts at baseline, those in whom density continuously decreased had a 0.62-fold lower risk (95% CI: 0.56, 0.69). Similar results were observed in postmenopausal women, remaining significant after adjustment for baseline breast density or changes in body mass index (fatty-to-dense HR, 1.50 [95% CI: 1.39, 1.62]; dense-to-fatty HR, 1.42 [95% CI: 1.31, 1.53]; dense-to-dense HR, 1.62 [95% CI: 1.51, 1.75]). Conclusion In both premenopausal and postmenopausal women undergoing three consecutive biennial mammographic screenings, a consecutive increase in breast density augmented the future breast cancer risk whereas a continuous decrease was associated with a lower risk. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Kataoka et al in this issue.