Longitudinal Trends in Mammographic Percent Density and Breast Cancer Risk

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

INTRODUCTION

Benign 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.

METHODS

We 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.

RESULTS

Overall, 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).

DISCUSSION

Assessment 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.

Repeated measures of mammographic density and texture to evaluate prediction and risk of breast cancer: a systematic review of the methods used in the literature

PURPOSE

It may be important for women to have mammograms at different points in time to track changes in breast density, as fluctuations in breast density can affect breast cancer risk. This systematic review aimed to assess methods used to relate repeated mammographic images to breast cancer risk.

METHODS

The databases including Medline (Ovid) 1946-, Embase.com 1947-, CINAHL Plus 1937-, Scopus 1823-, Cochrane Library (including CENTRAL), and Clinicaltrials.gov were searched through October 2021. Eligibility criteria included published articles in English describing the relationship of change in mammographic features with risk of breast cancer. Risk of bias was assessed using the Quality in Prognostic Studies tool.

RESULTS

Twenty articles were included. The Breast Imaging Reporting and Data System and Cumulus were most commonly used for classifying mammographic density and automated assessment was used on more recent digital mammograms. Time between mammograms varied from 1 year to a median of 4.1, and only nine of the studies used more than two mammograms. Several studies showed that adding change of density or mammographic features improved model performance. Variation in risk of bias of studies was highest in prognostic factor measurement and study confounding.

CONCLUSION

This review provided an updated overview and revealed research gaps in assessment of the use of texture features, risk prediction, and AUC. We provide recommendations for future studies using repeated measure methods for mammogram images to improve risk classification and risk prediction for women to tailor screening and prevention strategies to level of risk.

Studying the association between longitudinal mammographic density measurements and breast cancer risk: a joint modelling approach

BACKGROUND

Researchers have suggested that longitudinal trajectories of mammographic breast density (MD) can be used to understand changes in breast cancer (BC) risk over a woman's lifetime. Some have suggested, based on biological arguments, that the cumulative trajectory of MD encapsulates the risk of BC across time. Others have tried to connect changes in MD to the risk of BC.

METHODS

To summarize the MD-BC association, we jointly model longitudinal trajectories of MD and time to diagnosis using data from a large ([Formula: see text]) mammography cohort of Swedish women aged 40-80 years. Five hundred eighteen women were diagnosed with BC during follow-up. We fitted three joint models (JMs) with different association structures; Cumulative, current value and slope, and current value association structures.

RESULTS

All models showed evidence of an association between MD trajectory and BC risk ([Formula: see text] for current value of MD, [Formula: see text] and [Formula: see text] for current value and slope of MD respectively, and [Formula: see text] for cumulative value of MD). Models with cumulative association structure and with current value and slope association structure had better goodness of fit than a model based only on current value. The JM with current value and slope structure suggested that a decrease in MD may be associated with an increased (instantaneous) BC risk. It is possible that this is because of increased screening sensitivity rather than being related to biology.

CONCLUSION

We argue that a JM with a cumulative association structure may be the most appropriate/biologically relevant model in this context.

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

Importance

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.

Objective

To prospectively evaluate the association between change in mammographic density in each breast over time and risk of subsequent breast cancer.

Design, Setting, and Participants

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

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.

Main Outcomes and Measures

Longitudinal changes over time in each woman's volumetric breast density by case and control status.

Results

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).

Conclusions and Relevance

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.

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

PURPOSE

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.

MATERIALS AND METHODS

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 1-7 years 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.

RESULTS

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.

CONCLUSIONS

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.

Performance of novel deep learning network with the incorporation of the automatic segmentation network for diagnosis of breast cancer in automated breast ultrasound

OBJECTIVE

To develop novel deep learning network (DLN) with the incorporation of the automatic segmentation network (ASN) for morphological analysis and determined the performance for diagnosis breast cancer in automated breast ultrasound (ABUS).

METHODS

A total of 769 breast tumors were enrolled in this study and were randomly divided into training set and test set at 600 vs. 169. The novel DLNs (Resent v2, ResNet50 v2, ResNet101 v2) added a new ASN to the traditional ResNet networks and extracted morphological information of breast tumors. The accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under the receiver operating characteristic (ROC) curve (AUC), and average precision (AP) were calculated. The diagnostic performances of novel DLNs were compared with those of two radiologists with different experience.

RESULTS

The ResNet34 v2 model had higher specificity (76.81%) and PPV (82.22%) than the other two, the ResNet50 v2 model had higher accuracy (78.11%) and NPV (72.86%), and the ResNet101 v2 model had higher sensitivity (85.00%). According to the AUCs and APs, the novel ResNet101 v2 model produced the best result (AUC 0.85 and AP 0.90) compared with the remaining five DLNs. Compared with the novice radiologist, the novel DLNs performed better. The F1 score was increased from 0.77 to 0.78, 0.81, and 0.82 by three novel DLNs. However, their diagnostic performance was worse than that of the experienced radiologist.

CONCLUSIONS

The novel DLNs performed better than traditional DLNs and may be helpful for novice radiologists to improve their diagnostic performance of breast cancer in ABUS.

KEY POINTS

• A novel automatic segmentation network to extract morphological information was successfully developed and implemented with ResNet deep learning networks. • The novel deep learning networks in our research performed better than the traditional deep learning networks in the diagnosis of breast cancer using ABUS images. • The novel deep learning networks in our research may be useful for novice radiologists to improve diagnostic performance.

Spatially tracked whole-breast three-dimensional ultrasound system toward point-of-care breast cancer screening in high-risk women with dense breasts

BACKGROUND

Mammographic screening has reduced mortality in women through the early detection of breast cancer. However, the sensitivity for breast cancer detection is significantly reduced in women with dense breasts, in addition to being an independent risk factor. Ultrasound (US) has been proven effective in detecting small, early-stage, and invasive cancers in women with dense breasts.

PURPOSE

To develop an alternative, versatile, and cost-effective spatially tracked three-dimensional (3D) US system for whole-breast imaging. This paper describes the design, development, and validation of the spatially tracked 3DUS system, including its components for spatial tracking, multi-image registration and fusion, feasibility for whole-breast 3DUS imaging and multi-planar visualization in tissue-mimicking phantoms, and a proof-of-concept healthy volunteer study.

METHODS

The spatially tracked 3DUS system contains (a) a six-axis manipulator and counterbalanced stabilizer, (b) an in-house quick-release 3DUS scanner, adaptable to any commercially available US system, and removable, allowing for handheld 3DUS acquisition and two-dimensional US imaging, and (c) custom software for 3D tracking, 3DUS reconstruction, visualization, and spatial-based multi-image registration and fusion of 3DUS images for whole-breast imaging. Spatial tracking of the 3D position and orientation of the system and its joints (J_1-6 ) were evaluated in a clinically accessible workspace for bedside point-of-care (POC) imaging. Multi-image registration and fusion of acquired 3DUS images were assessed with a quadrants-based protocol in tissue-mimicking phantoms and the target registration error (TRE) was quantified. Whole-breast 3DUS imaging and multi-planar visualization were evaluated with a tissue-mimicking breast phantom. Feasibility for spatially tracked whole-breast 3DUS imaging was assessed in a proof-of-concept healthy male and female volunteer study.

RESULTS

Mean tracking errors were 0.87 ± 0.52, 0.70 ± 0.46, 0.53 ± 0.48, 0.34 ± 0.32, 0.43 ± 0.28, and 0.78 ± 0.54 mm for joints J_1-6 , respectively. Lookup table (LUT) corrections minimized the error in joints J₁ , J₂ , and J₅ . Compound motions exercising all joints simultaneously resulted in a mean tracking error of 1.08 ± 0.88 mm (N = 20) within the overall workspace for bedside 3DUS imaging. Multi-image registration and fusion of two acquired 3DUS images resulted in a mean TRE of 1.28 ± 0.10 mm. Whole-breast 3DUS imaging and multi-planar visualization in axial, sagittal, and coronal views were demonstrated with the tissue-mimicking breast phantom. The feasibility of the whole-breast 3DUS approach was demonstrated in healthy male and female volunteers. In the male volunteer, the high-resolution whole-breast 3DUS acquisition protocol was optimized without the added complexities of curvature and tissue deformations. With small post-acquisition corrections for motion, whole-breast 3DUS imaging was performed on the healthy female volunteer showing relevant anatomical structures and details.

CONCLUSIONS

Our spatially tracked 3DUS system shows potential utility as an alternative, accurate, and feasible whole-breast approach with the capability for bedside POC imaging. Future work is focused on reducing misregistration errors due to motion and tissue deformations, to develop a robust spatially tracked whole-breast 3DUS acquisition protocol, then exploring its clinical utility for screening high-risk women with dense breasts.

Mammographic Density Changes over Time and Breast Cancer Risk: A Systematic Review and Meta-Analysis

Simple Summary

Although mammographic density is strongly linked to the risk of breast cancer, research on the relationship between changes in density over time and the risk of breast cancer has shown conflicting results. We found in the present meta-analysis that increased breast density over time was associated with higher breast cancer risk whereas decreased breast density might be associated with lower breast cancer risk. The results of the meta-analysis constitute a potential opportunity for more individualized screening strategies based on the evolution of breast density during mammography screening.

Abstract

The aim of this meta-analysis was to evaluate the association between mammographic density changes over time and the risk of breast cancer. We performed a systematic literature review based on the PubMed and ISI Web of Knowledge databases. A meta-analysis was conducted by computing extracted hazard ratios (HRs) and 95% confidence intervals (CIs) for cohort studies or odds ratios (ORs) and 95% confidence interval using inverse variance method. Of the nine studies included, five were cohort studies that used HR as a measurement type for their statistical analysis and four were case–control or cohort studies that used OR as a measurement type. Increased breast density over time in cohort studies was associated with higher breast cancer risk (HR: 1.61; 95% CI: 1.33–1.96) whereas decreased breast density over time was associated with lower breast cancer risk (HR: 0.78; 95% CI: 0.71–0.87). Similarly, increased breast density over time was associated with higher breast cancer risk in studies presented ORs (pooled OR: 1.85; 95% CI: 1.29–2.65). Our findings imply that an increase in breast density over time seems to be linked to an increased risk of breast cancer, whereas a decrease in breast density over time seems to be linked to a lower risk of breast cancer.

Automated percent mammographic density, mammographic texture variation, and risk of breast cancer: a nested case-control study

Percent mammographic density (PMD) is a strong breast cancer risk factor, however, other mammographic features, such as V, the standard deviation (SD) of pixel intensity, may be associated with risk. We assessed whether PMD, automated PMD (APD), and V, yielded independent associations with breast cancer risk. We included 1900 breast cancer cases and 3921 matched controls from the Nurses' Health Study (NHS) and the NHSII. Using digitized film mammograms, we estimated PMD using a computer-assisted thresholding technique. APD and V were determined using an automated computer algorithm. We used logistic regression to generate odds ratios (ORs) and 95% confidence intervals (CIs). Median time from mammogram to diagnosis was 4.1 years (interquartile range: 1.6-6.8 years). PMD (OR per SD:1.52, 95% CI: 1.42, 1.63), APD (OR per SD:1.32, 95% CI: 1.24, 1.41), and V (OR per SD:1.32, 95% CI: 1.24, 1.40) were positively associated with breast cancer risk. Associations for APD were attenuated but remained statistically significant after mutual adjustment for PMD or V. Women in the highest quartile of both APD and V (OR vs Q1/Q1: 2.49, 95% CI: 2.02, 3.06), or PMD and V (OR vs Q1/Q1: 3.57, 95% CI: 2.79, 4.58) had increased breast cancer risk. An automated method of PMD assessment is feasible and yields similar, but somewhat weaker, estimates to a manual measure. PMD, APD and V are each independently, positively associated with breast cancer risk. Women with dense breasts and greater texture variation are at the highest relative risk of breast cancer.

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.

Association between changes in mammographic density category and the risk of breast cancer: A nationwide cohort study in East-Asian women

Breast density is strongly associated with breast cancer risk; however, studies on the association between density changes and breast cancer risk have controversial results. The aim of our study was to determine the association between breast density changes and breast cancer risk in East-Asian women. We included 3 301 279 women aged ≥40 years screened for breast cancer twice during 2009 to 2010 and 2011 to 2012. Data were obtained from the National Health Insurance Service (NHIS) database. Breast density was evaluated using the Breast Imaging-Reporting and Data System (BI-RADS). Relative risk (RR) and 5-year risk of developing breast cancer according to density category changes were calculated. Overall, 23.0% of the women had a higher breast density and 22.2% of the women had a lower breast density in second screening compared to the first. An increase in the BI-RADS density category between two subsequent mammographic screenings was associated with an increase in breast cancer risk and vice versa in terms of RR. The 5-year breast cancer risk was affected by the initial BI-RADS density category, changes in density category and patients' characteristics such as age, menopausal status and family history of breast cancer. In patients with breast cancer family history, the 5-year breast cancer risk was prominent, at a maximum of 2.39% (95% CI = 1.23-3.55) in women with breast density category of 2 to 4. Changes in the BI-RADS density category were associated with breast cancer risk. Longitudinal measures of BI-RADS density may be helpful in identifying high-risk women, especially those with a breast cancer family history.

Automated whole breast segmentation for hand-held ultrasound with position information: Application to breast density estimation

BACKGROUND AND OBJECTIVE

Women with higher breast densities have a relatively higher risk to be diagnosed with breast cancer. Hand-held ultrasound (HHUS) can provide precise screening results and detect masses in dense breasts. However, its lack of position information and automatic extraction of breast area hinder the implementation of density estimation. To facilitate reliable breast density evaluation, this study proposed an upgraded version of our whole-breast ultrasound (WBUS) system, which not only can provide precise position information, but also can extract precise breast area automatically based on deep learning method.

METHODS

WBUS images with probe position information were collected from 117 women. For each case, an automatic breast region segmentation by DeepResUnet was conducted, then fibroglandular tissues were extracted from breast region using fuzzy c-mean (FCM) classifier. Finally, the percentage of breast density and breast area of the DeepResUnet predicted region and the breast region of the ground truth were calculated and compared.

RESULTS

The average and standard deviation of each breast case for DeepResUnet predicted breast region of 10-fold in Accuracy (ACC) was 0.963±0.054. Sensitivity (SENS) was 0.928±0.11. Specificity (SPEC) was 0.967±0.054. Dice coefficient (Dice) was 0.916±0.98. Region intersection over union (IoU) was 0.856±0.134. Significant and very high correlations of breast density, fibroglandular tissue area and breast area (R = 0.843, R= 0.822 and R = 0.984, all p values < 0.001) were found between the ground truth and the result of the proposed method for ultrasound images.

CONCLUSIONS

Breast density, fibroglandular tissue, and breast volume evaluated based on the proposed method and WBUS system have significant correlations with ground truth, indicating that the proposed method and WBUS system has the potential to be an alternative modality for breast screening and density estimation in clinical use.

Evaluation of the association between mammographic density and the risk of breast cancer using Quantra software and the BI-RADS classification

To determine the association between mammographic density (MD) and the risk of breast cancer (BC) in Chinese women and to investigate the role of fertility risk factors in regulating the relationship between MD and BC.We used Quantra software and the BI-RADS classification to assess MD in 466 patients and 932 controls. Conditional matched logistic multiple regression analysis was used to determine the relationship between MD and BC, and risk was evaluated with the odds ratio (OR) and 95% confidence interval (CI).The ORs for category 4 versus category 2 were 1.95 (95% confidence interval [95% CI] (1.42∼2.66)) and 1.76 (95% CI (1.28∼2.42)) for the BI-RADS and Quantra classifications, respectively. The ORs for category 5 volumetric breast density (VBD) versus category 2 VBD and 5 fibroglandular tissue volume (FGV) versus category 2 FGV were 1.63 (95% CI (1.20∼2.23)) and 1.92 (95% CI (1.40∼2.63)), respectively. Females with category 5 VBD whose age at menarche was ≤13 years had the highest risk of BC (OR = 2.16, 95% CI (1.24∼3.79)), and females with category 5 FGV whose age at menarche was = 15 years had the lowest risk of BC (OR = 1.65, 95% CI (1.05∼2.62)). Females with categories 3-5 VBD and categories 3-5 FGV had reduced risks of BC with increasing number of births. Females with category 5 VBD had an increased risk of BC with increasing age at first childbirth (the OR increased from 1.49 to 1.95). Those with category 5 VBD had a reduced risk of BC with increasing breastfeeding duration (the OR decreased from 2.08 to 1.55). Females with category 5 FGV had a reduced risk of BC with increasing breastfeeding duration (the OR decreased from 4.12 to 1.62).Both the BI-RADS density classification and Quantra measures indicated that MD is positively associated with the risk of BC in Chinese women and that associations between MD and BC risk differ by age at menarche, parity, age at first childbirth and breastfeeding duration.

Heritability of Mammographic Breast Density, Density Change, Microcalcifications, and Masses

Mammographic features influence breast cancer risk and are used in risk prediction models. Understanding how genetics influence mammographic features is important because the mechanisms through which they are associated with breast cancer are not well known. Here, using mammographic screening history and detailed questionnaire data from 56,820 women from the KARMA prospective cohort study, we investigated the association between a genetic predisposition to breast cancer and mammographic features among women with a family history of breast cancer (N = 49,674) and a polygenic risk score (PRS, N = 9,365). The heritability of mammographic features such as dense area (MD), microcalcifications, masses, and density change (MDC, cm²/year) was estimated using 1,940 sister pairs. Heritability was estimated at 58% [95% confidence interval (CI), 48%-67%) for MD, 23% (2%-45%) for microcalcifications, and 13% (1%-25%)] for masses. The estimated heritability for MDC was essentially null (2%; 95% CI, -8% to 12%). The association between a genetic predisposition to breast cancer (using PRS) and MD and microcalcifications was positive, while for masses this was borderline significant. In addition, for MDC, having a family history of breast cancer was associated with slightly greater MD reduction. In summary, we have confirmed previous findings of heritability in MD, and also established heritability of the number of microcalcifications and masses at baseline. Because these features are associated with breast cancer risk and can improve detecting women at short-term risk of breast cancer, further investigation of common loci associated with mammographic features is warranted to better understand the etiology of breast cancer. SIGNIFICANCE: These findings provide novel data on the heritability of microcalcifications, masses, and density change, which are all associated with breast cancer risk and can indicate women at short-term risk.

Evaluation of automated volumetric breast density software in comparison with visual assessments in an Asian population: A retrospective observational study

This study aims to compare Quantra, as an automated volumetric breast density (Vbd) tool, with visual assessment according to ACR BI-RADS density categories and to determine its potential usage in clinical practice.Five hundred randomly selected screening and diagnostic mammograms were included in this retrospective study. Three radiologists independently assigned qualitative ACR BI-RADS density categories to the mammograms. Quantra automatically calculates the volumetric density data into the system. The readers were blinded to the Quantra and other readers assessment. Inter-reader agreement and agreement between Quantra and each reader were tested. Region under the curve (ROC) analysis was performed to obtain the cut-off value to separate dense from a non-dense breast. Results with P value <.05 was taken as significant.There were 40.4% Chinese, 27% Malays, 19% Indian and 3.6% represent other ethnicities. The mean age of the patients was 57. 15%, 45.6%, 30.4%, and 9% of patients fall under BI-RADS A, B, C and D density category respectively. Fair agreement with Kappa (κ) value: 0.49, 0.38, and 0.30 were seen for Reader 1, 2 and 3 versus Quantra. Moderate agreement with κ value: 0.63, 0.64, 0.51 was seen when the data were dichotomized (density A and B to "non-dense", C and D to "dense"). The cut-off Vbd value was 13.5% to stratify dense from non-dense breasts with a sensitivity of 86.2% and specificity of 83.1% (AUC 91.4%; confidence interval: 88.8, 94.1).Quantra showed moderate agreement with radiologists visual assessment. Hence, this study adds to the available evidence to support the potential use of Quantra as an adjunct tool for breast density assessment in routine clinical practice in the Asian population. We found 13.5% is the best cut-off value to stratify dense to non-dense breasts in our study population. Its application will provide an objective, consistent and reproducible results as well as aiding clinical decision-making on the need for supplementary breast ultrasound in our screening population.

Mammographic Density Change and Risk of Breast Cancer

BACKGROUND

We examined the association between annual mammographic density change (MDC) and breast cancer (BC) risk, and how annual MDC influences the association between baseline mammographic density (MD) and BC risk.

METHODS

We used the Karolinska Mammography Project for Risk Prediction of Breast Cancer cohort of Swedish women (N = 43 810) aged 30-79 years with full access to BC risk factors and mammograms. MD was measured as dense area (cm2) and percent MD using the STRATUS method. We used the contralateral mammogram for women with BC and randomly selected a mammogram from either left or right breast for healthy women. We calculated relative area MDC between repeated examinations. Relative area MDC was categorized as decreased (>10% decrease per year), stable (no change), or increased (>10% increase per year). We used Cox proportional hazards regression to estimate the association of BC with MDC and interaction analysis to investigate how MDC modified the association between baseline MD and BC risk. All tests of statistical significance were two-sided.

RESULTS

In all, 563 women were diagnosed with BC. Compared with women with a decreased MD over time, no statistically significant difference in BC risk was seen for women with either stable MD or increasing MD (hazard ratio = 1.01, 95% confidence interval = 0.82 to 1.23, P = .90; and hazard ratio = 0.98, 95% confidence interval = 0.80 to 1.22, P = .90, respectively). Categorizing baseline MD and subsequently adding MDC did not seem to influence the association between baseline MD and BC risk.

CONCLUSIONS

Our results suggest that annual MDC does not influence BC risk. Furthermore, MDC does not seem to influence the association between baseline MD and BC risk.

Mammographic breast density, its changes, and breast cancer risk in premenopausal and postmenopausal women

BACKGROUND

The risk of breast cancer related to changes in breast density over time, including its regression and persistence, remains controversial. The authors investigated the relationship between breast density and its changes over time with the development of breast cancer in premenopausal and postmenopausal women.

METHODS

The current cohort study included 74,249 middle-aged Korean women (aged ≥35 years) who were free of breast cancer at baseline and who underwent repeated screening mammograms. Mammographic breast density was categorized according to the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS). A dense breast was defined as heterogeneously dense or extremely dense, and changes in dense breasts between baseline and subsequent follow-up were classified as none, developed, regressed, or persistent dense breast.

RESULTS

During a median follow-up of 6.1 years (interquartile range, 4.1-8.8 years), a total of 803 incident breast cancers were identified. Baseline breast density was found to be positively associated with incident breast cancer in a dose-response manner, and this association did not significantly differ by menopausal status. The multivariable-adjusted hazard ratios (HRs) for breast cancer comparing "heterogeneously dense" and "extremely dense" categories with the nondense category were 1.96 (95% confidence interval [95% CI], 1.40-2.75) and 2.86 (95% CI, 2.04-4.01), respectively. With respect to changes in dense breasts over time, multivariable-adjusted HRs for breast cancer comparing persistent dense breast with none were 2.37 (95% CI, 1.34-4.21) in premenopausal women and 3.61 (95% CI, 1.78-7.30) in postmenopausal women.

CONCLUSIONS

Both baseline dense breasts and their persistence over time were found to be strongly associated with an increased risk of incident breast cancer in premenopausal and postmenopausal women.

LAY SUMMARY

Both baseline breast density and its changes over time were found to be independently associated with the risk of breast cancer in both premenopausal and postmenopausal women. The risk of incident breast cancer increased in women with persistent dense breasts, whereas the breast cancer risk decreased as dense breasts regressed. The findings of the current study support that both dense breasts at baseline and their persistence over time are independent risk factors for developing breast cancer.

"Are changes in breast density during the menstrual cycle relevant? To what?"

PURPOSE

Cancers can be hidden by high breast density (BDen)- the masking effect (ME). BDen is also a modifiable and highly prevalent breast cancer risk (BCR) factor. The purposes of this study were to determine how much glandular volume (GVol), breast volume (BVol) and their ratio: BDen change during the menstrual cycle, and if these changes could affect ME or be relevant to results of interventional studies aiming to diminish BCR using these parameters as surrogates.

METHODS

We retrieved GVol, BVol and BDen data values obtained from 39,997 right mammograms performed with photon counting technique of 19,904 premenopausal women who reported their first day of last menses (FDLM). Many women had more than one study included over the years (with a different FDLM) but were not studied longitudinally. We segregated women by age (yearly), divided the menstrual cycle in 4 weeks, and assigned results with respect to the FDLM.

RESULTS

All parameters vary cyclically, with higher values in week 4 (GVol and BDen) or week 1 (BVol). Mean inter-week differences were very small for the three parameters, and diminished with age. However, especially in the youngest women, inter-week differences could be more than 10% for BDen, 15% for GVol, and 50% for BVol.

CONCLUSION

Small inter-week mean differences almost certainly rule out relevant changes to ME directly attributable to BDen. However, the possibility of large differences during the menstrual cycle in younger women, who are the ideal targets of interventional studies to diminish BCR, might distort results and should be accounted for.

Changes in breast density over serial mammograms: A case-control study

PURPOSE

In addition to a breast density category, temporal changes in breast density have gained attention as a dynamic breast cancer risk marker. This case-control study aimed to investigate a potential change in breast density preceding tumor development and the relationship of this potential change to prognostic pathological tumor variables.

METHOD

A total of 51 consecutive, eligible-for-analyses, biopsy-proven breast cancers were diagnosed between 1 st of August and 31 st of December 2014 at Skåne University Hospital, Sweden. Mammogram data and patient- and tumor characteristics were retrieved retrospectively from medical charts. Breast density was quantitatively estimated using LIBRA (a free open source software package). The cases were matched for year of birth, number of screening rounds, and date for first and last mammograms with controls from the Malmö Breast Tomosynthesis Screening Trial in a 1:2 ratio, resulting in median time between mammograms of 4.5 (1.3-11.9) years for cases and 4.7 (1.4-11.1) years for controls, averaging approximately three screening rounds (1-6 rounds).

RESULTS

We detected a statistically significant difference in breast density change over time, with cases showing an increase in breast density (1.7 %) as compared to controls (-0.3 %) (p = 0.045). We found that in women with breast cancer, older women (≥ 55 years) experienced a higher breast density increase compared to younger women (5.1 % vs. 0.3 %, p = 0.002).

CONCLUSIONS

There was a statistically significant difference in density change, where women with breast cancer showed an increased density over time, which was particularly evident in women > 55 years of age.

Mammographic Breast Density Assessed with Fully Automated Method and its Risk for Breast Cancer

Objectives:

We evaluated the association between breast cancer and breast density (BD) measured using fully automated software. We also evaluated the performance of cancer risk models such as only clinical risk factors, density related measures, and both clinical risk factors and density-related measures for determining cancer risk.

Materials and Methods:

This is a retrospective case–control study. The data were collected from August 2015 to December 2018. Two hundred fifty women with breast cancer and 400 control subjects were included in this study. We evaluated the BD qualitatively using breast imaging-reporting and data system density and quantitatively using 3D slicer. We also collected clinical factors such as age, familial history of breast cancer, menopausal status, number of births, body mass index, and hormonal replacement therapy use. We calculated the odds ratio (OR) for BD to determine the risk of breast cancer. We performed receiver operating characteristic (ROC) curve to assess the performance of cancer risk models.

Results:

The OR for the percentage BD for second, third, and fourth quartiles was 1.632 (95% confidence intervals [CI]: 1.102–2.416), 2.756 (95% CI: 1.704–4.458), and 3.163 (95% CI: 1.356–5.61). The area under ROC curve for clinical risk factors only, mammographic density measures, combined mammographic, and clinical risk factors was 0.578 (95% CI: 0.45, 0.64), 0.684 (95% CI: 0.58, 0.75), and 0.724 (95% CI: 0.64, 0.80), respectively.

Conclusion:

Mammographic BD was found to be positively associated with breast cancer. The density related measures combined clinical risk factors, and density model had good discriminatory power in identifying the cancer risk.

Automated volumetric breast density measures: differential change between breasts in women with and without breast cancer

BACKGROUND

Given that breast cancer and normal dense fibroglandular tissue have similar radiographic attenuation, we examine whether automated volumetric density measures identify a differential change between breasts in women with cancer and compare to healthy controls.

METHODS

Eligible cases (n = 1160) had unilateral invasive breast cancer and bilateral full-field digital mammograms (FFDMs) at two time points: within 2 months and 1-5 years before diagnosis. Controls (n = 2360) were matched to cases on age and date of FFDMs. Dense volume (DV) and volumetric percent density (VPD) for each breast were assessed using Volpara™. Differences in DV and VPD between mammograms (median 3 years apart) were calculated per breast separately for cases and controls and their difference evaluated by using the Wilcoxon signed-rank test. To simulate clinical practice where cancer laterality is unknown, we examined whether the absolute difference between breasts can discriminate cases from controls using area under the ROC curve (AUC) analysis, adjusting for age, BMI, and time.

RESULTS

Among cases, the VPD and DV between mammograms of the cancerous breast decreased to a lesser degree (- 0.26% and - 2.10 cm³) than the normal breast (- 0.39% and - 2.74 cm³) for a difference of 0.13% (p value < 0.001) and 0.63 cm³ (p = 0.002), respectively. Among controls, the differences between breasts were nearly identical for VPD (- 0.02 [p = 0.92]) and DV (0.05 [p = 0.77]). The AUC for discriminating cases from controls using absolute difference between breasts was 0.54 (95% CI 0.52, 0.56) for VPD and 0.56 (95% CI, 0.54, 0.58) for DV.

CONCLUSION

There is a small relative increase in volumetric density measures over time in the breast with cancer which is not found in the normal breast. However, the magnitude of this difference is small, and this measure alone does not appear to be a good discriminator between women with and without breast cancer.

Changes in mammographic density over time and the risk of breast cancer: An observational cohort study

BACKGROUND

The effect of changes in mammographic density over time on the risk of breast cancer remains inconclusive.

METHODS

We used information from four centres of the Breast Cancer Screening Program in Spain in the period 1996-2015. We analysed individual level data from 117,388 women first screened age 50-54, with at least two screening examinations. Breast density was determined using the BI-RADS classification (A to D in increasing order) at earliest and latest screening examination. Adjusted Poisson regression models were used to estimate the relative risk (RR) and 95% confidence intervals (95%CI) of the association between changes in mammographic density and breast cancer risk over time.

RESULTS

During an average 5.8 years of follow-up, 1592 (1.36%) women had a breast cancer diagnosis. An increase in density category increased breast cancer risk, and a decrease in density decreased the risk, compared with women who remained in the same BI-RADS category. Women whose density category increased from B to C or B to D had a RR of 1.55 (95%CI = 1.24-1.94) and 2.32 (95%CI = 1.48-3.63), respectively. The RR for women whose density increased from C to D was 1.51 (95%CI = 1.03-2.22). Changes in BI-RADS density were similarly associated with the risk for invasive cancer than for ductal carcinoma in situ.

CONCLUSIONS

Although a modest proportion of women changed BI-RADS density category, mammographic density changes modulated the risk of breast cancer and identified women at a differential risk. Using two longitudinal measures of BI-RADS density could help target women for risk-based screening strategies.

Multimodality Imaging of Breast Parenchymal Density and Correlation with Risk Assessment

Purpose of Review

Breast density, or the amount of fibroglandular tissue in the breast, has become a recognized and independent marker for breast cancer risk. Public awareness of breast density as a possible risk factor for breast cancer has resulted in legislation for risk stratification purposes in many US states. This review will provide a comprehensive overview of the currently available imaging modalities for qualitative and quantitative breast density assessment and the current evidence on breast density and breast cancer risk assessment.

Recent Findings

To date, breast density assessment is mainly performed with mammography and to some extent with magnetic resonance imaging. Data indicate that computerized, quantitative techniques in comparison with subjective visual estimations are characterized by higher reproducibility and robustness.

Summary

Breast density reduces the sensitivity of mammography due to a masking effect and is also a recognized independent risk factor for breast cancer. Standardized breast density assessment using automated volumetric quantitative methods has the potential to be used for risk prediction and stratification and in determining the best screening plan for each woman.

Density and tailored breast cancer screening: practice and prediction - an overview

Mammography, as the primary screening modality, has facilitated a substantial decrease in breast cancer-related mortality in the general population. However, the sensitivity of mammography for breast cancer detection is decreased in women with higher breast densities, which is an independent risk factor for breast cancer. With increasing public awareness of the implications of a high breast density, there is an increasing demand for supplemental screening in these patients. Yet, improvements in breast cancer detection with supplemental screening methods come at the expense of increased false-positives, recall rates, patient anxiety, and costs. Therefore, breast cancer screening practice must change from a general one-size-fits-all approach to a more personalized, risk-based one that is tailored to the individual woman’s risk, personal beliefs, and preferences, while accounting for cost, potential harm, and benefits.

This overview will provide an overview of the available breast density assessment modalities, the current breast density screening recommendations for women at average risk of breast cancer, and supplemental methods for breast cancer screening. In addition, we will provide a look at the possibilities for a risk-adapted breast cancer screening.

Quantitative background parenchymal uptake on molecular breast imaging and breast cancer risk: a case-control study

BACKGROUND

Background parenchymal uptake (BPU), which refers to the level of Tc-99m sestamibi uptake within normal fibroglandular tissue on molecular breast imaging (MBI), has been identified as a breast cancer risk factor, independent of mammographic density. Prior analyses have used subjective categories to describe BPU. We evaluate a new quantitative method for assessing BPU by testing its reproducibility, comparing quantitative results with previously established subjective BPU categories, and determining the association of quantitative BPU with breast cancer risk.

METHODS

Two nonradiologist operators independently performed region-of-interest analysis on MBI images viewed in conjunction with corresponding digital mammograms. Quantitative BPU was defined as a unitless ratio of the average pixel intensity (counts/pixel) within the fibroglandular tissue versus the average pixel intensity in fat. Operator agreement and the correlation of quantitative BPU measures with subjective BPU categories assessed by expert radiologists were determined. Percent density on mammograms was estimated using Cumulus. The association of quantitative BPU with breast cancer (per one unit BPU) was examined within an established case-control study of 62 incident breast cancer cases and 177 matched controls.

RESULTS

Quantitative BPU ranged from 0.4 to 3.2 across all subjects and was on average higher in cases compared to controls (1.4 versus 1.2, p < 0.007 for both operators). Quantitative BPU was strongly correlated with subjective BPU categories (Spearman's r = 0.59 to 0.69, p < 0.0001, for each paired combination of two operators and two radiologists). Interoperator and intraoperator agreement in the quantitative BPU measure, assessed by intraclass correlation, was 0.92 and 0.98, respectively. Quantitative BPU measures showed either no correlation or weak negative correlation with mammographic percent density. In a model adjusted for body mass index and percent density, higher quantitative BPU was associated with increased risk of breast cancer for both operators (OR = 4.0, 95% confidence interval (CI) 1.6-10.1, and 2.4, 95% CI 1.2-4.7).

CONCLUSION

Quantitative measurement of BPU, defined as the ratio of average counts in fibroglandular tissue relative to that in fat, can be reliably performed by nonradiologist operators with a simple region-of-interest analysis tool. Similar to results obtained with subjective BPU categories, quantitative BPU is a functional imaging biomarker of breast cancer risk, independent of mammographic density and hormonal factors.

PORTUGUESE STUDY OF MEAN GLANDULAR DOSE IN MAMMOGRAPHY AND COMPARISON WITH EUROPEAN REFERENCES

To characterise the mean glandular dose (MGD) in a sample of healthcare providers for digital mammography in Portugal. To compare the achieved values with European references. The MGD was measured on a poly-methyl-methacrylate phantom (45 mm) for each system using dosimeters. In addition, MGD was estimated using exposure settings collected from mammography exams in clinical context. Data were collected from 25 computed-radiography systems (CR) and 13 integrated digital (DR). For both measurements (phantom and clinical exposures), the average MGD for CR was higher compared to the DR. For CR the mean MGD was 1.85 mGy (CC projection) and 2.10 mGy (MLO projection). For DR systems the corresponding values were 1.54 mGy (CC) and 1.68 mGy (MLO). The average MGD obtained using both methods and for both technologies is within the acceptable reference range proposed by European guidelines (<2.5 mGy). Dose Reference Levels implementation should be the next step to optimise mammography practice in Portugal.

Does mammographic density mediate risk factor associations with breast cancer? An analysis by tumor characteristics

BACKGROUND

Though mammographic density (MD) has been proposed as an intermediate marker of breast cancer risk, few studies have examined whether the associations between breast cancer risk factors and risk are mediated by MD, particularly by tumor characteristics.

METHODS

Our study population included 3392 cases (1105 premenopausal) and 8882 (3192 premenopausal) controls from four case-control studies. For established risk factors, we estimated the percent of the total risk factor association with breast cancer that was mediated by percent MD (secondarily, by dense area and non-dense area) for invasive breast cancer as well as for subtypes defined by the estrogen receptor (ER+/ER-), progesterone receptor (PR+/PR-), and HER2 (HER2+/HER2-). Analyses were conducted separately in pre- and postmenopausal women.

RESULTS

Positive associations between prior breast biopsy and risk of invasive breast cancer as well as all subtypes were partially mediated by percent MD in pre- and postmenopausal women (percent mediated = 11-27%, p ≤ 0.02). In postmenopausal women, nulliparity and hormone therapy use were positively associated with invasive, ER+ , PR+ , and HER2- breast cancer; percent MD partially mediated these associations (percent mediated ≥ 31%, p ≤ 0.02). Further, among postmenopausal women, percent MD partially mediated the positive association between later age at first birth and invasive as well as ER+ breast cancer (percent mediated = 16%, p ≤ 0.05).

CONCLUSION

Percent MD partially mediated the associations between breast biopsy, nulliparity, age at first birth, and hormone therapy with risk of breast cancer, particularly among postmenopausal women, suggesting that these risk factors at least partially influence breast cancer risk through changes in breast tissue composition.

Quantitative breast density analysis using tomosynthesis and comparison with MRI and digital mammography

BACKGROUND AND OBJECTIVE

Breast density at mammography has been used as markers of breast cancer risk. However, newly introduced tomosynthesis and computer-aided quantitative method could provide more reliable breast density evaluation.

METHODS

In the experiment, 98 tomosynthesis image volumes were obtained from 98 women. For each case, an automatic skin removal was used and followed by a fuzzy c-mean (FCM) classifier which separated the fibroglandular tissues from other tissues in breast area. Finally, percent of breast density and breast volume were calculated and the results were compared with MRI. In addition, the percent of breast density and breast area of digital mammography calculated using the software Cumulus (University of Toronto, Toronto, ON, Canada.) were also compared with 3-D modalities.

RESULTS

Percent of breast density and breast volume, which were computed from tomosynthesis, MRI and digital mammography were 17.37% ± 4.39% and 607.12 cm³ ± 323.01 cm³, 20.3% ± 8.6% and 537.59 cm³ ± 287.74 cm³, and 12.03% ± 4.08%, respectively. There were significant correlations on breast density as well as volume between tomosynthesis and MRI (R = 0.482 and R = 0.805), tomosynthesis and breast density with breast area of digital mammography (R = 0.789 and R = 0.877), and MRI and breast density with breast area of digital mammography (R = 0.482 and R = 0.857) (all P values < .001).

CONCLUSIONS

Breast density and breast volume evaluated from tomosynthesis, MRI and breast density and breast area of digital mammographic images have significant correlations and indicate that tomosynthesis could provide useful 3-D information on breast density through proposed method.

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.

Accuracy of fully automated, quantitative, volumetric measurement of the amount of fibroglandular breast tissue using MRI: correlation with anthropomorphic breast phantoms

To demonstrate the accuracy of fully automated, quantitative, volumetric measurement of the amount of fibroglandular breast tissue (FGT), using MRI, and to investigate the impact of different MRI sequences using anthropomorphic breast phantoms as the ground truth. In this study, 10 anthropomorphic breast phantoms that consisted of different known fractions of adipose and protein tissue, which closely resembled normal breast parenchyma, were developed. Anthropomorphic breast phantoms were imaged with a 1.5 T unit (Siemens, Avantofit) using an 18-channel breast coil. The sequence protocol consisted of an isotropic Dixon sequence (Di), an anisotropic Dixon sequence (Da), and T₁ 3D FLASH sequences with and without fat saturation (T1). Fully automated, quantitative, volumetric measurement of FGT for all anthropomorphic phantoms and sequences was performed and correlated with the amounts of fatty and protein components in the phantoms as the ground truth. Fully automated, quantitative, volumetric measurements of FGT with MRI for all sequences ranged from 5.86 to 61.05% (mean 33.36%). The isotropic Dixon sequence yielded the highest accuracy (median 0.51%-0.78%) and precision (median range 0.19%) compared with anisotropic Dixon (median 1.92%-2.09%; median range 0.55%) and T₁ -weighted sequences (median 2.54%-2.46%; median range 0.82%). All sequences yielded good correlation with the FGT content of the anthropomorphic phantoms. The best correlation of FGT measurements was identified for Dixon sequences (Di, R²  = 0.999; Da, R²  = 0.998) compared with conventional T₁ -weighted sequences (R²  = 0.971). MRI yields accurate, fully automated, quantitative, volumetric measurements of FGT, an increasingly important and sensitive imaging biomarker for breast cancer risk. Compared with conventional T₁ sequences, Dixon-type sequences show the highest correlation and reproducibility for automated, quantitative, volumetric FGT measurements using anthropomorphic breast phantoms as the ground truth.

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.

Breast density estimation from high spectral and spatial resolution MRI

A three-dimensional breast density estimation method is presented for high spectral and spatial resolution (HiSS) MR imaging. Twenty-two patients were recruited (under an Institutional Review Board--approved Health Insurance Portability and Accountability Act-compliant protocol) for high-risk breast cancer screening. Each patient received standard-of-care clinical digital x-ray mammograms and MR scans, as well as HiSS scans. The algorithm for breast density estimation includes breast mask generating, breast skin removal, and breast percentage density calculation. The inter- and intra-user variabilities of the HiSS-based density estimation were determined using correlation analysis and limits of agreement. Correlation analysis was also performed between the HiSS-based density estimation and radiologists' breast imaging-reporting and data system (BI-RADS) density ratings. A correlation coefficient of 0.91 ([Formula: see text]) was obtained between left and right breast density estimations. An interclass correlation coefficient of 0.99 ([Formula: see text]) indicated high reliability for the inter-user variability of the HiSS-based breast density estimations. A moderate correlation coefficient of 0.55 ([Formula: see text]) was observed between HiSS-based breast density estimations and radiologists' BI-RADS. In summary, an objective density estimation method using HiSS spectral data from breast MRI was developed. The high reproducibility with low inter- and low intra-user variabilities shown in this preliminary study suggest that such a HiSS-based density metric may be potentially beneficial in programs requiring breast density such as in breast cancer risk assessment and monitoring effects of therapy.

Mammographically dense human breast tissue stimulates MCF10DCIS.com progression to invasive lesions and metastasis

Background

High mammographic density (HMD) not only confers a significantly increased risk of breast cancer (BC) but also is associated with BCs of more advanced stages. However, it is unclear whether BC progression and metastasis are stimulated by HMD. We investigated whether patient-derived HMD breast tissue could stimulate the progression of MCF10DCIS.com cells compared with patient-matched low mammographic density (LMD) tissue.

Methods

Sterile breast specimens were obtained immediately after prophylactic mastectomy from high-risk women (n = 10). HMD and LMD regions of each specimen were resected under radiological guidance. Human MCF10DCIS.com cells, a model of ductal carcinoma in situ (DCIS), were implanted into silicone biochambers in the groins of severe combined immunodeficiency mice, either alone or with matched LMD or HMD tissue (1:1), and maintained for 6 weeks. We assessed biochamber weight as a measure of primary tumour growth, histological grade of the biochamber material, circulating tumour cells and metastatic burden by luciferase and histology. All statistical tests were two-sided.

Results

HMD breast tissue led to increased primary tumour take, increased biochamber weight and increased proportions of high-grade DCIS and grade 3 invasive BCs compared with LMD. This correlated with an increased metastatic burden in the mice co-implanted with HMD tissue.

Conclusions

Our study is the first to explore the direct effect of HMD and LMD human breast tissue on the progression and dissemination of BC cells in vivo. The results suggest that HMD status should be a consideration in decision-making for management of patients with DCIS lesions.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-016-0767-4) contains supplementary material, which is available to authorized users.

Bayesian joint ordinal and survival modeling for breast cancer risk assessment

We propose a joint model to analyze the structure and intensity of the association between longitudinal measurements of an ordinal marker and time to a relevant event. The longitudinal process is defined in terms of a proportional‐odds cumulative logit model. Time‐to‐event is modeled through a left‐truncated proportional‐hazards model, which incorporates information of the longitudinal marker as well as baseline covariates. Both longitudinal and survival processes are connected by means of a common vector of random effects.

General inferences are discussed under the Bayesian approach and include the posterior distribution of the probabilities associated to each longitudinal category and the assessment of the impact of the baseline covariates and the longitudinal marker on the hazard function. The flexibility provided by the joint model makes possible to dynamically estimate individual event‐free probabilities and predict future longitudinal marker values.

The model is applied to the assessment of breast cancer risk in women attending a population‐based screening program. The longitudinal ordinal marker is mammographic breast density measured with the Breast Imaging Reporting and Data System (BI‐RADS) scale in biennial screening exams. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Background parenchymal uptake on molecular breast imaging as a breast cancer risk factor: a case-control study

BACKGROUND

Molecular breast imaging (MBI) is a functional test used for supplemental screening of women with mammographically dense breasts. Additionally, MBI depicts variable levels of background parenchymal uptake (BPU) within nonmalignant, dense fibroglandular tissue. We investigated whether BPU is a risk factor for breast cancer.

METHODS

We conducted a retrospective case-control study of 3027 eligible women who had undergone MBI between February 2004 and February 2014. Sixty-two incident breast cancer cases were identified. A total of 179 controls were matched on age, menopausal status, and MBI year. Two radiologists blinded to case status independently assessed BPU as one of four categories: photopenic, minimal to mild, moderate, or marked. Conditional logistic regression analysis was performed to estimate the associations (OR) of BPU categories (moderate or marked vs. minimal to mild or photopenic) and breast cancer risk, adjusted for other risk factors.

RESULTS

The median age was 60.2 years (range 38-86 years) for cases vs. 60.2 years (range 38-88 years) for controls (p = 0.88). Women with moderate or marked BPU had a 3.4-fold (95 % CI 1.6-7.3) and 4.8-fold (95 % CI 2.1-10.8) increased risk of breast cancer, respectively, compared with women with photopenic or minimal to mild BPU, for two radiologists. The results were similar after adjustment for BI-RADS density (OR 3.3 [95 % CI 1.6-7.2] and OR 4.6 [95 % CI 2.1-10.5]) or postmenopausal hormone use (OR 3.6 [95 % CI 1.7-7.7] and OR 5.0 [95 % CI 2.2-11.4]). The association of BPU with breast cancer remained in analyses limited to postmenopausal women only (OR 3.8 [95 % CI 1.5-9.3] and OR 4.1 [95 % CI 1.6-10.2]) and invasive breast cancer cases only (OR 3.6 [95 % CI 1.5-8.8] and OR 4.4 [95 % CI 1.7-11.1]). Variable BPU was observed among women with similar mammographic density; the distribution of BPU categories differed across density categories (p < 0.0001).

CONCLUSIONS

This study provides the first evidence for BPU as a risk factor for breast cancer. Among women with dense breasts, who comprise >40 % of the screening population, BPU may serve as a functional imaging biomarker to identify the subset at greatest risk.

Assessing within-woman changes in mammographic density: a comparison of fully versus semi-automated area-based approaches

BACKGROUND

Mammographic density (MD) varies throughout a woman's life. We compared the performance of a fully automated (ImageJ-based) method to the observer-dependent Cumulus approach in the assessment of within-woman changes in MD over time.

METHODS

MD was assessed in annual pre-diagnostic films (from age 40 to early 50s) from 313 breast cancer cases and 452 matched controls using Cumulus (left medio-lateral oblique (MLO) readings) and the ImageJ-based method (mean left-right MLO readings). Linear mixed models were used to compare within-woman changes in MD among controls. Associations between individual-specific MD trajectories and breast cancer were examined using conditional logistic regression.

RESULTS

The age-related trajectories predicted by Cumulus and the ImageJ-based method were similar for all MD measures, except that the ImageJ-based method yielded slightly higher (by 2.54%, 95% CI 2.07%, 3.00%) estimates for percent MD. For both methods, the yearly rate of change in percent MD was twice faster after menopause than before, and higher BMI was associated with lower mean percent MD, but not associated with rate of change. Both methods yielded similar associations of individual-specific MD trajectories with breast cancer risk.

CONCLUSIONS

The ImageJ-based method is a valid fully automated alternative to Cumulus for measuring within-woman changes in MD in digitized films. The Age Trial is registered as an International Standard Randomized Controlled Trial, number ISRCTN24647151.

Automated 3D ultrasound image segmentation to aid breast cancer image interpretation

Segmentation of an ultrasound image into functional tissues is of great importance to clinical diagnosis of breast cancer. However, many studies are found to segment only the mass of interest and not all major tissues. Differences and inconsistencies in ultrasound interpretation call for an automated segmentation method to make results operator-independent. Furthermore, manual segmentation of entire three-dimensional (3D) ultrasound volumes is time-consuming, resource-intensive, and clinically impractical. Here, we propose an automated algorithm to segment 3D ultrasound volumes into three major tissue types: cyst/mass, fatty tissue, and fibro-glandular tissue. To test its efficacy and consistency, the proposed automated method was employed on a database of 21 cases of whole breast ultrasound. Experimental results show that our proposed method not only distinguishes fat and non-fat tissues correctly, but performs well in classifying cyst/mass. Comparison of density assessment between the automated method and manual segmentation demonstrates good consistency with an accuracy of 85.7%. Quantitative comparison of corresponding tissue volumes, which uses overlap ratio, gives an average similarity of 74.54%, consistent with values seen in MRI brain segmentations. Thus, our proposed method exhibits great potential as an automated approach to segment 3D whole breast ultrasound volumes into functionally distinct tissues that may help to correct ultrasound speed of sound aberrations and assist in density based prognosis of breast cancer.

Comparison of Clinical and Automated Breast Density Measurements: Implications for Risk Prediction and Supplemental Screening

Purpose To compare the classification of breast density with two automated methods, Volpara (version 1.5.0; Matakina Technology, Wellington, New Zealand) and Quantra (version 2.0; Hologic, Bedford, Mass), with clinical Breast Imaging Reporting and Data System (BI-RADS) density classifications and to examine associations of these measures with breast cancer risk. Materials and Methods In this study, 1911 patients with breast cancer and 4170 control subjects matched for age, race, examination date, and mammography machine were evaluated. Participants underwent mammography at Mayo Clinic or one of four sites within the San Francisco Mammography Registry between 2006 and 2012 and provided informed consent or a waiver for research, in compliance with HIPAA regulations and institutional review board approval. Digital mammograms were retrieved a mean of 2.1 years (range, 6 months to 6 years) before cancer diagnosis, with the corresponding clinical BI-RADS density classifications, and Volpara and Quantra density estimates were generated. Agreement was assessed with weighted κ statistics among control subjects. Breast cancer associations were evaluated with conditional logistic regression, adjusted for age and body mass index. Odds ratios, C statistics, and 95% confidence intervals (CIs) were estimated. Results Agreement between clinical BI-RADS density classifications and Volpara and Quantra BI-RADS estimates was moderate, with κ values of 0.57 (95% CI: 0.55, 0.59) and 0.46 (95% CI: 0.44, 0.47), respectively. Differences of up to 14% in dense tissue classification were found, with Volpara classifying 51% of women as having dense breasts, Quantra classifying 37%, and clinical BI-RADS assessment used to classify 43%. Clinical and automated measures showed similar breast cancer associations; odds ratios for extremely dense breasts versus scattered fibroglandular densities were 1.8 (95% CI: 1.5, 2.2), 1.9 (95% CI: 1.5, 2.5), and 2.3 (95% CI: 1.9, 2.8) for Volpara, Quantra, and BI-RADS classifications, respectively. Clinical BI-RADS assessment showed better discrimination of case status (C = 0.60; 95% CI: 0.58, 0.61) than did Volpara (C = 0.58; 95% CI: 0.56, 0.59) and Quantra (C = 0.56; 95% CI: 0.54, 0.58) BI-RADS classifications. Conclusion Automated and clinical assessments of breast density are similarly associated with breast cancer risk but differ up to 14% in the classification of women with dense breasts. This could have substantial effects on clinical practice patterns. (©) RSNA, 2015 Online supplemental material is available for this article.

Digital histologic analysis reveals morphometric patterns of age-related involution in breast epithelium and stroma

Complete age-related regression of mammary epithelium, often termed postmenopausal involution, is associated with decreased breast cancer risk. However, most studies have qualitatively assessed involution. We quantitatively analyzed epithelium, stroma, and adipose tissue from histologically normal breast tissue of 454 patients in the Normal Breast Study. High-resolution digital images of normal breast hematoxylin and eosin-stained slides were partitioned into epithelium, adipose tissue, and nonfatty stroma. Percentage area and nuclei per unit area (nuclear density) were calculated for each component. Quantitative data were evaluated in association with age using linear regression and cubic spline models. Stromal area decreased (P = 0.0002), and adipose tissue area increased (P < 0.0001), with an approximate 0.7% change in area for each component, until age 55 years when these area measures reached a steady state. Although epithelial area did not show linear changes with age, epithelial nuclear density decreased linearly beginning in the third decade of life. No significant age-related trends were observed for stromal or adipose nuclear density. Digital image analysis offers a high-throughput method for quantitatively measuring tissue morphometry and for objectively assessing age-related changes in adipose tissue, stroma, and epithelium. Epithelial nuclear density is a quantitative measure of age-related breast involution that begins to decline in the early premenopausal period.

Introduction of an automated user-independent quantitative volumetric magnetic resonance imaging breast density measurement system using the Dixon sequence: comparison with mammographic breast density assessment

OBJECTIVES

The purposes of this study were to introduce and assess an automated user-independent quantitative volumetric (AUQV) breast density (BD) measurement system on the basis of magnetic resonance imaging (MRI) using the Dixon technique as well as to compare it with qualitative and quantitative mammographic (MG) BD measurements.

MATERIALS AND METHODS

Forty-three women with normal mammogram results (Breast Imaging Reporting and Data System 1) were included in this institutional review board-approved prospective study. All participants were subjected to BD assessment with MRI using the following sequence with the Dixon technique (echo time/echo time, 6 milliseconds/2.45 milliseconds/2.67 milliseconds; 1-mm isotropic; 3 minutes 38 seconds). To test the reproducibility, a second MRI after patient repositioning was performed. The AUQV magnetic resonance (MR) BD measurement system automatically calculated percentage (%) BD. The qualitative BD assessment was performed using the American College of Radiology Breast Imaging Reporting and Data System BD categories. Quantitative BD was estimated semiautomatically using the thresholding technique Cumulus4. Appropriate statistical tests were used to assess the agreement between the AUQV MR measurements and to compare them with qualitative and quantitative MG BD estimations.

RESULTS

The AUQV MR BD measurements were successfully performed in all 43 women. There was a nearly perfect agreement of AUQV MR BD measurements between the 2 MR examinations for % BD (P < 0.001; intraclass correlation coefficient, 0.998) with no significant differences (P = 0.384). The AUQV MR BD measurements were significantly lower than quantitative and qualitative MG BD assessment (P < 0.001).

CONCLUSIONS

The AUQV MR BD measurement system allows a fully automated, user-independent, robust, reproducible, as well as radiation- and compression-free volumetric quantitative BD assessment through different levels of BD. The AUQV MR BD measurements were significantly lower than the currently used qualitative and quantitative MG-based approaches, implying that the current assessment might overestimate breast density with MG.

Increased vitamin D and calcium intake associated with reduced mammographic breast density among premenopausal women

Vitamin D has been identified as a weak protective factor for postmenopausal breast cancer (relative risk, ~0.9), whereas high breast density has been identified as a strong risk factor (relative risk, ~4-6). To test the hypothesis that there is an association between vitamin D intake, but not circulating vitamin D levels, and mammographic breast density among women in our study, we conducted a cross-sectional study of 165 screening mammography patients at Nashville General Hospital's Breast Health Center, a public facility serving medically indigent and underserved women. Dietary and total (dietary plus supplements) vitamin D and calcium intakes were estimated by the Harvard African American Food Frequency Questionnaire, and blood samples were analyzed for 25-hydroxyvitamin D. Average percent breast density for the left and right breasts combined was estimated from digitized films using an interactive thresholding method available through Cumulus software. After statistical adjustment for age, race, and body mass index, the results revealed that there were significant trends of decreasing breast density with increasing vitamin D and calcium intake among premenopausal but not among postmenopausal women. There was no association between serum vitamin D and breast density in premenopausal or postmenopausal women. Confirmation of our findings in larger studies may assist in clarifying the role of vitamin D in breast density.

A Review on Automatic Mammographic Density and Parenchymal Segmentation

Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk factors, such as mammographic densities and parenchymal patterns, have been established as biomarkers but have not been fully incorporated in the risk prediction models used for risk stratification in screening and/or measuring responsiveness to preventive approaches. Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment. This paper presents a comprehensive review of automatic mammographic tissue segmentation methodologies developed over the past two decades and the evidence for risk assessment/density classification using segmentation. The aim of this review is to analyse how engineering advances have progressed and the impact automatic mammographic tissue segmentation has in a clinical environment, as well as to understand the current research gaps with respect to the incorporation of image-based risk factors in non-image-based risk prediction models.

Background parenchymal uptake during molecular breast imaging and associated clinical factors

OBJECTIVE. The purposes of this study were to describe the prevalence of background parenchymal uptake categories observed at screening molecular breast imaging (MBI) and to examine the association of background parenchymal uptake with mammographic density and other clinical factors. MATERIALS AND METHODS. Adjunct MBI screening was performed for women with dense breasts on previous mammograms. Two radiologists reviewed images from the MBI examinations and subjectively categorized background parenchymal uptake into four groups: photopenic, minimal-mild, moderate, or marked. Women with breast implants or a personal history of breast cancer were excluded. The association between background parenchymal uptake categories and patient characteristics was examined with Kruskal-Wallis and chi-square tests as appropriate. RESULTS. In 1149 eligible participants, background parenchymal uptake was photopenic in 252 (22%), minimal-mild in 728 (63%), and moderate or marked in 169 (15%). The distribution of categories differed across BI-RADS density categories (p < 0.0001). In 164 participants with extremely dense breasts, background parenchymal uptake was photopenic in 72 (44%), minimal-mild in 55 (34%), and moderate or marked in 37 (22%). The moderate-marked group was younger on average, more likely to be premenopausal or perimenopausal, and more likely to be using postmenopausal hormone therapy than the photopenic or minimal-mild groups (p < 0.0001). CONCLUSION. Among women with similar-appearing mammographic density, background parenchymal uptake ranged from photopenic to marked. Background parenchymal uptake was associated with menopausal status and postmenopausal hormone therapy but not with premenopausal hormonal contraceptives, phase of menstrual cycle, or Gail model 5-year risk of breast cancer. Additional work is necessary to fully characterize the underlying cause of background parenchymal uptake and determine its utility in predicting subsequent risk of breast cancer.

Change of mammographic density predicts the risk of contralateral breast cancer--a case-control study

Introduction

Mammographic density is a strong risk factor for breast cancer, but it is unknown whether density at first breast cancer diagnosis and changes during follow-up influences risk of non-simultaneous contralateral breast cancer (CBC).

Methods

We collected mammograms for CBC-patients (cases, N = 211) and unilateral breast cancer patients (controls, N = 211), individually matched on age and calendar period of first breast cancer diagnosis, type of adjuvant therapy and length of follow-up (mean follow-up time: 8.25 years). The odds of CBC as a function of changes of density during follow-up were investigated using conditional logistic regression, adjusting for non-dense area at diagnosis.

Results

Patients who experienced ≥10% absolute decrease in percent density had a 55% decreased odds of CBC (OR = 0.45 95% CI: 0.24 to 0.84) relative to patients who had little or no change in density from baseline to first follow-up mammogram (mean = 1.6 (SD = 0.6) years after diagnosis), whereas among those who experienced an absolute increase in percent density we could not detect any effect on the odds of CBC (OR = 0.83 95% CI: 0.24 to 2.87).

Conclusion

Decrease of mammographic density within the first two years after first diagnosis is associated with a significantly reduced risk of CBC, this potential new risk predictor can thus contribute to decision-making in follow-up strategies and treatment.

Type 2 diabetes and mammographic breast density among underserved women

PURPOSE

We conducted a study of women recruited at Meharry Medical College, a historically black medical school, to investigate the relationship between diabetes and mammographic breast density.

METHODS

A total of 476 women completed in-person interviews, body measurements, and full-field digital mammograms on a Hologic mammography unit from December 2011 to February 2014. Average percent breast density for the left and right breasts combined was estimated using Quantra, an automated algorithm for volumetric assessment of breast tissue. The prevalence of type 2 diabetes was determined by self-report.

RESULTS

After adjustment for confounding variables, the mean percent breast density among premenopausal women with type 2 diabetes [[Formula: see text] 13.8 %, 95 % confidence interval (CI) 11.6-15.9] was nonsignificantly lower than that of women without type 2 diabetes ([Formula: see text] 15.9 %, 95 % CI 15.0-16.8) (p = 0.07); however, there was no association among postmenopausal women. The effect of type 2 diabetes in severely obese women (BMI ≥ 35) appeared to differ by menopausal status with a reduction in mean percent breast density in premenopausal women, but an increase in mean percent breast density in postmenopausal women which could have been due to chance.

CONCLUSIONS

Confirmation of our findings in larger studies may assist in clarifying the role of the insulin signaling breast cancer pathway in women with high breast density.