Longitudinal Changes in Volumetric Breast Density with Tamoxifen and Aromatase Inhibitors

MRI-based Deep Learning Models for Preoperative Breast Volume and Density Assessment Assisting Breast Reconstruction

BACKGROUND

The volume of the implant is the most critical element of breast reconstruction, so it is necessary to accurately assess the preoperative volume of the healthy and affected breasts and select the appropriate implant for placement. Accurate and automated methods for quantitative assessment of breast volume can optimize breast reconstruction surgery and assist physicians in clinical decision making. The aim of this study was to develop an artificial intelligence model for automated segmentation of the breast and measurement of volume.

MATERIAL AND METHODS

A total of 249 subjects undergoing breast reconstruction surgery were enrolled in this study. Subjects underwent preoperative breast MRI, and the breast region manually outlined by the imaging physician served as the gold standard for volume measurement by the automated segmentation model. In this study, we developed three automated algorithms for automatic segmentation of breast regions, including a simple alignment model, an alignment dynamic encoding model, and a deep learning model. The volumetric agreement between the three automated segmentation algorithms and the breast regions manually segmented by imaging physicians was evaluated by calculating the mean square error (MSE) and intragroup correlation coefficient (ICC), and the reproducibility of the automated segmentation of the breast regions was assessed by the test-retest step.

RESULTS

The three breast automated segmentation models developed in this study (simple registration model, dynamic programming model, and deep learning model) showed strong ICC with manual segmentation of the breast region, with MSEs of 1.124, 0.693, and 0.781, and ICCs of 0.975 (95% CI, 0.869-0.991), 0.986 (95% CI, 0.967-0.996), and 0.983 (95% CI, 0.961-0.992), respectively. Regarding the test-retest results of breast volume, the dynamic programming model performed the best with an MSE of 0.370 and an ICC of 0.993 (95% CI, 0.982-0.997), followed by the deep learning algorithm with an MSE of 0.741 and an ICC of 0.983 (95% CI, 0.956-0.993), and the simple registration algorithm with an MSE of 0.763 and an ICC of 0.982 (95% CI, 0.949-0.993). The reproducibility of the breast region segmented by the three automated algorithms was higher than that of manual segmentation by different radiologists.

CONCLUSION

The three automated breast segmentation algorithms developed in this study generate accurate and reliable breast regions, enable highly reproducible breast region segmentation and automated volume measurements, and provide a valuable tool for surgical selection of appropriate prostheses.

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Population Attributable Risk of Advanced-Stage Breast Cancer by Race and Ethnicity

Importance

Advanced-stage breast cancer rates vary by race and ethnicity, with Black women having a 2-fold higher rate than White women among regular screeners. Clinical risk factors that explain a large proportion of advanced breast cancers by race and ethnicity are unknown.

Objective

To evaluate the population attributable risk proportions (PARPs) for advanced-stage breast cancer (prognostic pathologic stage IIA or higher) associated with clinical risk factors among routinely screened premenopausal and postmenopausal women by race and ethnicity.

Design, Setting, and Participants

This cohort study used data collected prospectively from Breast Cancer Surveillance Consortium community-based breast imaging facilities from January 2005 to June 2018. Participants were women aged 40 to 74 years undergoing 3 331 740 annual (prior screening within 11-18 months) or biennial (prior screening within 19-30 months) screening mammograms associated with 1815 advanced breast cancers diagnosed within 2 years of screening examinations. Data analysis was performed from September 2022 to August 2023.

Exposures

Heterogeneously or extremely dense breasts, first-degree family history of breast cancer, overweight/obesity (body mass index >25.0), history of benign breast biopsy, and screening interval (biennial vs annual) stratified by menopausal status and race and ethnicity (Asian or Pacific Islander, Black, Hispanic/Latinx, White, other/multiracial).

Main Outcomes and Measures

PARPs for advanced breast cancer.

Results

Among 904 615 women, median (IQR) age was 57 (50-64) years. Of the 3 331 740 annual or biennial screening mammograms, 10.8% were for Asian or Pacific Islander women; 9.5% were for Black women; 5.3% were for Hispanic/Latinx women; 72.0% were for White women; and 2.0% were for women of other races and ethnicities, including those who were Alaska Native, American Indian, 2 or more reported races, or other. Body mass index PARPs were larger for postmenopausal vs premenopausal women (30% vs 22%) and highest for postmenopausal Black (38.6%; 95% CI, 32.0%-44.8%) and Hispanic/Latinx women (31.8%; 95% CI, 25.3%-38.0%) and premenopausal Black women (30.3%; 95% CI, 17.7%-42.0%), with overall prevalence of having overweight/obesity highest in premenopausal Black (84.4%) and postmenopausal Black (85.1%) and Hispanic/Latinx women (72.4%). Breast density PARPs were larger for premenopausal vs postmenopausal women (37% vs 24%, respectively) and highest among premenopausal Asian or Pacific Islander (46.6%; 95% CI, 37.9%-54.4%) and White women (39.8%; 95% CI, 31.7%-47.3%) whose prevalence of dense breasts was high (62%-79%). For premenopausal and postmenopausal women, PARPs were small for family history of breast cancer (5%-8%), history of breast biopsy (7%-12%), and screening interval (2.1%-2.3%).

Conclusions and Relevance

In this cohort study among routinely screened women, the proportion of advanced breast cancers attributed to biennial vs annual screening was small. To reduce the number of advanced breast cancer diagnoses, primary prevention should focus on interventions that shift patients with overweight and obesity to normal weight.

Breast density reduction as a predictor for prognosis in premenopausal women with estrogen receptor-positive breast cancer: an exploratory analysis of the updated ASTRRA study

BACKGROUND

While the relationship between mammographic breast density reduction (MDR) and endocrine therapy efficacy has been reported in estrogen receptor (ER)-positive breast cancer, it is still unclear in premenopausal women, especially in the case of adding ovarian function suppression (OFS) to antihormone therapy. The authors investigated the impact of MDR on prognosis stratified by treatment based on the updated results of the ASTRRA trial.

MATERIALS AND METHODS

The ASTRRA trial, a randomized phase III study, showed that adding OFS to tamoxifen (TAM) improved survival in premenopausal women with estrogen receptor-positive breast cancer after chemotherapy. The authors updated survival outcomes and assessed mammography before treatment and the annual follow-up mammography for up to 5 years after treatment initiation. Mammographic density (MD) was classified into four categories based on the Breast Imaging-Reporting and Data System. MDR-positivity was defined as a downgrade in MD grade on follow-up mammography up to 2 years after randomization, with pretreatment MD grade as a reference.

RESULTS

The authors evaluated MDR in 944 of the 1282 patients from the trial, and 813 (86.2%) had grade III or IV MD. There was no difference in the MDR-positivity rate between the two treatment groups [TAM-only group (106/476 (22.3%)) vs. TAM+OFS group (89/468 (19.0%)); P =0.217). MDR-positivity was significantly associated with better disease-free survival (DFS) in the TAM+OFS group (estimated 8-year DFS: 93.1% in MDR-positive vs. 82.0% in MDR-negative patients; HR: 0.37; 95% CI: 0.16-0.85; P =0.019), but not in the TAM-only group ( Pinteraction =0.039). MDR-positive patients who received TAM+OFS had a favorable DFS compared to MDR-negative patients who received only TAM (HR: 0.30; 95% CI: 0.13-0.70; P =0.005).

CONCLUSION

Although the proportion of MDR-positive patients was comparable between both treatment groups, MDR-positivity was independently associated with favorable outcomes only in the TAM+OFS group.

Predicting mammographic density with linear ultrasound transducers

BACKGROUND

High mammographic density (MD) is a risk factor for the development of breast cancer (BC). Changes in MD are influenced by multiple factors such as age, BMI, number of full-term pregnancies and lactating periods. To learn more about MD, it is important to establish non-radiation-based, alternative examination methods to mammography such as ultrasound assessments.

METHODS

We analyzed data from 168 patients who underwent standard-of-care mammography and performed additional ultrasound assessment of the breast using a high-frequency (12 MHz) linear probe of the VOLUSON® 730 Expert system (GE Medical Systems Kretztechnik GmbH & Co OHG, Austria). Gray level bins were calculated from ultrasound images to characterize mammographic density. Percentage mammographic density (PMD) was predicted by gray level bins using various regression models.

RESULTS

Gray level bins and PMD correlated to a certain extent. Spearman's ρ ranged from - 0.18 to 0.32. The random forest model turned out to be the most accurate prediction model (cross-validated R2, 0.255). Overall, ultrasound images from the VOLUSON® 730 Expert device in this study showed limited predictive power for PMD when correlated with the corresponding mammograms.

CONCLUSIONS

In our present work, no reliable prediction of PMD using ultrasound imaging could be observed. As previous studies showed a reasonable correlation, predictive power seems to be highly dependent on the device used. Identifying feasible non-radiation imaging methods of the breast and their predictive power remains an important topic and warrants further evaluation. Trial registration 325-19 B (Ethics Committee of the medical faculty at Friedrich Alexander University of Erlangen-Nuremberg, Erlangen, Germany).

Mammographic Breast Density: Current Assessment Methods, Clinical Implications, and Future Directions

Mammographic breast density is widely accepted as an independent risk factor for the development of breast cancer. In addition, because dense breast tissue may mask breast malignancies, breast density is inversely related to the sensitivity of screening mammography. Given the risks associated with breast density, as well as ongoing efforts to stratify individual risk and personalize breast cancer screening and prevention, numerous studies have sought to better understand the factors that impact breast density, and to develop and implement reproducible, quantitative methods to assess mammographic density. Breast density assessments have been incorporated into risk assessment models to improve risk stratification. Recently, novel techniques for analyzing mammographic parenchymal complexity, or texture, have been explored as potential means of refining mammographic tissue-based risk assessment beyond breast density.

Mammographic Density Reduction is Associated to the Prognosis in Asian Breast Cancer Patients Receiving Hormone Therapy

INTRODUCTION

Using mammographic density as a significant biomarker for predicting prognosis in adjuvant hormone therapy patients is controversial due to the conflicting results of recent studies. This study aimed to evaluate hormone therapy-induced mammographic density reduction and its association with prognosis in Taiwanese patients.

METHODS

In this retrospective study, 1941 patients with breast cancer were screened, and 399 patients with estrogen receptor-positive breast cancer who received adjuvant hormone therapy were enrolled. The mammographic density was measured using a fully automatic estimation procedure based on full-field digital mammography. The prognosis included relapse and metastasis during treatment follow-up. The Kaplan-Meier method and Cox proportional hazards model were used for disease-free survival analysis.

RESULTS

A mammographic density reduction rate >20.8%, measured preoperatively and after receiving hormone therapy from 12-18 months, was a significant threshold for predicting prognosis in patients with breast cancer. The disease-free survival rate was significantly higher in patients whose mammographic density reduction rate was >20.8% (P = .048).

CONCLUSION

This study's findings could help estimate the prognosis for patients with breast cancer and may improve the quality of adjuvant hormone therapy after enlarging the study cohort in the future.

Two fully automated data-driven 3D whole-breast segmentation strategies in MRI for MR-based breast density using image registration and U-Net with a focus on reproducibility

Presence of higher breast density (BD) and persistence over time are risk factors for breast cancer. A quantitatively accurate and highly reproducible BD measure that relies on precise and reproducible whole-breast segmentation is desirable. In this study, we aimed to develop a highly reproducible and accurate whole-breast segmentation algorithm for the generation of reproducible BD measures. Three datasets of volunteers from two clinical trials were included. Breast MR images were acquired on 3 T Siemens Biograph mMR, Prisma, and Skyra using 3D Cartesian six-echo GRE sequences with a fat-water separation technique. Two whole-breast segmentation strategies, utilizing image registration and 3D U-Net, were developed. Manual segmentation was performed. A task-based analysis was performed: a previously developed MR-based BD measure, MagDensity, was calculated and assessed using automated and manual segmentation. The mean squared error (MSE) and intraclass correlation coefficient (ICC) between MagDensity were evaluated using the manual segmentation as a reference. The test-retest reproducibility of MagDensity derived from different breast segmentation methods was assessed using the difference between the test and retest measures (Δ2-1), MSE, and ICC. The results showed that MagDensity derived by the registration and deep learning segmentation methods exhibited high concordance with manual segmentation, with ICCs of 0.986 (95%CI: 0.974-0.993) and 0.983 (95%CI: 0.961-0.992), respectively. For test-retest analysis, MagDensity derived using the registration algorithm achieved the smallest MSE of 0.370 and highest ICC of 0.993 (95%CI: 0.982-0.997) when compared to other segmentation methods. In conclusion, the proposed registration and deep learning whole-breast segmentation methods are accurate and reliable for estimating BD. Both methods outperformed a previously developed algorithm and manual segmentation in the test-retest assessment, with the registration exhibiting superior performance for highly reproducible BD measurements.

Breast Density: Current Knowledge, Assessment Methods, and Clinical Implications

Breast density is an accepted independent risk factor for the future development of breast cancer, and greater breast density has the potential to mask malignancies on mammography, thus lowering the sensitivity of screening mammography. The risk associated with dense breast tissue has been shown to be modifiable with changes in breast density. Numerous studies have sought to identify factors that influence breast density, including age, genetic, racial/ethnic, prepubertal, adolescent, lifestyle, environmental, hormonal, and reproductive history factors. Qualitative, semiquantitative, and quantitative methods of breast density assessment have been developed, but to date there is no consensus assessment method or reference standard for breast density. Breast density has been incorporated into breast cancer risk models, and there is growing consciousness of the clinical implications of dense breast tissue in both the medical community and public arena. Efforts to improve breast cancer screening sensitivity for women with dense breasts have led to increased attention to supplemental screening methods in recent years, prompting the American College of Radiology to publish Appropriateness Criteria for supplemental screening based on breast density.

Use of a convolutional neural network-based mammographic evaluation to predict breast cancer recurrence among women with hormone receptor-positive operable breast cancer

PURPOSE

We evaluated whether a novel, fully automated convolutional neural network (CNN)-based mammographic evaluation can predict breast cancer relapse among women with operable hormone receptor (HR)-positive breast cancer.

METHODS

We conducted a retrospective cohort study among women with stage I-III, HR-positive unilateral breast cancer diagnosed at Columbia University Medical Center from 2007 to 2017, who received adjuvant endocrine therapy and had at least two mammograms (baseline, annual follow-up) of the contralateral unaffected breast for CNN analysis. We extracted demographics, clinicopathologic characteristics, breast cancer treatments, and relapse status from the electronic health record. Our primary endpoint was change in CNN risk score (range, 0-1). We used two-sample t-tests to assess for difference in mean CNN scores between patients who relapsed vs. remained in remission, and conducted Cox regression analyses to assess for association between change in CNN score and breast cancer-free interval (BCFI), adjusting for known prognostic factors.

RESULTS

Among 848 women followed for a median of 59 months, there were 67 (7.9%) breast cancer relapses (36 distant, 25 local, 6 new primaries). There was a significant difference in mean absolute change in CNN risk score from baseline to 1-year follow-up between those who relapsed vs. remained in remission (0.001 vs. - 0.022, p = 0.030). After adjustment for prognostic factors, a 0.01 absolute increase in CNN score at 1-year was significantly associated with BCFI, hazard ratio = 1.05 (95% Confidence Interval 1.01-1.09, p = 0.011).

CONCLUSION

Short-term change in the CNN-based breast cancer risk model on adjuvant endocrine therapy predicts breast cancer relapse, and warrants further evaluation in prospective studies.

Current regular aspirin use and mammographic breast density: a cross-sectional analysis considering concurrent statin and metformin use

PURPOSE

The nonsteroidal anti-inflammatory drug aspirin is an agent of interest for breast cancer prevention. However, it is unclear if aspirin affects mammographic breast density (MBD), a marker of elevated breast cancer risk, particularly in the context of concurrent use of medications indicated for common cardiometabolic conditions, which may also be associated with MBD.

METHODS

We used data from the New York Mammographic Density Study for 770 women age 40-60 years old with no history of breast cancer. We evaluated the association between current regular aspirin use and MBD, using linear regression for continuous measures of absolute and percent dense areas and absolute non-dense area, adjusted for body mass index (BMI), sociodemographic and reproductive factors, and use of statins and metformin. We assessed effect modification by BMI and reproductive factors.

RESULTS

After adjustment for co-medication, current regular aspirin use was only positively associated with non-dense area (β = 18.1, 95% CI: 6.7, 29.5). Effect modification by BMI and parity showed current aspirin use to only be associated with larger non-dense area among women with a BMI ≥ 30 (β = 28.2, 95% CI: 10.8, 45.7), and with lower percent density among parous women (β = -3.3, 95% CI: -6.4, -0.3).

CONCLUSIONS

Independent of co-medication use, current regular aspirin users had greater non-dense area with stronger estimates for women with higher BMI. We found limited support for an association between current aspirin use and mammographically dense breast tissue among parous women.

Mammographic density, endocrine therapy and breast cancer risk: a prognostic and predictive biomarker review

BACKGROUND

Endocrine therapy is effective at preventing or treating breast cancer. Some forms of endocrine therapy have been shown to reduce mammographic density. Reduced mammographic density for women receiving endocrine therapy could be used to estimate the chance of breast cancer returning or developing breast cancer in the first instance (a prognostic biomarker). In addition, changes in mammographic density might be able to predict how well a woman responds to endocrine therapy (a predictive biomarker). The role of breast density as a prognostic or predictive biomarker could help improve the management of breast cancer.

OBJECTIVES

To assess the evidence that a reduction in mammographic density following endocrine therapy for breast cancer prevention in women without previous breast cancer, or for treatment in women with early-stage hormone receptor-positive breast cancer, is a prognostic or predictive biomarker.

SEARCH METHODS

We searched the Cochrane Breast Cancer Group Specialised Register, CENTRAL, MEDLINE, Embase, and two trials registers on 3 August 2020 along with reference checking, bibliographic searching, and contact with study authors to obtain further data.

SELECTION CRITERIA

We included randomised, cohort and case-control studies of adult women with or without breast cancer receiving endocrine therapy. Endocrine therapy agents included were selective oestrogen receptor modulators and aromatase inhibitors. We required breast density before start of endocrine therapy and at follow-up. We included studies published in English.

DATA COLLECTION AND ANALYSIS

We used standard methodological procedures expected by Cochrane. Two review authors independently extracted data and assessed risk of bias using adapted Quality in Prognostic Studies (QUIPS) and Risk Of Bias In Non-randomised Studies - of Interventions (ROBINS-I) tools. We used the GRADE approach to evaluate the certainty of the evidence. We did not perform a quantitative meta-analysis due to substantial heterogeneity across studies.

MAIN RESULTS

Eight studies met our inclusion criteria, of which seven provided data on outcomes listed in the protocol (5786 women). There was substantial heterogeneity across studies in design, sample size (349 to 1066 women), participant characteristics, follow-up (5 to 14 years), and endocrine therapy agent. There were five breast density measures and six density change definitions. All studies had at least one domain as at moderate or high risk of bias. Common concerns were whether the study sample reflected the review target population, and likely post hoc definitions of breast density change. Most studies on prognosis for women receiving endocrine therapy reported a reduced risk associated with breast density reduction. Across endpoints, settings, and agents, risk ratio point estimates (most likely value) were between 0.1 and 1.5, but with substantial uncertainty. There was greatest consistency in the direction and magnitude of the effect for tamoxifen (across endpoints and settings, risk ratio point estimates were between 0.3 and 0.7). The findings are summarised as follows. Prognostic biomarker findings: Treatment Breast cancer mortality Two studies of 823 women on tamoxifen (172 breast cancer deaths) reported risk ratio point estimates of ~0.4 and ~0.5 associated with a density reduction. The certainty of the evidence was low. Recurrence Two studies of 1956 women on tamoxifen reported risk ratio point estimates of ~0.4 and ~0.7 associated with a density reduction. There was risk of bias in methodology for design and analysis of the studies and considerable uncertainty over the size of the effect. One study of 175 women receiving an aromatase inhibitor reported a risk ratio point estimate of ~0.1 associated with a density reduction. There was considerable uncertainty about the effect size and a moderate or high risk of bias in all domains. One study of 284 women receiving exemestane or tamoxifen as part of a randomised controlled trial reported risk ratio point estimates of ~1.5 (loco-regional recurrence) and ~1.3 (distance recurrence) associated with a density reduction. There was risk of bias in reporting and study confounding, and uncertainty over the size of the effects. The certainty of the evidence for all recurrence endpoints was very low. Incidence of a secondary primary breast cancer Two studies of 451 women on exemestane, tamoxifen, or unknown endocrine therapy reported risk ratio point estimates of ~0.5 and ~0.6 associated with a density reduction. There was risk of bias in reporting and study confounding, and uncertainty over the effect size. The certainty of the evidence was very low. We were unable to find data regarding the remaining nine outcomes prespecified in the review protocol. Prevention Incidence of invasive breast cancer and ductal carcinoma in situ (DCIS) One study of 507 women without breast cancer who were receiving preventive tamoxifen as part of a randomised controlled trial (51 subsequent breast cancers) reported a risk ratio point estimate of ~0.3 associated with a density reduction. The certainty of the evidence was low. Predictive biomarker findings: One study of a subset of 1065 women from a randomised controlled trial assessed how much the effect of endocrine therapy could be explained by breast density declines in those receiving endocrine therapy. This study evaluated the prevention of invasive breast cancer and DCIS. We found some evidence to support the hypothesis, with a risk ratio interaction point estimate ~0.5. However, the 95% confidence interval included unity, and data were based on 51 women with subsequent breast cancer in the tamoxifen group. The certainty of the evidence was low.

AUTHORS' CONCLUSIONS

There is low-/very low-certainty evidence to support the hypothesis that breast density change following endocrine therapy is a prognostic biomarker for treatment or prevention. Studies suggested a potentially large effect size with tamoxifen, but the evidence was limited. There was less evidence that breast density change following tamoxifen preventive therapy is a predictive biomarker than prognostic biomarker. Evidence for breast density change as a prognostic treatment biomarker was stronger for tamoxifen than aromatase inhibitors. There were no studies reporting mammographic density change following endocrine therapy as a predictive biomarker in the treatment setting, nor aromatase inhibitor therapy as a prognostic or predictive biomarker in the preventive setting. Further research is warranted to assess mammographic density as a biomarker for all classes of endocrine therapy and review endpoints.

Impact of systemic adjuvant therapy and CYP2D6 activity on mammographic density in a cohort of tamoxifen-treated breast cancer patients

PURPOSE

Change in mammographic density has been suggested to be a proxy of tamoxifen response. We investigated the effect of additional adjuvant systemic therapy and CYP2D6 activity on MD change in a cohort of tamoxifen-treated pre- and postmenopausal breast cancer patients.

METHODS

Swedish breast cancer patients (n = 699)  operated 2006-2014, genotyped for CYP2D6, having at least three months postoperative tamoxifen treatment, a baseline, and at least one follow-up digital mammogram were included in the study. Other systemic adjuvant treatment included chemotherapy, goserelin, and aromatase inhibitors. Change in MD, dense area, was assessed using the automated STRATUS method. Patients were stratified on baseline characteristics, treatments, and CYP2D6 activity (poor, intermediate, extensive, and ultrarapid). Relative density change was calculated at year 1, 2, and 5 during follow-up in relation to treatments and CYP2D6 activity.

RESULTS

Mean relative DA decreased under the follow-up period, with a more pronounced MD reduction in premenopausal patients. No significant effect of chemotherapy, aromatase inhibitors, goserelin, or CYP2D6 activity on DA change was found. DA did not revert to baseline levels after tamoxifen discontinuation.

CONCLUSION

Our results indicate that other systemic adjuvant therapy does not further reduce MD in tamoxifen-treated breast cancer patients. We could not confirm the previously suggested association between CYP2D6 activity and MD reduction in a clinical setting with multimodality adjuvant treatment. No rebound effect on MD decline after tamoxifen discontinuation was evident.

Quantitative Breast Density in Contrast-Enhanced Mammography

Contrast-enhanced mammography (CEM) demonstrates a potential role in personalized screening models, in particular for women at increased risk and women with dense breasts. In this study, volumetric breast density (VBD) measured in CEM images was compared with VBD obtained from digital mammography (DM) or tomosynthesis (DBT) images. A total of 150 women who underwent CEM between March 2019 and December 2020, having at least a DM/DBT study performed before/after CEM, were included. Low-energy CEM (LE-CEM) and DM/DBT images were processed with automatic software to obtain the VBD. VBDs from the paired datasets were compared by Wilcoxon tests. A multivariate regression model was applied to analyze the relationship between VBD differences and multiple independent variables certainly or potentially affecting VBD. Median VBD was comparable for LE-CEM and DM/DBT (12.73% vs. 12.39%), not evidencing any statistically significant difference (p = 0.5855). VBD differences between LE-CEM and DM were associated with significant differences of glandular volume, breast thickness, compression force and pressure, contact area, and nipple-to-posterior-edge distance, i.e., variables reflecting differences in breast positioning (coefficient of determination 0.6023; multiple correlation coefficient 0.7761). Volumetric breast density was obtained from low-energy contrast-enhanced spectral mammography and was not significantly different from volumetric breast density measured from standard mammograms.

Association of Daily Alcohol Intake, Volumetric Breast Density, and Breast Cancer Risk

High alcohol intake and breast density increase breast cancer (BC) risk, but their interrelationship is unknown. We examined whether volumetric density modifies and/or mediates the alcohol-BC association. BC cases (n = 2233) diagnosed from 2006 to 2013 in the San Francisco Bay area had screening mammograms 6 or more months before diagnosis; controls (n = 4562) were matched on age, mammogram date, race or ethnicity, facility, and mammography machine. Logistic regression was used to estimate alcohol-BC associations adjusted for age, body mass index, and menopause; interaction terms assessed modification. Percent mediation was quantified as the ratio of log (odds ratios [ORs]) from models with and without density measures. Alcohol consumption was associated with increased BC risk (2-sided P_trend = .004), as were volumetric percent density (OR = 1.45 per SD, 95% confidence interval [CI] = 1.36 to 1.56) and dense volume (OR = 1.30, 95% CI = 1.24 to 1.37). Breast density did not modify the alcohol-BC association (2-sided P > .10 for all). Dense volume mediated 25.0% (95% CI = 5.5% to 44.4%) of the alcohol-BC association (2-sided P = .01), suggesting alcohol may partially increase BC risk by increasing fibroglandular tissue.

Mammographic density changes following BC treatment

BACKGROUND

Mammographic density (MD) reduction is associated with lower risk of breast cancer (BC) recurrence and may be used as a marker of treatment outcome; however, trends in MD following BC therapies and the factors associated with such trends are poorly understood. The aim of this study was to investigate MD changes following BC treatment and the factors associated with these changes.

METHODS

A total of 226 BC-affected patients who received BC treatments were examined. MD was assessed by the Laboratory for individualized Radiodensity Assessment (LIBRA) software. A Wilcoxon ranked signed test was used to investigate the differences in MD before and after treatment and median independent test to assess the associated factors.

RESULTS

Significant differences in MD between baseline and follow-up mammograms were observed for all MD measures: percent density (p ≤ 0.005), dense area (p ≤ 0.004), and nondense area (p ≤ 0.02). After adjustment, these differences were more pronounced among younger at BC diagnosis (p ≤ 0.001), premenopausal (p ≤ 0.003), and obese women (p ≤ 0.05). Changes in MD were evident regardless of the treatment regimen. MD reduction was observed among patients with high baseline MD (p < 0.001), younger at BC diagnosis (p ≤ 0.04), premenopausal (p < 0.001), and normal body mass index (p = 0.04). Patients who experienced an increase in nondense area had high percent density at baseline (p ≤ 0.001).

CONCLUSION

Two different MD changes were observed over time: MD increase and decrease. Baseline MD, menopausal status, age at BC diagnosis, and body mass index influenced these changes.

Breast Cancer Risk with Progestin Subdermal Implants: A Challenge in Patients Counseling

There is a steady global rise in the use of progestin subdermal implants, where use has increased by more than 20 times in the past two decades. BC risk has been reported with the older progestin only methods such as oral pills, injectables, and intrauterine devices, however, little is known about the risk with subdermal implants. In this review, we aim to update clinicians and researchers on the current evidence to support patient counseling and to inform future research directions. The available evidence of the association between the use of progestin subdermal implants and BC risk is discussed. We provide an overview of the potential role of endogenous progesterone in BC development. The chemical structure and molecular targets of synthetic progestins of relevance are summarized together with the preclinical and clinical evidence on their association with BC risk. We review all studies that investigated the action of the specific progestins included in subdermal implants. As well, we discuss the potential effect of the use of subdermal implants in women at increased BC risk, including carriers of BC susceptibility genetic mutations.

Mammographic density change in a cohort of premenopausal women receiving tamoxifen for breast cancer prevention over 5 years

Background

A decrease in breast density due to tamoxifen preventive therapy might indicate greater benefit from the drug. It is not known whether mammographic density continues to decline after 1 year of therapy, or whether measures of breast density change are sufficiently stable for personalised recommendations.

Methods

Mammographic density was measured annually over up to 5 years in premenopausal women with no previous diagnosis of breast cancer but at increased risk of breast cancer attending a family-history clinic in Manchester, UK (baseline 2010-2013). Tamoxifen (20 mg/day) for prevention was prescribed for up to 5 years in one group; the other group did not receive tamoxifen and were matched by age. Fully automatic methods were used on mammograms over the 5-year follow-up: three area-based measures (NN-VAS, Stratus, Densitas) and one volumetric (Volpara). Additionally, percentage breast density at baseline and first follow-up mammograms was measured visually. The size of density declines at the first follow-up mammogram and thereafter was estimated using a linear mixed model adjusted for age and body mass index. The stability of density change at 1 year was assessed by evaluating mean squared error loss from predictions based on individual or mean density change at 1 year.

Results

Analysis used mammograms from 126 healthy premenopausal women before and as they received tamoxifen for prevention (median age 42 years) and 172 matched controls (median age 41 years), with median 3 years follow-up. There was a strong correlation between percentage density measures used on the same mammogram in both the tamoxifen and no tamoxifen groups (all correlation coeficients > 0.8). Tamoxifen reduced mean breast density in year 1 by approximately 17–25% of the inter-quartile range of four automated percentage density measures at baseline, and from year 2, it decreased further by approximately 2–7% per year. Predicting change at 2 years using individual change at 1 year was approximately 60–300% worse than using mean change at 1year.

Conclusions

All measures showed a consistent and large average tamoxifen-induced change in density over the first year, and a continued decline thereafter. However, these measures of density change at 1 year were not stable on an individual basis.

Breast Cancer Population Attributable Risk Proportions Associated with Body Mass Index and Breast Density by Race/Ethnicity and Menopausal Status

BACKGROUND

Overweight/obesity and dense breasts are strong breast cancer risk factors whose prevalences vary by race/ethnicity. The breast cancer population attributable risk proportions (PARP) explained by these factors across racial/ethnic groups are unknown.

METHODS

We analyzed data collected from 3,786,802 mammography examinations (1,071,653 women) in the Breast Cancer Surveillance Consortium, associated with 21,253 invasive breast cancers during a median of 5.2 years follow-up. HRs for body mass index (BMI) and breast density, adjusted for age and registry were estimated using separate Cox regression models by race/ethnicity (White, Black, Hispanic, Asian) and menopausal status. HRs were combined with observed risk-factor proportions to calculate PARPs for shifting overweight/obese to normal BMI and shifting heterogeneously/extremely dense to scattered fibroglandular densities.

RESULTS

The prevalences and HRs for overweight/obesity and heterogeneously/extremely dense breasts varied across races/ethnicities and menopausal status. BMI PARPs were larger for postmenopausal versus premenopausal women (12.0%-28.3% vs. 1.0%-9.9%) and nearly double among postmenopausal Black women (28.3%) than other races/ethnicities (12.0%-15.4%). Breast density PARPs were larger for premenopausal versus postmenopausal women (23.9%-35.0% vs. 13.0%-16.7%) and lower among premenopausal Black women (23.9%) than other races/ethnicities (30.4%-35.0%). Postmenopausal density PARPs were similar across races/ethnicities (13.0%-16.7%).

CONCLUSIONS

Overweight/obesity and dense breasts account for large proportions of breast cancers in White, Black, Hispanic, and Asian women despite large differences in risk-factor distributions.

IMPACT

Risk prediction models should consider how race/ethnicity interacts with BMI and breast density. Efforts to reduce BMI could have a large impact on breast cancer risk reduction, particularly among postmenopausal Black women.

Mammographic density changes during neoadjuvant breast cancer treatment: NeoDense, a prospective study in Sweden

OBJECTIVES

To assess if mammographic density (MD) changes during neoadjuvant breast cancer treatment and is predictive of a pathological complete response (pCR).

METHODS

We prospectively included 200 breast cancer patients assigned to neoadjuvant chemotherapy (NACT) in the NeoDense study (2014-2019). Raw data mammograms were used to assess MD with a fully automated volumetric method and radiologists categorized MD using the Breast Imaging-Reporting and Data System (BI-RADS), 5th Edition. Logistic regression was used to calculate odds ratios (OR) for pCR comparing BI-RADS categories c vs. a, b, and d as well as with a 0.5% change in percent dense volume adjusting for baseline characteristics.

RESULTS

The overall median age was 53.1 years, and 48% of study participants were premenopausal pre-NACT. A total of 23% (N = 45) of the patients accomplished pCR following NACT. Patients with very dense breasts (BI-RADS d) were more likely to have a positive axillary lymph node status at diagnosis: 89% of the patients with very dense breasts compared to 72% in the entire cohort. A total of 74% of patients decreased their absolute dense volume during NACT. The likelihood of accomplishing pCR following NACT was independent of volumetric MD at diagnosis and change in volumetric MD during treatment. No trend was observed between decreasing density according to BI-RADS and the likelihood of accomplishing pCR following NACT.

CONCLUSIONS

The majority of patients decreased their MD during NACT. We found no evidence of MD as a predictive marker of pCR in the neoadjuvant setting.

Race May Not Impact Endocrine Therapy-Related Changes in Breast Density

BACKGROUND

Reduction in breast density may be a biomarker of endocrine therapy (ET) efficacy. Our objective was to assess the impact of race on ET-related changes in volumetric breast density (VBD).

METHODS

This retrospective cohort study assessed longitudinal changes in VBD measures in women with estrogen receptor-positive invasive breast cancer treated with ET. VBD, the ratio of fibroglandular volume (FGV) to breast volume (BV), was measured using Volpara software. Changes in measurements were evaluated using a multivariable linear mixed effects model.

RESULTS

Compared with white women (n = 191), black women (n = 107) had higher rates of obesity [mean ± SD body mass index (BMI) 34.5 ± 9.1 kg/m² vs. 30.6 ± 7.0 kg/m², P < 0.001] and premenopausal status (32.7% vs. 16.7%, P = 0.002). Age- and BMI-adjusted baseline FGV, BV, and VBD were similar between groups. Modeled longitudinal changes were also similar: During a follow-up of 30.7 ± 15.0 months (mean ± SD), FGV decreased over time in premenopausal women (slope = -0.323 cm³; SE = 0.093; P = 0.001), BV increased overall (slope = 2.475 cm³; SE = 0.483; P < 0.0001), and VBD decreased (premenopausal slope = -0.063%, SE = 0.011; postmenopausal slope = -0.016%, SE = 0.004; P < 0.0001). Race was not significantly associated with these longitudinal changes, nor did race modify the effect of time on these changes. Higher BMI was associated with lower baseline VBD (P < 0.0001). Among premenopausal women, VBD declined more steeply for women with lower BMI (time × BMI, P = 0.0098).

CONCLUSIONS

Race does not appear to impact ET-related longitudinal changes in VBD.

IMPACT

Racial disparities in estrogen receptor-positive breast cancer recurrence and mortality may not be explained by differential declines in breast density due to ET.

Environmental Influences on Mammographic Breast Density in California: A Strategy to Reduce Breast Cancer Risk

State legislation in many U.S. states, including California, mandates informing women if they have dense breasts on screening mammography, meaning over half of their breast tissue is comprised of non-adipose tissue. Breast density is important to interpret screening sensitivity and is an established breast cancer risk factor. Environmental chemical exposures may play an important role in this, especially during key windows of susceptibility for breast development: in utero, during puberty, pregnancy, lactation, and the peri-menopause. There is a paucity of research, however, examining whether environmental chemical exposures are associated with mammographic breast density, and even less is known about environmental exposures during windows of susceptibility. Now, with clinical breast density scoring being reported routinely for mammograms, it is possible to find out, especially in California, where there are large study populations that can link environmental exposures during windows of susceptibility to breast density. Density scores are now available throughout the state through electronic medical records. We can link these with environmental chemical exposures via state-wide monitoring. Studying the effects of environmental exposure on breast density may provide valuable monitoring and etiologic data to inform strategies to reduce breast cancer risk.

Involution of Breast Lobules, Mammographic Breast Density and Prognosis Among Tamoxifen-Treated Estrogen Receptor-Positive Breast Cancer Patients

Mammographic breast density (MD) reflects breast fibroglandular content. Its decline following adjuvant tamoxifen treated, estrogen receptor (ER)-positive breast cancer has been associated with improved outcomes. Breast cancers arise from structures termed lobules, and lower MD is associated with increased age-related lobule involution. We assessed whether pre-treatment involution influenced associations between MD decline and risk of breast cancer-specific death. ER-positive tamoxifen treated patients diagnosed at Kaiser Permanente Northwest (1990-2008) were defined as cases who died of breast cancer (n = 54) and matched controls (remained alive over similar follow-up; n = 180). Lobule involution was assessed by examining terminal duct lobular units (TDLUs) in benign tissues surrounding cancers as TDLU count/mm², median span and acini count/TDLU. MD (%) was measured in the unaffected breast at baseline (median 6-months before) and follow-up (median 12-months after tamoxifen initiation). TDLU measures and baseline MD were positively associated among controls (p < 0.05). In multivariable regression models, MD decline (≥10%) was associated with reduced risk of breast cancer-specific death before (odds ratio (OR): 0.41, 95% CI: 0.18-0.92) and after (OR: 0.41, 95% CI: 0.18-0.94) adjustment for TDLU count/mm², TDLU span (OR: 0.34, 95% CI: 0.14-0.84), and acini count/TDLU (OR: 0.33, 95% CI: 0.13-0.81). MD decline following adjuvant tamoxifen is associated with reduced risk of breast cancer-specific death, irrespective of pre-treatment lobule involution.

Effect of Bazedoxifene and Conjugated Estrogen (Duavee) on Breast Cancer Risk Biomarkers in High-Risk Women: A Pilot Study

Interventions that relieve vasomotor symptoms while reducing risk for breast cancer would likely improve uptake of chemoprevention for perimenopausal and postmenopausal women. We conducted a pilot study with 6 months of the tissue selective estrogen complex bazedoxifene (20 mg) and conjugated estrogen (0.45 mg; Duavee) to assess feasibility and effects on risk biomarkers for postmenopausal breast cancer. Risk biomarkers included fully automated mammographic volumetric density (Volpara), benign breast tissue Ki-67 (MIB-1 immunochemistry), and serum levels of progesterone, IGF-1, and IGFBP3, bioavailable estradiol and testosterone. Twenty-eight perimenopausal and postmenopausal women at increased risk for breast cancer were enrolled: 13 in cohort A with baseline Ki-67 < 1% and 15 in cohort B with baseline Ki-67 of 1% to 4%. All completed the study with > 85% drug adherence. Significant changes in biomarkers, uncorrected for multiple comparisons, were a decrease in mammographic fibroglandular volume (P = 0.043); decreases in serum progesterone, bioavailable testosterone, and IGF-1 (P < 0.01), an increase in serum bioavailable estradiol (P < 0.001), and for women from cohort B a reduction in Ki-67 (P = 0.017). An improvement in median hot flash score from 15 at baseline to 0 at 6 months, and menopause-specific quality-of-life total, vasomotor, and sexual domain scores were also observed (P < 0.001). Given the favorable effects on risk biomarkers and patient reported outcomes, a placebo-controlled phase IIB trial is warranted.

Longitudinal Changes in Volumetric Breast Density in Healthy Women across the Menopausal Transition

BACKGROUND

Mammographic breast density declines during menopause. We assessed changes in volumetric breast density across the menopausal transition and factors that influence these changes.

METHODS

Women without a history of breast cancer, who had full field digital mammograms during both pre- and postmenopausal periods, at least 2 years apart, were sampled from four facilities within the San Francisco Mammography Registry from 2007 to 2013. Dense breast volume (DV) was assessed using Volpara on mammograms across the time period. Annualized change in DV from pre- to postmenopause was estimated using linear mixed models adjusted for covariates and per-woman random effects. Multiplicative interactions were evaluated between premenopausal risk factors and time to determine whether these covariates modified the annualized changes.

RESULTS

Among the 2,586 eligible women, 1,802 had one premenopausal and one postmenopausal mammogram, 628 had an additional perimenopausal mammogram, and 156 had two perimenopausal mammograms. Women experienced an annualized decrease in DV [-2.2 cm³ (95% confidence interval, -2.7 to -1.7)] over the menopausal transition. Declines were greater among women with a premenopausal DV above the median (54 cm³) versus below (DV, -3.5 cm³ vs. -1.0 cm³; P < 0.0001). Other breast cancer risk factors, including race, body mass index, family history, alcohol, and postmenopausal hormone therapy, had no effect on change in DV over the menopausal transition.

CONCLUSIONS

High premenopausal DV was a strong predictor of greater reductions in DV across the menopausal transition.

IMPACT

We found that few factors other than premenopausal density influence changes in DV across the menopausal transition, limiting targeted prevention efforts.

Adjuvant Therapy and Mammographic Density Changes in Women With Breast Cancer

Background

Tamoxifen decreases mammographic density. Whether compliance affects this relationship is unclear as is the relationship between other types of adjuvant treatment and changes in mammographic density.

Methods

This prospective cohort study included 2490 women diagnosed with breast cancer during 2001-2015 in Sweden. Mammographic density was assessed within 3 months of diagnosis and 6-36 months post diagnosis. Logistic regression was performed to study the association between each respective adjuvant treatment and mammographic density reduction (annual dense area decrease >15%).

Results

Intention-to-treat analyses using treatment information from the regional cancer registries showed that tamoxifen-treated patients more frequently experienced mammographic density reductions compared with nontreated patients (odds ratio [OR] = 1.58, 95% confidence interval [CI] = 1.25 to 1.99), as did chemotherapy-treated patients (OR = 1.28, 95% CI = 1.06 to 1.54). For chemotherapy, the association was mainly seen in premenopausal women. Neither aromatase inhibitors nor radiotherapy was associated with density change. Tamoxifen use based on prescription and dispensation data from the Swedish Prescribed Drug Register showed that users were more likely to have density reductions compared with nonusers (adjusted OR = 2.24, 95% CI = 1.40 to 3.59). Moreover, among tamoxifen users, tamoxifen continuers were more likely than discontinuers to experience density reductions (adjusted OR = 1.50, 95% CI = 1.04 to 2.17).

Conclusions

Our results indicate that adherence influences the association between tamoxifen and mammographic density reduction. We further found that chemotherapy was associated with density reductions and propose that this is largely secondary to chemotherapy-induced ovarian failure.

Can Dietary and Physical Activity Modifications Reduce Breast Density in Postmenopausal Women? The DAMA Study, a Randomized Intervention Trial in Italy

BACKGROUND

Few randomized trials have been carried out to evaluate the effect of lifestyle modifications on mammographic breast density (MBD). The randomized 2 × 2 factorial Diet, physical Activity and MAmmography trial aimed to evaluate whether MBD can be reduced in postmenopausal women with high baseline MBD by a 24-month dietary and/or physical activity (PA) interventions.

METHODS

We randomized healthy postmenopausal women, attending the Florence (Italy) mammographic screening program, ages 50 to 69 years, nonsmokers, with MBD > 50% and no recent hormone therapy, to (i) a dietary intervention focused on plant foods, with a low glycemic load, low in saturated fats and alcohol; (ii) a PA intervention combining daily moderate intensity activities and one weekly supervised session of more strenuous activity; (iii) both interventions; (iv) general recommendations. We evaluated changes in MBD based on Volpara estimates comparing baseline and follow-up digital mammograms by an intention-to-treat-analysis.

RESULTS

MBD measures were available for 226 participants. An interaction emerged between treatments and thus we run analyses by arms. A decrease in volumetric percent density emerged for women in the dietary intervention (ratio 0.91; 95% CI, 0.86-0.97; P = 0.002) and in the PA intervention arm (0.93; 95% CI, 0.87-0.98; P = 0.01) in comparison with controls. No clear effect emerged in the double intervention arm.

CONCLUSIONS

This intervention trial suggests that a 24-month dietary or PA intervention may reduce MBD in postmenopausal women.

IMPACT

A modification of dietary habits or an increase in PA in postmenopausal women may reduce MBD. Further studies are needed to confirm these findings for planning breast cancer preventive strategies.

Progress in preventive therapy for cancer: a reminiscence and personal viewpoint

Prophylactic drug treatment with aspirin, statins and anti-hypertensive agents has had a major impact on the incidence of cardiovascular disease and is now well established. Progress in therapeutic cancer prevention has been much slower; only recently have effective agents been clearly established. Breast cancer has led the way and endocrine agents used to treat it-notably tamoxifen and the aromatase inhibitors-have now been shown to have a substantial preventive effect as well. However, these agents carry some toxicity and thus identifying high-risk women who are likely to benefit most is a key priority. In contrast, the ability of low-dose aspirin to prevent about one-third of colorectal, gastric, and oesophageal cancers, combined with its much lower toxicity profile, make it attractive for a much larger proportion of the general population. Vaccination against the human papilloma virus is also a preventive intervention with large benefits for the whole population. Here I recall my involvement in these initiatives and offer a personal viewpoint on what has been achieved and what remains to be done.

Vaginal estrogen and mammogram results: case series and review of literature on treatment of genitourinary syndrome of menopause (GSM) in breast cancer survivors

OBJECTIVE

To examine mammographic density before and after at least 1 year of vaginal estrogen use in a small cohort of healthy postmenopausal women and women with a personal history of breast cancer.

METHODS

We extracted data via chart review of patients from a single practitioner's menopause specialty clinic in Baltimore, MD. Mammographic change was primarily determined via the Bi-RADS scoring system, including the Bi-RADS density score. In addition, we conduct a narrative review of the current literature on the usage of local estrogen therapy, and systemic and local alternatives in the treatment of genitourinary syndrome of menopause (GSM) in breast cancer survivors.

RESULTS

Twenty healthy postmenopausal women and three breast cancer survivors fit our inclusion criteria. Amongst these two groups, we did not find an increase in mammographic density after at least 1 year and up to 18 years of local vaginal estrogen. Ospemifene use in one patient did not appear to be associated with any change in Bi-RADS score. Our narrative review found little data on the effects of vaginal estrogen therapy or newer alternative systemic therapies such as ospemifene on mammographic density.

CONCLUSIONS

Low-dose vaginal estrogen use for 1 or more years in a small cohort of women with GSM did not appear to be associated with any changes in breast density or Bi-RADS breast cancer risk scores in the majority of study participants, including three breast cancer survivors. Larger long-term controlled clinical trials should be conducted to examine the effects of low-dose vaginal estrogen on mammographic density in women with and without a personal history of breast cancer. Furthermore, relative efficacy and risk of vaginal estrogen compared with other forms of treatment for GSM should also be studied in long-term trials.

Breast density: why all the fuss?

The term "breast density" or mammographic density (MD) denotes those components of breast parenchyma visualised at mammography that are denser than adipose tissue. MD is composed of a mixture of epithelial and stromal components, notably collagen, in variable proportions. MD is most commonly assessed in clinical practice with the time-honoured method of visual estimation of area-based percent density (PMD) on a mammogram, with categorisation into quartiles. The computerised semi-automated thresholding method, Cumulus, also yielding area-based percent density, is widely used for research purposes; however, the advent of fully automated volumetric methods developed as a consequence of the widespread use of digital mammography (DM) and yielding both absolute and percent dense volumes, has resulted in an explosion of interest in MD recently. Broadly, the importance of MD is twofold: firstly, the presence of marked MD significantly reduces mammographic sensitivity for breast cancer, even with state-of-the-art DM. Recognition of this led to the formation of a powerful lobby group ('Are You Dense') in the US, as a consequence of which 32 states have legislated for mandatory disclosure of MD to women undergoing mammography. Secondly, it is now widely accepted that MD is in itself a risk factor for breast cancer, with a four-to sixfold increased relative risk in women with PMD in the highest quintile compared to those with PMD in the lowest quintile. Consequently, major research efforts are underway to assess whether use of MD could provide a major step forward towards risk-adapted, personalised breast cancer prevention, imaging, and treatment.

Preventing invasive breast cancer using endocrine therapy

Developments in breast cancer treatment have resulted in reduction in breast cancer mortality in the developed world. However incidence continues to rise and greater use of preventive interventions including the use of therapeutic agents is needed to control this burden. High quality evidence from 9 major trials involving more than 83000 participants shows that selective oestrogen receptor modulators (SERMs) reduce breast cancer incidence by 38%. Combined results from 2 large trials with 8424 participants show that aromatase inhibitors (AIs) reduce breast cancer incidence by 53%. These benefits are restricted to prevention of ER positive breast cancers. Restricting preventive therapy to high-risk women improves the benefit-harm balance and many guidelines now encourage healthcare professionals to discuss preventive therapy in these women. Further research is needed to improve our risk-prediction models for the identification of high risk women for preventive therapy with greater accuracy and to develop surrogate biomarkers of response. Long-term follow-up of the IBIS-I trial has provided valuable insights into the durability of benefits from preventive therapy, and underscores the need for such follow up to fully evaluate other agents. Full utilisation of preventive therapy also requires greater knowledge and awareness among both doctors and patients about benefits, harms and risk factors. Healthcare professionals should routinely discuss preventive therapy with women at high-risk of breast cancer.

Qualitative Versus Quantitative Mammographic Breast Density Assessment: Applications for the US and Abroad

Mammographic breast density (MBD) has been proven to be an important risk factor for breast cancer and an important determinant of mammographic screening performance. The measurement of density has changed dramatically since its inception. Initial qualitative measurement methods have been found to have limited consistency between readers, and in regards to breast cancer risk. Following the introduction of full-field digital mammography, more sophisticated measurement methodology is now possible. Automated computer-based density measurements can provide consistent, reproducible, and objective results. In this review paper, we describe various methods currently available to assess MBD, and provide a discussion on the clinical utility of such methods for breast cancer screening.