Mammographic density reduction is a prognostic marker of response to adjuvant tamoxifen therapy in postmenopausal patients with breast cancer

Migration History, Language Acculturation, and Mammographic Breast Density

Background: Breast cancer incidence is lower in many U.S. ethnic minority and foreign-born population groups. Investigating whether migration and acculturation patterns in risk are reflected in disease biomarkers may help to elucidate the underlying mechanisms.Methods: We compared the distribution of breast cancer risk factors across U.S.-born white, African American and Hispanic women, and foreign-born Hispanic women (n = 477, ages 40-64 years, 287 born in Caribbean countries). We used linear regression models to examine the associations of migration history and linguistic acculturation with mammographic breast density (MBD), measured using computer-assisted methods as percent and area of dense breast tissue.Results: The distribution of most breast cancer risk factors varied by ethnicity, nativity, and age at migration. In age- and body mass index-adjusted models, U.S.-born women did not differ in average MBD according to ethnicity, but foreign-born Hispanic women had lower MBD [e.g., -4.50%; 95% confidence interval (CI), -7.12 to -1.89 lower percent density in foreign- vs. U.S.-born Hispanic women]. Lower linguistic acculturation and lower percent of life spent in the United States were also associated with lower MBD [e.g., monolingual Spanish and bilingual vs. monolingual English speakers, respectively, had 5.09% (95% CI, -8.33 to -1.85) and 3.34% (95% CI, -6.57 to -0.12) lower percent density]. Adjusting for risk factors (e.g., childhood body size, parity) attenuated some of these associations.Conclusions: Hispanic women predominantly born in Caribbean countries have lower MBD than U.S.-born women of diverse ethnic backgrounds, including U.S.-born Hispanic women of Caribbean heritage.Impact: MBD may provide insight into mechanisms driving geographic and migration variations in breast cancer risk. Cancer Epidemiol Biomarkers Prev; 27(5); 566-74. ©2018 AACR.

Mammographic density: a potential monitoring biomarker for adjuvant and preventative breast cancer endocrine therapies

Increased mammographic density (MD) has been shown beyond doubt to be a marker for increased breast cancer risk, though the underpinning pathobiology is yet to be fully elucidated. Estrogenic activity exerts a strong influence over MD, which consequently has been observed to change predictably in response to tamoxifen anti-estrogen therapy, although results for other selective estrogen receptor modulators and aromatase inhibitors are less consistent. In both primary and secondary prevention settings, tamoxifen-associated MD changes correlate with successful modulation of risk or outcome, particularly among pre-menopausal women; an observation that supports the potential use of MD change as a surrogate marker where short-term MD changes reflect longer-term anti-estrogen efficacy. Here we summarize endocrine therapy-induced MD changes and attendant outcomes and discuss both the need for outcome surrogates in such therapy, as well as make a case for MD as such a monitoring marker. We then discuss the process and steps required to validate and introduce MD into practice as a predictor or surrogate for endocrine therapy efficacy in preventive and adjuvant breast cancer treatment settings.

A comprehensive tool for measuring mammographic density changes over time

Background

Mammographic density is a marker of breast cancer risk and diagnostics accuracy. Density change over time is a strong proxy for response to endocrine treatment and potentially a stronger predictor of breast cancer incidence. We developed STRATUS to analyse digital and analogue images and enable automated measurements of density changes over time.

Method

Raw and processed images from the same mammogram were randomly sampled from 41,353 healthy women. Measurements from raw images (using FDA approved software iCAD) were used as templates for STRATUS to measure density on processed images through machine learning. A similar two-step design was used to train density measures in analogue images. Relative risks of breast cancer were estimated in three unique datasets. An alignment protocol was developed using images from 11,409 women to reduce non-biological variability in density change. The protocol was evaluated in 55,073 women having two regular mammography screens. Differences and variances in densities were compared before and after image alignment.

Results

The average relative risk of breast cancer in the three datasets was 1.6 [95% confidence interval (CI) 1.3–1.8] per standard deviation of percent mammographic density. The discrimination was AUC 0.62 (CI 0.60–0.64). The type of image did not significantly influence the risk associations. Alignment decreased the non-biological variability in density change and re-estimated the yearly overall percent density decrease from 1.5 to 0.9%, p < 0.001.

Conclusions

The quality of STRATUS density measures was not influenced by mammogram type. The alignment protocol reduced the non-biological variability between images over time. STRATUS has the potential to become a useful tool for epidemiological studies and clinical follow-up.

Electronic supplementary material

The online version of this article (10.1007/s10549-018-4690-5) contains supplementary material, which is available to authorized users.

Review of quantitative multiscale imaging of breast cancer

Breast cancer is the most common cancer among women worldwide and ranks second in terms of overall cancer deaths. One of the difficulties associated with treating breast cancer is that it is a heterogeneous disease with variations in benign and pathologic tissue composition, which contributes to disease development, progression, and treatment response. Many of these phenotypes are uncharacterized and their presence is difficult to detect, in part due to the sparsity of methods to correlate information between the cellular microscale and the whole-breast macroscale. Quantitative multiscale imaging of the breast is an emerging field concerned with the development of imaging technology that can characterize anatomic, functional, and molecular information across different resolutions and fields of view. It involves a diverse collection of imaging modalities, which touch large sections of the breast imaging research community. Prospective studies have shown promising results, but there are several challenges, ranging from basic physics and engineering to data processing and quantification, that must be met to bring the field to maturity. This paper presents some of the challenges that investigators face, reviews currently used multiscale imaging methods for preclinical imaging, and discusses the potential of these methods for clinical breast imaging.

Stromal characteristics may hold the key to mammographic density: the evidence to date

There is strong epidemiological data indicating a role for increased mammographic density (MD) in predisposing to breast cancer, however, the biological mechanisms underlying this phenomenon are less well understood. Recently, studies of human breast tissues have started to characterise the features of mammographically dense breasts, and a number of in-vitro and in-vivo studies have explored the potential mechanisms through which dense breast tissue may exert this tumourigenic risk. This article aims to review both the pathological and biological evidence implicating a key role for the breast stromal compartment in MD, how this may be modified and the clinical significance of these findings. The epidemiological context will be briefly discussed but will not be covered in detail.

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.

The Short-Term Effect of Weight Loss Surgery on Volumetric Breast Density and Fibroglandular Volume

Purpose

Obesity and breast density are both associated with an increased risk of breast cancer and are potentially modifiable. Weight loss surgery (WLS) causes a significant reduction in the amount of body fat and a decrease in breast cancer risk. The effect of WLS on breast density and its components has not been documented. Here, we analyze the impact of WLS on volumetric breast density (VBD) and on each of its components (fibroglandular volume and breast volume) by using three-dimensional methods.

Materials and Methods

Fibroglandular volume, breast volume, and their ratio, the VBD, were calculated from mammograms before and after WLS by using Volpara™ automated software.

Results

For the 80 women included, average body mass index decreased from 46.0 ± 7.22 to 33.7 ± 7.06 kg/m². Mammograms were performed on average 11.6 ± 9.4 months before and 10.1 ± 7 months after WLS. There was a significant reduction in average breast volume (39.4 % decrease) and average fibroglandular volume (15.5 % decrease), and thus, the average VBD increased from 5.15 to 7.87 % (p < 1 × 10⁻⁹) after WLS. When stratified by menopausal status and diabetic status, VBD increased significantly in all groups but only perimenopausal and postmenopausal women and non-diabetics experienced a significant reduction in fibroglandular volume.

Conclusions

Breast volume and fibroglandular volume decreased, and VBD increased following WLS, with the most significant change observed in postmenopausal women and non-diabetics. Further studies are warranted to determine how physical and biological alterations in breast density components after WLS may impact breast cancer risk.

Mammographic Density Reduction as a Prognostic Marker for Postmenopausal Breast Cancer: Results Using a Joint Longitudinal-Survival Modeling Approach

Previous studies have linked reductions in mammographic density after a breast cancer diagnosis to an improved prognosis. These studies focused on short-term change, using a 2-stage process, treating estimated change as a fixed covariate in a survival model. We propose the use of a joint longitudinal-survival model. This enables us to model long-term trends in density while accounting for dropout as well as for measurement error. We studied the change in mammographic density after a breast cancer diagnosis and its association with prognosis (measured by cause-specific mortality), overall and with respect to hormone replacement therapy and tamoxifen treatment. We included 1,740 women aged 50–74 years, diagnosed with breast cancer in Sweden during 1993–1995, with follow-up until 2008. They had a total of 6,317 mammographic density measures available from the first 5 years of follow-up, including baseline measures. We found that the impact of the withdrawal of hormone replacement therapy on density reduction was larger than that of tamoxifen treatment. Unlike previous studies, we found that there was an association between density reduction and survival, both for tamoxifen-treated women and women who were not treated with tamoxifen.

Association between mammographic density and pregnancies relative to age and BMI: a breast cancer case-only analysis

PURPOSE

Percentage mammographic density (PMD) is a major risk factor for breast cancer (BC). It is strongly associated with body mass index (BMI) and age, which are themselves risk factors for breast cancer. This analysis investigated the association between the number of full-term pregnancies and PMD in different subgroups relative to age and BMI.

METHODS

Patients were identified in the breast cancer database of the University Breast Center for Franconia. A total of 2410 patients were identified, for whom information on parity, age, and BMI, and a mammogram from the time of first diagnosis were available for assessing PMD. Linear regression analyses were conducted to investigate the influence on PMD of the number of full-term pregnancies (FTPs), age, BMI, and interaction terms between them.

RESULTS

As in previous studies, age, number of FTPs, and BMI were found to be associated with PMD in the expected direction. However, including the respective interaction terms improved the prediction of PMD even further. Specifically, the association between PMD and the number of FTPs differed in young patients under the age of 45 (mean decrease of 0.37 PMD units per pregnancy) from the association in older age groups (mean decrease between 2.29 and 2.39 PMD units). BMI did not alter the association between PMD and the number of FTPs.

CONCLUSIONS

The effect of pregnancies on mammographic density does not appear to become apparent before the age of menopause. The mechanism that drives the effect of pregnancies on mammographic density appears to be counter-regulated by other influences on mammographic density in younger patients.

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.

Association Between Western and Mediterranean Dietary Patterns and Mammographic Density

OBJECTIVE

To examine the association between two dietary patterns (Western and Mediterranean), previously linked to breast cancer risk, and mammographic density.

METHODS

This cross-sectional study included 3,584 women attending population-based breast cancer screening programs and recruited between October 7, 2007, and July 14, 2008 (participation rate 74.5%). Collected data included anthropometric measurements; demographic, obstetric, and gynecologic characteristics; family and personal health history; and diet in the preceding year. Mammographic density was blindly assessed by a single radiologist and classified into four categories: less than 10%, 10-25%, 25-50%, and greater than 50%. The association between adherence to either a Western or a Mediterranean dietary pattern and mammographic density was explored using multivariable ordinal logistic regression models with random center-specific intercepts. Models were adjusted for age, body mass index, parity, menopause, smoking, family history, hormonal treatment, and calorie and alcohol intake. Differences according to women's characteristics were tested including interaction terms.

RESULTS

Women with a higher adherence to the Western dietary pattern were more likely to have high mammographic density (n=242 [27%]) than women with low adherence (n=169 [19%]) with a fully adjusted odds ratio (ORQ4vsQ1) of 1.25 (95% confidence interval [CI] 1.03-1.52). This association was confined to overweight-obese women (adjusted ORQ4vsQ1 [95% CI] 1.41 [1.13-1.76]). No association between Mediterranean dietary pattern and mammographic density was observed.

CONCLUSION

The Western dietary pattern was associated with increased mammographic density among overweight-obese women. Our results might inform specific dietary recommendations for women with high mammographic density.

Sample size and power determination when limited preliminary information is available

Background

We describe a novel strategy for power and sample size determination developed for studies utilizing investigational technologies with limited available preliminary data, specifically of imaging biomarkers. We evaluated diffuse optical spectroscopic imaging (DOSI), an experimental noninvasive imaging technique that may be capable of assessing changes in mammographic density. Because there is significant evidence that tamoxifen treatment is more effective at reducing breast cancer risk when accompanied by a reduction of breast density, we designed a study to assess the changes from baseline in DOSI imaging biomarkers that may reflect fluctuations in breast density in premenopausal women receiving tamoxifen.

Method

While preliminary data demonstrate that DOSI is sensitive to mammographic density in women about to receive neoadjuvant chemotherapy for breast cancer, there is no information on DOSI in tamoxifen treatment. Since the relationship between magnetic resonance imaging (MRI) and DOSI has been established in previous studies, we developed a statistical simulation approach utilizing information from an investigation of MRI assessment of breast density in 16 women before and after treatment with tamoxifen to estimate the changes in DOSI biomarkers due to tamoxifen.

Results

Three sets of 10,000 pairs of MRI breast density data with correlation coefficients of 0.5, 0.8 and 0.9 were simulated and generated and were used to simulate and generate a corresponding 5,000,000 pairs of DOSI values representing water, ctHHB, and lipid. Minimum sample sizes needed per group for specified clinically-relevant effect sizes were obtained.

Conclusion

The simulation techniques we describe can be applied in studies of other experimental technologies to obtain the important preliminary data to inform the power and sample size calculations.

Electronic supplementary material

The online version of this article (doi:10.1186/s12874-017-0329-1) contains supplementary material, which is available to authorized users.

Longitudinal Changes in Volumetric Breast Density with Tamoxifen and Aromatase Inhibitors

Background: Reductions in breast density with tamoxifen and aromatase inhibitors may be an intermediate marker of treatment response. We compare changes in volumetric breast density among breast cancer cases using tamoxifen or aromatase inhibitors (AI) to untreated women without breast cancer.Methods: Breast cancer cases with a digital mammogram prior to diagnosis and after initiation of tamoxifen (n = 366) or AI (n = 403) and a sample of controls (n = 2170) were identified from the Mayo Clinic Mammography Practice and San Francisco Mammography Registry. Volumetric percent density (VPD) and dense breast volume (DV) were measured using Volpara (Matakina Technology) and Quantra (Hologic) software. Linear regression estimated the effect of treatment on annualized changes in density.Results: Premenopausal women using tamoxifen experienced annualized declines in VPD of 1.17% to 1.70% compared with 0.30% to 0.56% for controls and declines in DV of 7.43 to 15.13 cm³ compared with 0.28 to 0.63 cm³ in controls, for Volpara and Quantra, respectively. The greatest reductions were observed among women with ≥10% baseline density. Postmenopausal AI users had greater declines in VPD than controls (Volpara P = 0.02; Quantra P = 0.03), and reductions were greatest among women with ≥10% baseline density. Declines in VPD among postmenopausal women using tamoxifen were only statistically greater than controls when measured with Quantra.Conclusions: Automated software can detect volumetric breast density changes among women on tamoxifen and AI.Impact: If declines in volumetric density predict breast cancer outcomes, these measures may be used as interim prognostic indicators. Cancer Epidemiol Biomarkers Prev; 26(6); 930-7. ©2017 AACR.

Ten-Year Survival after Multiple Invasive Melanomas Is Worse than after a Single Melanoma: a Population-Based Study

The prognosis of melanoma patients who are diagnosed with multiple primary lesions remains controversial. We used a large population-based cohort to re-examine this issue, applying a delayed entry methodology to avoid survival bias. Of 32,238 eligible patients diagnosed between 1995 and 2008, 29,908 (93%) had a single invasive melanoma, 2,075 (6%) had two, and 255 (1%) had three. Allowing for differences in entry time, 10-year cause-specific survival for these three groups was 89% (95% confidence interval [CI] = 88-90%), 83% (95% CI = 80-86%), and 67% (95% CI = 54-81%), respectively. After adjustment for key prognostic factors, the hazard ratio of death within 10 years from melanoma was two times higher for those with two melanomas (hazard ratio = 2.01, 95% CI = 1.57-2.59; P < 0.001) and nearly three times higher when three melanomas were diagnosed (hazard ratio = 2.91, 95% CI = 1.64-5.18; P < 0.001) compared with people with a single melanoma. Melanoma-specific mortality remained elevated after adjusting for maximum thickness or ulceration of any melanoma regardless of the index tumor. After appropriately accounting for the interval between diagnosis of the first and subsequent melanomas, patients with multiple invasive melanomas have significantly poorer survival than patients with a single invasive melanoma.

What we need to know about dense breasts: implications for breast cancer screening

High breast density and its relationship to the risk of breast cancer has become a hot topic in the medical literature and in the lay press, especially in the United States, where it has brought about changes in the legal framework that require radiologists to inform clinicians and patients about breast density. Radiologists, who are mainly responsible for this information, need to know the scientific evidence and controversies regarding this subject. The discussion is centered on the real importance of the risk, the limitation that not having standardized methods of measurement represents, and the possible application of complementary screening techniques (ultrasound, magnetic resonance imaging, or tomosynthesis) for which clear recommendations have yet to be established. We need controlled studies that evaluate the application of these techniques in women with dense breasts, including the possibility that they can lead to overdiagnosis.

Amount of stroma is associated with mammographic density and stromal expression of oestrogen receptor in normal breast tissues

Following female sex and age, mammographic density is considered one of the strongest risk factors for breast cancer. Despite the association between mammographic density and breast cancer risk, little is known about the underlying histology and biological basis of breast density. To better understand the mechanisms behind mammographic density we assessed morphology, proliferation and hormone receptor status in relation to mammographic density in breast tissues from healthy women. Tissues were obtained from 2012-2013 by ultrasound-guided core needle biopsy from 160 women as part of the Karma (Karolinska mammography project for risk prediction for breast cancer) project. Mammograms were collected through routine mammography screening and mammographic density was calculated using STRATUS. The histological composition, epithelial and stromal proliferation status and hormone receptor status were assessed through immunohistochemical staining. Higher mammographic density was significantly associated with a greater proportion of stromal and epithelial tissue and a lower proportion of adipose tissue. Epithelial expression levels of Ki-67, oestrogen receptor (ER) and progesterone receptor (PR) were not associated with mammographic density. Epithelial Ki-67 was associated with a greater proportion of epithelial tissue, and epithelial PR was associated with a greater proportion of stromal and a lower proportion of adipose tissue. Epithelial ER was not associated with any tissues. In contrast, expression of ER in the stroma was significantly associated with a greater proportion of stroma, and negatively associated with the amount of adipose tissue. High mammographic density is associated with higher amount of stroma and epithelium and less amount of fat, but is not associated with a change in epithelial proliferation or receptor status. Increased expressions of both epithelial PR and stromal ER are associated with a greater proportion of stroma, suggesting hormonal involvement in regulating breast tissue composition.

The implications of genetic variation for the pharmacokinetics and pharmacodynamics of aromatase inhibitors

INTRODUCTION

Breast cancer is the most common female cancer and remains a serious public health concern worldwide. Third-generation aromatase inhibitors (AIs) are widely used in postmenopausal women with estrogen receptor positive breast cancer. However, there is marked interindividual variability in terms of the efficacy and incidence of adverse events following treatment with AIs. Pharmacogenetics has the potential to predict clinical outcomes based on patients' genetic information, paving the way towards personalized treatment.

AREAS COVERED

This article reviews pharmacogenetic studies of AIs, including pharmacokinetic and pharmacodynamic aspects, highlighting those studies where the efficacy and adverse events of AIs have been examined using both candidate gene and genome-wide approaches.

EXPERT OPINION

Pharmacogenetics is a promising approach to develop personalized medicine with AIs. However, the application of pharmacogenetics to predict therapeutic efficacy and adverse events in breast cancer patients is still far from implementation in routine clinical practice. Large, comprehensive, multicenter studies that simultaneously evaluate multiple genes and pathways, including rare variants, are warranted in order to produce reliable and informative results. The ultimate aim is to develop clinically-relevant guidelines for breast cancer therapy.

Quantitative 3D breast magnetic resonance imaging fibroglandular tissue analysis and correlation with qualitative assessments: a feasibility study

BACKGROUND

The amount of fibroglandular tissue (FGT) has been linked to breast cancer risk based on mammographic density studies. Currently, the qualitative assessment of FGT on mammogram (MG) and magnetic resonance imaging (MRI) is prone to intra and inter-observer variability. The purpose of this study is to develop an objective quantitative FGT measurement tool for breast MRI that could provide significant clinical value.

METHODS

An IRB approved study was performed. Sixty breast MRI cases with qualitative assessment of mammographic breast density and MRI FGT were randomly selected for quantitative analysis from routine breast MRIs performed at our institution from 1/2013 to 12/2014. Blinded to the qualitative data, whole breast and FGT contours were delineated on T1-weighted pre contrast sagittal images using an in-house, proprietary segmentation algorithm which combines the region-based active contours and a level set approach. FGT (%) was calculated by: [segmented volume of FGT (mm(3))/(segmented volume of whole breast (mm(3))] ×100. Statistical correlation analysis was performed between quantified FGT (%) on MRI and qualitative assessments of mammographic breast density and MRI FGT.

RESULTS

There was a significant positive correlation between quantitative MRI FGT assessment and qualitative MRI FGT (r=0.809, n=60, P<0.001) and mammographic density assessment (r=0.805, n=60, P<0.001). There was a significant correlation between qualitative MRI FGT assessment and mammographic density assessment (r=0.725, n=60, P<0.001). The four qualitative assessment categories of FGT correlated with the calculated mean quantitative FGT (%) of 4.61% (95% CI, 0-12.3%), 8.74% (7.3-10.2%), 18.1% (15.1-21.1%), 37.4% (29.5-45.3%).

CONCLUSIONS

Quantitative measures of FGT (%) were computed with data derived from breast MRI and correlated significantly with conventional qualitative assessments. This quantitative technique may prove to be a valuable tool in clinical use by providing computer generated standardized measurements with limited intra or inter-observer variability.

Raised mammographic density: causative mechanisms and biological consequences

High mammographic density is the most important risk factor for breast cancer, after ageing. However, the composition, architecture, and mechanical properties of high X-ray density soft tissues, and the causative mechanisms resulting in different mammographic densities, are not well described. Moreover, it is not known how high breast density leads to increased susceptibility for cancer, or the extent to which it causes the genomic changes that characterise the disease. An understanding of these principals may lead to new diagnostic tools and therapeutic interventions.

Relationship Between Breast Density and Selective Estrogen-Receptor Modulators, Aromatase Inhibitors, Physical Activity, and Diet: A Systematic Review

Background Lower breast density (BD) is associated with lower risk of breast cancer and may serve as a biomarker for the efficacy of chemopreventive strategies. This review explores parameters that are thought to be associated with lower BD. We conducted a systematic review of articles published to date using the PRISMA strategy. Articles that assessed change in BD with estrogen-receptor modulators (tamoxifene [TAM], raloxifene [RLX], and tibolone) and aromatase inhibitors (AIs), as well as cross-sectional and longitudinal studies (LSs) that assessed association between BD and physical activity (PA) or diet were reviewed. Results Ten studies assessed change in BD with TAM; all reported TAM-mediated BD decreases. Change in BD with RLX was assessed by 11 studies; 3 reported a reduction in BD. Effect of tibolone was assessed by 5 RCTs; only 1 reported change in BD. AI-mediated BD reduction was reported by 3 out of 10 studies. The association between PA and BD was assessed by 21 studies; 4 reported an inverse association. The relationship between diet and BD was assessed in 34 studies. All studies on calcium and vitamin D as well as vegetable intake reported an inverse association with BD in premenopausal women. Two RCTs demonstrated BD reduction with a low-fat, high-carbohydrate intervention. Conclusion TAM induces BD reduction; however, the effect of RLX, tibolone, and AIs on BD is unclear. Although data on association between diet and BD in adulthood are contradictory, intake of vegetables, vitamin D, and calcium appear to be associated with lower BD in premenopausal women.

Association of infertility and fertility treatment with mammographic density in a large screening-based cohort of women: a cross-sectional study

Background

Ovarian stimulation drugs, in particular hormonal agents used for controlled ovarian stimulation (COS) required to perform in vitro fertilization, increase estrogen and progesterone levels and have therefore been suspected to influence breast cancer risk. This study aims to investigate whether infertility and hormonal fertility treatment influences mammographic density, a strong hormone-responsive risk factor for breast cancer.

Methods

Cross-sectional study including 43,313 women recruited to the Karolinska Mammography Project between 2010 and 2013. Among women who reported having had infertility, 1576 had gone through COS, 1429 had had hormonal stimulation without COS and 5958 had not received any hormonal fertility treatment. Percent and absolute mammographic densities were obtained using the volumetric method Volpara™. Associations with mammographic density were assessed using multivariable generalized linear models, estimating mean differences (MD) with 95 % confidence intervals (CI).

Results

After multivariable adjustment, women with a history of infertility had 1.53 cm³ higher absolute dense volume compared to non-infertile women (95 % CI: 0.70 to 2.35). Among infertile women, only those who had gone through COS treatment had a higher absolute dense volume than those who had not received any hormone treatment (adjusted MD 3.22, 95 % CI: 1.10 to 5.33). No clear associations were observed between infertility, fertility treatment and percent volumetric density.

Conclusions

Overall, women reporting infertility had more dense tissue in the breast. The higher absolute dense volume in women treated with COS may indicate a treatment effect, although part of the association might also be due to the underlying infertility. Continued monitoring of cancer risk in infertile women, especially those who undergo COS, is warranted.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-016-0693-5) contains supplementary material, which is available to authorized users.

Joint modelling of longitudinal and survival data: incorporating delayed entry and an assessment of model misspecification

A now common goal in medical research is to investigate the inter-relationships between a repeatedly measured biomarker, measured with error, and the time to an event of interest. This form of question can be tackled with a joint longitudinal-survival model, with the most common approach combining a longitudinal mixed effects model with a proportional hazards survival model, where the models are linked through shared random effects. In this article, we look at incorporating delayed entry (left truncation), which has received relatively little attention. The extension to delayed entry requires a second set of numerical integration, beyond that required in a standard joint model. We therefore implement two sets of fully adaptive Gauss-Hermite quadrature with nested Gauss-Kronrod quadrature (to allow time-dependent association structures), conducted simultaneously, to evaluate the likelihood. We evaluate fully adaptive quadrature compared with previously proposed non-adaptive quadrature through a simulation study, showing substantial improvements, both in terms of minimising bias and reducing computation time. We further investigate, through simulation, the consequences of misspecifying the longitudinal trajectory and its impact on estimates of association. Our scenarios showed the current value association structure to be very robust, compared with the rate of change that we found to be highly sensitive showing that assuming a simpler trend when the truth is more complex can lead to substantial bias. With emphasis on flexible parametric approaches, we generalise previous models by proposing the use of polynomials or splines to capture the longitudinal trend and restricted cubic splines to model the baseline log hazard function. The methods are illustrated on a dataset of breast cancer patients, modelling mammographic density jointly with survival, where we show how to incorporate density measurements prior to the at-risk period, to make use of all the available information. User-friendly Stata software is provided.

The Impact of Acquisition Dose on Quantitative Breast Density Estimation with Digital Mammography: Results from ACRIN PA 4006

Purpose To investigate the impact of radiation dose on breast density estimation in digital mammography. Materials and Methods With institutional review board approval and Health Insurance Portability and Accountability Act compliance under waiver of consent, a cohort of women from the American College of Radiology Imaging Network Pennsylvania 4006 trial was retrospectively analyzed. All patients underwent breast screening with a combination of dose protocols, including standard full-field digital mammography, low-dose digital mammography, and digital breast tomosynthesis. A total of 5832 images from 486 women were analyzed with previously validated, fully automated software for quantitative estimation of density. Clinical Breast Imaging Reporting and Data System (BI-RADS) density assessment results were also available from the trial reports. The influence of image acquisition radiation dose on quantitative breast density estimation was investigated with analysis of variance and linear regression. Pairwise comparisons of density estimations at different dose levels were performed with Student t test. Agreement of estimation was evaluated with quartile-weighted Cohen kappa values and Bland-Altman limits of agreement. Results Radiation dose of image acquisition did not significantly affect quantitative density measurements (analysis of variance, P = .37 to P = .75), with percent density demonstrating a high overall correlation between protocols (r = 0.88-0.95; weighted κ = 0.83-0.90). However, differences in breast percent density (1.04% and 3.84%, P < .05) were observed within high BI-RADS density categories, although they were significantly correlated across the different acquisition dose levels (r = 0.76-0.92, P < .05). Conclusion Precision and reproducibility of automated breast density measurements with digital mammography are not substantially affected by variations in radiation dose; thus, the use of low-dose techniques for the purpose of density estimation may be feasible. (©) RSNA, 2016 Online supplemental material is available for this article.

Quantra™ should be considered a tool for two-grade scale mammographic breast density classification

OBJECTIVE

To assess the agreement between Quantra™ (Hologic Inc., Bedford, MA) and Breast Imaging Reporting and Data Systems (BI-RADS(®)) and the performance of Quantra at reproducing BI-RADS mammographic breast density (MBD) assessment.

METHODS

MBD assessment was performed using Quantra and BI-RADS. BI-RADS assessment was performed in two phases (1314 and 292 cases, respectively). Kappa was used to assess the interreader agreement and the agreement between Quantra and BI-RADS, and receiver-operating characteristics analysis was used to assess the performance of Quantra at reproducing BI-RADS rating.

RESULTS

Agreement (weighted kappa) between BI-RADS and Quantra in Phase 1 was 0.75 [95% confidence interval (CI): 0.73-0.78] and 0.85 (95% CI: 0.80-0.90) on four- and two-grade scales, respectively. The corresponding agreement in Phase 2 was 0.79 (95% CI: 0.75-0.84) and 0.84 (95% CI: 0.79-0.87) using the majority report. In Phase 1, Quantra demonstrated 93.2% sensitivity and 86.1% specificity for BI-RADS on a two-grade scale (1-2 vs 3-4). In Phase 2, it demonstrated 91.3% sensitivity and 83.6% specificity on a two-grade scale.

CONCLUSION

Quantra is limited in reproducing BI-RADS rating on a four-grade scale; however, it highly reproduces BI-RADS assessment on a two-grade scale.

ADVANCES IN KNOWLEDGE

Quantra (v. 2.0) is a poor predictor of BI-RADS assessment on a four-grade scale, but well reproduces BI-RADS rating on a two-grade scale. Therefore, it should be considered a tool for two-grade scale MBD classification.

Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort

INTRODUCTION

The Predicting Risk of Cancer at Screening study in Manchester, UK, is a prospective study of breast cancer risk estimation. It was designed to assess whether mammographic density may help in refinement of breast cancer risk estimation using either the Gail model (Breast Cancer Risk Assessment Tool) or the Tyrer-Cuzick model (International Breast Intervention Study model).

METHODS

Mammographic density was measured at entry as a percentage visual assessment, adjusted for age and body mass index. Tyrer-Cuzick and Gail 10-year risks were based on a questionnaire completed contemporaneously. Breast cancers were identified at the entry screen or shortly thereafter. The contribution of density to risk models was assessed using odds ratios (ORs) with profile likelihood confidence intervals (CIs) and area under the receiver operating characteristic curve (AUC). The calibration of predicted ORs was estimated as a percentage [(observed vs expected (O/E)] from logistic regression.

RESULTS

The analysis included 50,628 women aged 47-73 years who were recruited between October 2009 and September 2013. Of these, 697 had breast cancer diagnosed after enrolment. Median follow-up was 3.2 years. Breast density [interquartile range odds ratio (IQR-OR) 1.48, 95 % CI 1.34-1.63, AUC 0.59] was a slightly stronger univariate risk factor than the Tyrer-Cuzick model [IQR-OR 1.36 (95 % CI 1.25-1.48), O/E 60 % (95 % CI 44-74), AUC 0.57] or the Gail model [IQR-OR 1.22 (95 % CI 1.12-1.33), O/E 46 % (95 % CI 26-65 %), AUC 0.55]. It continued to add information after allowing for Tyrer-Cuzick [IQR-OR 1.47 (95 % CI 1.33-1.62), combined AUC 0.61] or Gail [IQR-OR 1.45 (95 % CI 1.32-1.60), combined AUC 0.59].

CONCLUSIONS

Breast density may be usefully combined with the Tyrer-Cuzick model or the Gail model.

Longitudinal Change in Mammographic Density among ER-Positive Breast Cancer Patients Using Tamoxifen

Tamoxifen-associated mammographic density (MD) reductions are linked to improved breast cancer survival. We evaluated MD at six time points to determine the timing of greatest reduction following tamoxifen initiation. We sampled 40 Kaiser Permanente Northwest estrogen receptor (ER)-positive breast cancer patients from a prior study of MD change, according to tamoxifen use duration and age at diagnosis: <4 years tamoxifen and ≤50 years (N = 6) or >50 years (N = 10) old; ≥4 years tamoxifen and ≤50 years (N = 13) or >50 years (N = 11) old. A single reader evaluated percent MD in the contralateral breast on baseline (pre-diagnosis) and five approximately yearly post-diagnostic (T1 to T5) mammograms. Mean MD change was calculated. Interactions with age (≤50 and >50 years), tamoxifen duration (<4 and ≥4 years), and baseline MD (tertiles) were tested in linear regression models. Overall, the largest MD decline occurred by T1 (mean 4.5%) with little additional decline by T5. Declines differed by tertile of baseline MD (Pinteraction < 0.01). In the highest tertile, the largest reduction occurred by T1 (mean 14.9%), with an additional reduction of 3.6% by T5. Changes were smaller in the middle and lowest baseline MD tertiles, with cumulative reductions of 3.0% and 0.4% from baseline to T5, respectively. There were no differences by age (Pinteraction = 0.36) or tamoxifen duration (Pinteraction = 0.42). Among ER-positive patients treated with tamoxifen and surviving ≥5 years, most of the MD reduction occurred within approximately 12 months of tamoxifen initiation, suggesting that MD measurement at a single time point following tamoxifen initiation can identify patients with substantial density declines.

Determinants of the reliability of ultrasound tomography sound speed estimates as a surrogate for volumetric breast density

PURPOSE

High breast density, as measured by mammography, is associated with increased breast cancer risk, but standard methods of assessment have limitations including 2D representation of breast tissue, distortion due to breast compression, and use of ionizing radiation. Ultrasound tomography (UST) is a novel imaging method that averts these limitations and uses sound speed measures rather than x-ray imaging to estimate breast density. The authors evaluated the reproducibility of measures of speed of sound and changes in this parameter using UST.

METHODS

One experienced and five newly trained raters measured sound speed in serial UST scans for 22 women (two scans per person) to assess inter-rater reliability. Intrarater reliability was assessed for four raters. A random effects model was used to calculate the percent variation in sound speed and change in sound speed attributable to subject, scan, rater, and repeat reads. The authors estimated the intraclass correlation coefficients (ICCs) for these measures based on data from the authors' experienced rater.

RESULTS

Median (range) time between baseline and follow-up UST scans was five (1-13) months. Contributions of factors to sound speed variance were differences between subjects (86.0%), baseline versus follow-up scans (7.5%), inter-rater evaluations (1.1%), and intrarater reproducibility (∼0%). When evaluating change in sound speed between scans, 2.7% and ∼0% of variation were attributed to inter- and intrarater variation, respectively. For the experienced rater's repeat reads, agreement for sound speed was excellent (ICC = 93.4%) and for change in sound speed substantial (ICC = 70.4%), indicating very good reproducibility of these measures.

CONCLUSIONS

UST provided highly reproducible sound speed measurements, which reflect breast density, suggesting that UST has utility in sensitively assessing change in density.

Selective Estrogen Receptor Modulator-Associated Nonalcoholic Fatty Liver Disease Improved Survival in Patients With Breast Cancer: A Retrospective Cohort Analysis

Selective estrogen receptor modulator (SERM)-associated nonalcoholic fatty liver disease (NAFLD) might be related to treatment efficacy in patients with breast cancer because of circulating estrogen antagonism. The aim of the study was to investigate the relationship between NAFLD and survival outcomes in patients with breast cancer who were treated with tamoxifen or toremifene. This single-center, retrospective, cohort study included 785 eligible patients who received tamoxifen or toremifene, after curative resection for breast cancer, at the Sun Yat-sen University Cancer Center between January 2005 and December 2009. Data were extracted from patient medical records. All patients underwent abdominal ultrasonography, at least once, at baseline and at the annual follow-up. Patients who were diagnosed with NAFLD on ultrasonography were classified into the NAFLD or the non-NAFLD arm at the 3-year follow-up visit. Univariate and multivariate Cox regression analyses were conducted to evaluate any associations between NAFLD and disease-free survival (DFS) or overall survival (OS). One hundred fifty-eight patients were diagnosed with NAFLD. Patients who developed NAFLD had better DFS and OS compared with those who did not. Univariate analyses revealed that the 5-year DFS rates were 91.56% and 85.01% for the NAFLD and non-NAFLD arms, respectively (hazard ratio [HR], 0.59; 95% confidence interval [CI], 0.37-0.96; log-rank P = 0.032). The 5-year OS rates were 96.64% and 93.31% for the NAFLD and non-NAFLD arms, respectively (HR, 0.39; 95% CI, 0.16-0.99; log-rank P = 0.039). Multivariate analysis revealed that NAFLD was an independent prognostic factor for DFS, improving the DFS rate by 41% compared with that in the non-NAFLD arm (HR, 0.59; 95% CI, 0.36-0.96; P = 0.033). SERM-associated NAFLD was independently associated with improved DFS and might be useful for predicting treatment responses in breast cancer patients treated with SERMs.

Increased COX-2 expression in epithelial and stromal cells of high mammographic density tissues and in a xenograft model of mammographic density

Mammographic density (MD) adjusted for age and body mass index is one of the strongest known risk factors for breast cancer. Given the high attributable risk of MD for breast cancer, chemoprevention with a safe and available agent that reduces MD and breast cancer risk would be beneficial. Cox-2 has been implicated in MD-related breast cancer risk, and was increased in stromal cells in high MD tissues in one study. Our study assessed differential Cox-2 expression in epithelial and stromal cells in paired samples of high and low MD human breast tissue, and in a validated xenograft biochamber model of MD. We also examined the effects of endocrine treatment upon Cox-2 expression in high and low MD tissues in the MD xenograft model. Paired high and low MD human breast tissue samples were immunostained for Cox-2, then assessed for differential expression and staining intensity in epithelial and stromal cells. High and low MD human breast tissues were separately maintained in biochambers in mice treated with Tamoxifen, oestrogen or placebo implants, then assessed for percentage Cox-2 staining in epithelial and stromal cells. Percentage Cox-2 staining was greater for both epithelial (p = 0.01) and stromal cells (p < 0.0001) of high compared with low MD breast tissues. In high MD biochamber tissues, percentage Cox-2 staining was greater in stromal cells of oestrogen-treated versus placebo-treated tissues (p = 0.05).

Breast Tissue Composition and Immunophenotype and Its Relationship with Mammographic Density in Women at High Risk of Breast Cancer

Aim

To investigate the cellular and immunophenotypic basis of mammographic density in women at high risk of breast cancer.

Methods

Mammograms and targeted breast biopsies were accrued from 24 women at high risk of breast cancer. Mammographic density was classified into Wolfe categories and ranked by increasing density. The histological composition and immunophenotypic profile were quantified from digitized haematoxylin and eosin-stained and immunohistochemically-stained (ERα, ERβ, PgR, HER2, Ki-67, and CD31) slides and correlated to mammographic density.

Results

Increasing mammographic density was significantly correlated with increased fibrous stroma proportion (rs (22) = 0.5226, p = 0.0088) and significantly inversely associated with adipose tissue proportion (rs (22) = -0.5409, p = 0.0064). Contrary to previous reports, stromal expression of ERα was common (19/20 cases, 95%). There was significantly higher stromal PgR expression in mammographically-dense breasts (p=0.026).

Conclusions

The proportion of stroma and fat underlies mammographic density in women at high risk of breast cancer. Increased expression of PgR in the stroma of mammographically dense breasts and frequent and unexpected presence of stromal ERα expression raises the possibility that hormone receptor expression in breast stroma may have a role in mediating the effects of exogenous hormonal therapy on mammographic density.

Intraindividual comparison of two methods of volumetric breast composition assessment

RATIONALE AND OBJECTIVES

To compare the results of two software-based methods, Quantra and Volpara, for volumetric breast composition assessment.

MATERIALS AND METHODS

Four hundred forty-five normal, bilateral, two-view, digital mammograms were included. Breast volume (BV), fibroglandular tissue volume (FTV), and percent density (PD) were measured using both methods and compared. Deming regression was performed to obtain linear equations for mapping the results of one software on the other.

RESULTS

The median and quartile ranges of both methods agreed well for BV but were different for FTV and PD, with Quantra showing much higher values of FTV and PD. The correlation of results obtained by both methods for BV, FTV, and PD was 0.99, 0.91, and 0.94, respectively. Intraclass correlation in the assignment of quartiles of BV, FTV, and PD was 0.96, 0.86, and 0.90, respectively. Both methods showed a similar association of FTV and PD with patient age and similar left-to-right correlation. Mapping of results onto each other using linear equations removed the systematic differences.

CONCLUSIONS

Although Quantra and Volpara use different models for analysis of volumetric breast composition and produce different nominal results of FTV and PD, both methods are highly correlated and show very good to excellent agreement in quartile assignment of all parameters measured. Both methods show a similar association with patient age and similar reproducibility. Both methods can be mapped onto each other using the equations suggested.

One versus Two Breast Density Measures to Predict 5- and 10-Year Breast Cancer Risk

BACKGROUND

One measure of Breast Imaging Reporting and Data System (BI-RADS) breast density improves 5-year breast cancer risk prediction, but the value of sequential measures is unknown. We determined whether two BI-RADS density measures improve the predictive accuracy of the Breast Cancer Surveillance Consortium 5-year risk model compared with one measure.

METHODS

We included 722,654 women of ages 35 to 74 years with two mammograms with BI-RADS density measures on average 1.8 years apart; 13,715 developed invasive breast cancer. We used Cox regression to estimate the relative hazards of breast cancer for age, race/ethnicity, family history of breast cancer, history of breast biopsy, and one or two density measures. We developed a risk prediction model by combining these estimates with 2000-2010 Surveillance, Epidemiology, and End Results incidence and 2010 vital statistics for competing risk of death.

RESULTS

The two-measure density model had marginally greater discriminatory accuracy than the one-measure model (AUC, 0.640 vs. 0.635). Of 18.6% of women (134,404 of 722,654) who decreased density categories, 15.4% (20,741 of 134,404) of women whose density decreased from heterogeneously or extremely dense to a lower density category with one other risk factor had a clinically meaningful increase in 5-year risk from <1.67% with the one-density model to ≥1.67% with the two-density model.

CONCLUSION

The two-density model has similar overall discrimination to the one-density model for predicting 5-year breast cancer risk and improves risk classification for women with risk factors and a decrease in density.

IMPACT

A two-density model should be considered for women whose density decreases when calculating breast cancer risk.

Minimal impact of adjuvant exemestane or tamoxifen treatment on mammographic breast density in postmenopausal breast cancer patients: a Dutch TEAM trial analysis

BACKGROUND

Mammographic breast density is one of the strongest independent risk factors for developing breast cancer. We examined the effect of exemestane and tamoxifen on breast density in Dutch postmenopausal early breast cancer patients participating in the Tamoxifen Exemestane Adjuvant Multinational (TEAM) trial.

MATERIAL AND METHODS

Analogue mammograms of selected TEAM participants before start, and after one and two (and if available after three) years of adjuvant endocrine therapy were collected centrally and reviewed. Study endpoints were change in breast density over time, and correlations between breast density and locoregional recurrence (LRR), distance recurrence (DR), and contralateral breast cancer (CBC).

RESULTS

Mammograms of 378 patients (181 tamoxifen, 197 exemestane) were included in the current per protocol analyses. Baseline breast density was low (breast density score<50% in 75% of patients) and not different between patients randomised to exemestane or tamoxifen (coefficient 0.16, standard error 0.17). Breast density did not change during treatment in exemestane (p=0.25) or tamoxifen users (p=0.59). No relation was observed between breast density and the occurrence of a LRR [hazards ratio (HR) 0.87, 95% CI 0.45-1.68, p=0.67], a DR (HR 1.02, 95% CI 0.77-1.35, p=0.90), or CBC (HR 1.31, 95% CI 0.63-2.72, p=0.48).

CONCLUSION

The in general low breast density score in early postmenopausal breast cancer patients did not substantially change over time, and this pattern was not different between tamoxifen and exemestane users. Breast density was not a predictive marker for efficacy of adjuvant endocrine therapy.

Prognostic significance of mammographic density change after initiation of tamoxifen for ER-positive breast cancer

BACKGROUND

A prior analysis of postmenopausal breast cancer patients linked a decline in mammographic density (MD) following the initiation of tamoxifen treatment with improved survival, but excluded premenopausal women, for whom tamoxifen is the primary anti-endocrine therapy. Therefore, we evaluated change in MD after tamoxifen and breast cancer death among patients age 32 to 87 years.

METHODS

This case-control study included 349 estrogen receptor (ER)-positive breast cancer patients who were treated with tamoxifen at Kaiser Permanente Northwest (1990-2008): 97 who died from breast cancer (case patients) and 252 who did not (control patients), matched on age and year at diagnosis and disease stage. Percent MD in the unaffected breast was measured at baseline (mean six months before tamoxifen initiation) and follow-up (mean 12 months after initiation). Associations between change in MD and breast cancer death were estimated using conditional logistic regression.

RESULTS

Patients in the highest tertile of MD decline had a lower risk of breast cancer death when compared with women in the lowest tertile (odds ratio [OR] = 0.44, 95% confidence interval [CI] = 0.22 to 0.88); results were similar after adjustment for baseline MD (OR = 0.49, 95% CI = 0.23 to 1.02). Reductions in death were observed only among patients in the middle and upper tertiles of baseline MD. Associations did not differ by age, tamoxifen use duration, estrogen and/or progestin use, body mass index, or receipt of chemotherapy or radiotherapy.

CONCLUSION

These data suggest that younger and older ER-positive breast cancer patients who experience large reductions in MD following tamoxifen initiation have an improved prognosis.

Update on breast cancer risk prediction and prevention

PURPOSE OF REVIEW

Breast cancer is the most common cancer in women worldwide. This review will focus on current prevention strategies for women at high risk.

RECENT FINDINGS

The identification of women who are at high risk of developing breast cancer is key to breast cancer prevention. Recent findings have shown that the inclusion of breast density and a panel of low-penetrance genetic polymorphisms can improve risk estimation compared with previous models. Preventive therapy with aromatase inhibitors has produced large reductions in breast cancer incidence in postmenopausal women. Tamoxifen confers long-term protection and is the only proven preventive treatment for premenopausal women. Several other agents, including metformin, bisphosphonates, aspirin and statins, have been found to be effective in nonrandomized settings.

SUMMARY

There are many options for the prevention of oestrogen-positive breast cancer, in postmenopausal women who can be given a selective oestrogen receptor modulator or an aromatase inhibitor. It still remains unclear how to prevent oestrogen-negative breast cancer, which occurs more often in premenopausal women. Identification of women at high risk of the disease is crucial, and the inclusion of breast density and a panel of genetic polymorphisms, which individually have low penetrance, can improve risk assessment.

Automated breast segmentation of fat and water MR images using dynamic programming

RATIONALE AND OBJECTIVES

To develop and test an algorithm that outlines the breast boundaries using information from fat and water magnetic resonance images.

MATERIALS AND METHODS

Three algorithms were implemented and tested using registered fat and water magnetic resonance images. Two of the segmentation algorithms are simple extensions of the techniques used for contrast-enhanced images: one algorithm uses clustering and local gradient (CLG) analysis and the other algorithm uses a Hessian-based sheetness filter (HSF). The third segmentation algorithm uses k-means++ and dynamic programming (KDP) for finding the breast pixels. All three algorithms separate the left and right breasts using either a fixed region or a morphological method. The performance is quantified using a mutual overlap (Dice) metric and a pectoral muscle boundary error. The algorithms are evaluated against three manual tracers using 266 breast images from 14 female subjects.

RESULTS

The KDP algorithm has a mean overlap percentage improvement that is statistically significant relative to the HSF and CLG algorithms. When using a fixed region to remove the tissue between breasts with tracer 1 as a reference, the KDP algorithm has a mean overlap of 0.922 compared to 0.864 (P < .01) for HSF and 0.843 (P < .01) for CLG. The performance of KDP is very similar to tracers 2 (0.926 overlap) and 3 (0.929 overlap). The performance analysis in terms of pectoral muscle boundary error showed that the fraction of the muscle boundary within three pixels of reference tracer 1 is 0.87 using KDP compared to 0.578 for HSF and 0.617 for CLG. Our results show that the performance of the KDP algorithm is independent of breast density.

CONCLUSIONS

We developed a new automated segmentation algorithm (KDP) to isolate breast tissue from magnetic resonance fat and water images. KDP outperforms the other techniques that focus on local analysis (CLG and HSF) and yields a performance similar to human tracers.

Effects of Tamoxifen and oestrogen on histology and radiographic density in high and low mammographic density human breast tissues maintained in murine tissue engineering chambers

Mammographic density (MD) is a strong risk factor for breast cancer. It is altered by exogenous endocrine treatments, including hormone replacement therapy and Tamoxifen. Such agents also modify breast cancer (BC) risk. However, the biomolecular basis of how systemic endocrine therapy modifies MD and MD-associated BC risk is poorly understood. This study aims to determine whether our xenograft biochamber model can be used to study the effectiveness of therapies aimed at modulating MD, by examine the effects of Tamoxifen and oestrogen on histologic and radiographic changes in high and low MD tissues maintained within the biochamber model. High and low MD human tissues were precisely sampled under radiographic guidance from prophylactic mastectomy fresh specimens of high-risk women, then inserted into separate vascularized murine biochambers. The murine hosts were concurrently implanted with Tamoxifen, oestrogen or placebo pellets, and the high and low MD biochamber tissues maintained in the murine host environment for 3 months, before the high and low MD biochamber tissues were harvested for histologic and radiographic analyses. The radiographic density of high MD tissue maintained in murine biochambers was decreased in Tamoxifen-treated mice compared to oestrogen-treated mice (p = 0.02). Tamoxifen treatment of high MD tissue in SCID mice led to a decrease in stromal (p = 0.009), and an increase in adipose (p = 0.023) percent areas, compared to placebo-treated mice. No histologic or radiographic differences were observed in low MD biochamber tissue with any treatment. High MD biochamber tissues maintained in mice implanted with Tamoxifen, oestrogen or placebo pellets had dynamic and measurable histologic compositional and radiographic changes. This further validates the dynamic nature of the MD xenograft model, and suggests the biochamber model may be useful for assessing the underlying molecular pathways of Tamoxifen-reduced MD, and in testing of other pharmacologic interventions in a preclinical model of high MD.

Area and volumetric density estimation in processed full-field digital mammograms for risk assessment of breast cancer

Introduction

Mammographic density, the white radiolucent part of a mammogram, is a marker of breast cancer risk and mammographic sensitivity. There are several means of measuring mammographic density, among which are area-based and volumetric-based approaches. Current volumetric methods use only unprocessed, raw mammograms, which is a problematic restriction since such raw mammograms are normally not stored. We describe fully automated methods for measuring both area and volumetric mammographic density from processed images.

Methods

The data set used in this study comprises raw and processed images of the same view from 1462 women. We developed two algorithms for processed images, an automated area-based approach (CASAM-Area) and a volumetric-based approach (CASAM-Vol). The latter method was based on training a random forest prediction model with image statistical features as predictors, against a volumetric measure, Volpara, for corresponding raw images. We contrast the three methods, CASAM-Area, CASAM-Vol and Volpara directly and in terms of association with breast cancer risk and a known genetic variant for mammographic density and breast cancer, rs10995190 in the gene ZNF365. Associations with breast cancer risk were evaluated using images from 47 breast cancer cases and 1011 control subjects. The genetic association analysis was based on 1011 control subjects.

Results

All three measures of mammographic density were associated with breast cancer risk and rs10995190 (p<0.025 for breast cancer risk and p<1×10⁻⁶ for rs10995190). After adjusting for one of the measures there remained little or no evidence of residual association with the remaining density measures (p>0.10 for risk, p>0.03 for rs10995190).

Conclusions

Our results show that it is possible to obtain reliable automated measures of volumetric and area mammographic density from processed digital images. Area and volumetric measures of density on processed digital images performed similar in terms of risk and genetic association.

Association of CYP2D6 metabolizer status with mammographic density change in response to tamoxifen treatment

Introduction

Not all breast cancer patients respond to tamoxifen treatment, possibly due to genetic predisposition. As tamoxifen-induced reductions in percent mammographic density (PMD) have been linked to the risk and prognosis of breast cancer, we conducted a candidate gene study to investigate the association between germline CYP2D6 polymorphisms and PMD change.

Methods

Baseline and follow-up mammograms were retrieved for 278 tamoxifen-treated subjects with CYP2D6 metabolizer status (extensive (EM), heterozygous extensive/intermediate (hetEM/IM) or poor metabolizer (PM)). Logistic regression analyses were conducted comparing subjects who experienced >10% reduction in PMD to those who experienced ≤10% reduction or increase.

Results

After multivariate adjustment, PMD change was found to be significantly associated with the degree of CYP2D6 enzyme functionality (Ptrend = 0.021). Compared with EM, hetEM/IM and PM were 72% (95% confidence interval (CI): 0.10 to 0.79) and 71% (0.03 to 2.62) less likely to experience a >10% reduction, respectively.

Conclusions

Tamoxifen-induced change in PMD appears to have a genetic component.

Prognostic value of mammographic breast density in patients with metastatic breast cancer

Breast density is a modifiable trait linked with breast cancer predisposition. However, the relation between mammographic breast density and survival outcome is not yet clarified. The present study aims to study the prognostic value of mammographic density in patients diagnosed with metastatic breast cancer. In this observational study, breast cancer patients with metastatic disease at diagnosis were enrolled. Two-view mammograms were performed at diagnosis, and breast density was quantitatively assessed. Progression-free survival (PFS) was correlated with breast density and other prognostic variables in univariate and multivariate analyses. PFS, stratified by different prognostic factors, was assessed in low compared to high density patients to check for possible differential survival outcome in patients' subgroups. Among the sixty enrolled patients, median PFS in low density patients was significantly better than those with high density (18.4 months, 95 % CI 14.88-22.15 vs. 9.3 months, 95 % CI 8.51-13.60, respectively, p = 0.002). Significant correlation of breast density with PFS persisted after adjustment by body mass index (p = 0.003) and after multivariate analysis incorporating other prognostic variables (HR 6.16, 95 % CI (2.17-17.48), p = 0.001). PFS was better in low density patients older than 40 years at diagnosis (p = 0.001), with HER2-negative disease (p = 0.015), hormonal receptor-positive phenotype (p = 0.020), patients with single site of metastasis (p = 0.006), and patients with bone-only metastases (p = 0.042). Breast density assessed at the time of diagnosis was significantly correlated with PFS of metastatic breast cancer patients. Survival outcome is improved in certain patients' subgroups with low breast density.

High volumetric breast density predicts risk for breast cancer in postmenopausal, but not premenopausal, Korean Women

PURPOSE

We investigated the association between mammographic breast density and breast cancer risk in Korean women according to menopausal status and breast cancer subtypes.

METHODS

We enrolled 677 patients diagnosed with breast cancer and 1,307 healthy controls who participated in screening mammography at the National Cancer Center. Breast density was estimated using volumetric breast composition measurement.

RESULTS

Of the total population, 1,156 (58.3 %) women were postmenopausal. The risk of breast cancer increased progressively with the increment of volumetric density grade (VDG) in postmenopausal women (p < 0.001). High breast density (VDG 4) was significantly associated with breast cancer compared with low breast density (VDG 1/2) regardless of body mass index. However, the association with parity and history of hormone replacement therapy (HRT) was only found in those with ≥2 children and those not receiving HRT. Breast density was positively associated with breast cancer risk regardless of histologic grade, tumor size, lymph node involvement, Ki67 index, and hormone receptor status. The association was more prominent in human epidermal growth factor receptor 2 (HER2)-positive tumors (VDG 1/2 vs. VDG 4 for HER2 normal, odds ratio [OR] 2.21, 95 % confidence interval [CI] 1.28-3.83, p < 0.001; for HER2 positive, OR 8.63, 95 % CI 3.26-22.83, p = 0.001; P heterogeneity = 0.030). However, no significant association was found between breast density and breast cancer risk in premenopausal women except for those with large-sized tumors (>2 cm) and a Ki67 index >15 %.

CONCLUSION

High volumetric breast density is significantly associated with the risk of breast cancer in postmenopausal women; however, these relationships were not found in premenopausal women.