Breast cancer risk factors and a novel measure of volumetric breast density: cross-sectional study

The Prospective Association between Early Life Growth and Breast Density in Young Adult Women

Breast density is a strong intermediate endpoint to investigate the association between early-life exposures and breast cancer risk. This study investigates the association between early-life growth and breast density in young adult women measured using Optical Breast Spectroscopy (OBS) and Dual X-ray Absorptiometry (DXA). OBS measurements were obtained for 536 female Raine Cohort Study participants at ages 27-28, with 268 completing DXA measurements. Participants with three or more height and weight measurements from ages 8 to 22 were used to generate linear growth curves for height, weight and body mass index (BMI) using SITAR modelling. Three growth parameters (size, velocity and timing) were examined for association with breast density measures, adjusting for potential confounders. Women who reached their peak height rapidly (velocity) and later in adolescence (timing) had lower OBS-breast density. Overall, women who were taller (size) had higher OBS-breast density. For weight, women who grew quickly (velocity) and later in adolescence (timing) had higher absolute DXA-breast density. Overall, weight (size) was also inversely associated with absolute DXA-breast density, as was BMI. These findings provide new evidence that adolescent growth is associated with breast density measures in young adult women, suggesting potential mediation pathways for breast cancer risk in later life.

Does Laterality in Breast Cancer still have the Importance to be Studied? A Meta-analysis of Patients with Breast Cancer

BACKGROUND

Breast cancer (BC) is one of the most common cancers in the world. Studies show that left-sided BC in pre and post-menopausal women leads to double the risk of worse morbidity and mortality and the reasons are uncertain. Finding the relationship between BC laterality and other possible risk factors can be advantageous for the prognosis of BC.

OBJECTIVE

This present study aimed to analyze the relationship between BC laterality and possible risk factors.

METHODS

A total of 6089 studies were screened. 23 studies from 1971 to 2021 met the inclusion criteria and were included in the meta-analysis. A pooled relative risk was generated via meta-analysis with a 95% confidence interval.

RESULTS

Left-side BC laterality was significant (p < 0.00001) in the women populations compared to the right side based on the pooled size with possible high-risk factors, including handedness, older women, body mass index, people with black skin, invasive type carcinoma, and estrogen receptor-negative BC. These findings suggest that there may be a complex interplay of genetic, environmental, and lifestyle factors that contribute to left-side BC laterality.

CONCLUSION

Results suggest an increased rate of BC on the left side, with high-risk factors contributing to BC laterality, which may be useful in predicting prognosis. This study provides significant insights into the relationship between high-risk factors and BC laterality. By identifying potential risk factors associated with left-side BC, it may be possible to improve the ability to predict prognosis and develop more targeted treatment strategies. This information could be particularly useful for healthcare providers and patients, as it may guide decisions regarding screening, prevention, and treatment, ultimately improving patient outcomes and reducing the overall burden of BC.

The Relationship between Body Mass Index and Mammographic Density during a Premenopausal Weight Loss Intervention Study

We evaluated the association between short-term change in body mass index (BMI) and breast density during a 1 year weight-loss intervention (Manchester, UK). We included 65 premenopausal women (35-45 years, ≥7 kg adult weight gain, family history of breast cancer). BMI and breast density (semi-automated area-based, automated volume-based) were measured at baseline, 1 year, and 2 years after study entry (1 year post intervention). Cross-sectional (between-women) and short-term change (within-women) associations between BMI and breast density were measured using repeated-measures correlation coefficients and multivariable linear mixed models. BMI was positively correlated with dense volume between-women (r = 0.41, 95%CI: 0.17, 0.61), but less so within-women (r = 0.08, 95%CI: -0.16, 0.28). There was little association with dense area (between-women r = -0.12, 95%CI: -0.38, 0.16; within-women r = 0.01, 95%CI: -0.24, 0.25). BMI and breast fat were positively correlated (volume: between r = 0.77, 95%CI: 0.69, 0.84, within r = 0.58, 95%CI: 0.36, 0.75; area: between r = 0.74, 95%CI: 0.63, 0.82, within r = 0.45, 95%CI: 0.23, 0.63). Multivariable models reported similar associations. Exploratory analysis suggested associations between BMI gain from 20 years and density measures (standard deviation change per +5 kg/m² BMI: dense area: +0.61 (95%CI: 0.12, 1.09); fat volume: -0.31 (95%CI: -0.62, 0.00)). Short-term BMI change is likely to be positively associated with breast fat, but we found little association with dense tissue, although power was limited by small sample size.

Pubertal mammary gland development is a key determinant of adult mammographic density

Mammographic density refers to the radiological appearance of fibroglandular and adipose tissue on a mammogram of the breast. Women with relatively high mammographic density for their age and body mass index are at significantly higher risk for breast cancer. The association between mammographic density and breast cancer risk is well-established, however the molecular and cellular events that lead to the development of high mammographic density are yet to be elucidated. Puberty is a critical time for breast development, where endocrine and paracrine signalling drive development of the mammary gland epithelium, stroma, and adipose tissue. As the relative abundance of these cell types determines the radiological appearance of the adult breast, puberty should be considered as a key developmental stage in the establishment of mammographic density. Epidemiological studies have pointed to the significance of pubertal adipose tissue deposition, as well as timing of menarche and thelarche, on adult mammographic density and breast cancer risk. Activation of hypothalamic-pituitary axes during puberty combined with genetic and epigenetic molecular determinants, together with stromal fibroblasts, extracellular matrix, and immune signalling factors in the mammary gland, act in concert to drive breast development and the relative abundance of different cell types in the adult breast. Here, we discuss the key cellular and molecular mechanisms through which pubertal mammary gland development may affect adult mammographic density and cancer risk.

Combined effect of volumetric breast density and body mass index on breast cancer risk

BACKGROUND

Breast density and body mass index (BMI) are used for breast cancer risk stratification. We evaluate whether the positive association between volumetric breast density and breast cancer risk is strengthened with increasing BMI.

METHODS

The San Francisco Mammography Registry and Mayo Clinic Rochester identified 781 premenopausal and 1850 postmenopausal women with breast cancer diagnosed between 2007 and 2015 that had a screening digital mammogram at least 6 months prior to diagnosis. Up to three controls (N = 3535) were matched per case on age, race, date, mammography machine, and state. Volumetric percent density (VPD) and dense volume (DV) were measured with Volpara™. Breast cancer risk was assessed with logistic regression stratified by menopause status. Multiplicative interaction tests assessed whether the association of density measures was differential by BMI categories.

RESULTS

The increased risk of breast cancer associated with VPD was strengthened with higher BMI for both premenopausal (p_interaction = 0.01) and postmenopausal (p_interaction = 0.0003) women. For BMI < 25, 25-30, and ≥ 30 kg/m², ORs for breast cancer for a 1 SD increase in VPD were 1.24, 1.65, and 1.97 for premenopausal, and 1.20, 1.55, and 2.25 for postmenopausal women, respectively. ORs for breast cancer for a 1 SD increase in DV were 1.39, 1.33, and 1.51 for premenopausal (p_interaction = 0.58), and 1.31, 1.34, and 1.65 (p_interaction = 0.03) for postmenopausal women for BMI < 25, 25-30 and ≥ 30 kg/m², respectively.

CONCLUSIONS

The effect of volumetric percent density on breast cancer risk is strongest in overweight and obese women. These associations have clinical relevance for informing prevention strategies.

Growth Trajectories, Breast Size, and Breast-Tissue Composition in a British Prebirth Cohort of Young Women

Mammographic percent density, the proportion of fibroglandular tissue in the breast, is a strong risk factor for breast cancer, but its determinants in young women are unknown. We examined associations of magnetic resonance imaging (MRI) breast-tissue composition at age 21 years with prospectively collected measurements of body size and composition from birth to early adulthood and markers of puberty (all standardized) in a sample of 500 nulliparous women from a prebirth cohort of children born in Avon, United Kingdom, in 1991-1992 and followed up to 2011-2014. Linear models were fitted to estimate relative change in MRI percent water, which is equivalent to mammographic percent density, associated with a 1-standard-deviation increase in the exposure of interest. In mutually adjusted analyses, MRI percent water was positively associated with birth weight (relative change (RC) = 1.03, 95% confidence interval (CI): 1.00, 1.06) and pubertal height growth (RC = 1.07, 95% CI: 1.02, 1.13) but inversely associated with pubertal weight growth (RC = 0.86, 95% CI: 0.84, 0.89) and changes in dual-energy x-ray absorptiometry percent body fat mass (e.g., for change between ages 11 years and 13.5 years, RC = 0.96, 95% CI: 0.93, 0.99). Ages at thelarche and menarche were positively associated with MRI percent water, but these associations did not persist upon adjustment for height and weight growth. These findings support the hypothesis that growth trajectories influence breast-tissue composition in young women, whereas puberty plays no independent role.

Adiposity, breast density, and breast cancer risk: epidemiological and biological considerations

Excess total body fat and abdominal adipose tissue are recognized risk factors for metabolic diseases but also for some types of cancers, including breast cancer. Several biological mechanisms in connection with local and systemic effects of adiposity are believed to be implicated in breast cancer development, and may involve breast fat. Breast adipose tissue can be studied through mammography by looking at breast density features such as the nondense area mainly composed of fat, or the percent breast density, which is the proportion of fibroglandular tissue in relation to fat. The relation between adiposity, breast density features, and breast cancer is complex. Studies suggest a paradoxical association as adiposity and absolute nondense area correlate positively with each other, but in contrast to adiposity, absolute nondense area seems to be associated negatively with breast cancer risk. As breast density is one of the strongest risk factors for breast cancer, it is therefore critical to understand how these factors interrelate. In this review, we discuss these relations by first presenting how adiposity measurements and breast density features are linked to breast cancer risk. Then, we used a systematic approach to capture the literature to review the relation between adiposity and breast density features. Finally, the role of adipose tissue in carcinogenesis is discussed briefly from a biological perspective.

Pre-natal exposures and breast tissue composition: findings from a British pre-birth cohort of young women and a systematic review

BACKGROUND

Breast density, the amount of fibroglandular tissue in the adult breast for a women's age and body mass index, is a strong biomarker of susceptibility to breast cancer, which may, like breast cancer risk itself, be influenced by events early in life. In the present study, we investigated the association between pre-natal exposures and breast tissue composition.

METHODS

A sample of 500 young, nulliparous women (aged approximately 21 years) from a U.K. pre-birth cohort underwent a magnetic resonance imaging examination of their breasts to estimate percent water, a measure of the relative amount of fibroglandular tissue equivalent to mammographic percent density. Information on pre-natal exposures was collected throughout the mothers' pregnancy and shortly after delivery. Regression models were used to investigate associations between percent water and pre-natal exposures. Mediation analysis, and a systematic review and meta-analysis of the published literature, were also conducted.

RESULTS

Adjusted percent water in young women was positively associated with maternal height (p for linear trend [p t] = 0.005), maternal mammographic density in middle age (p t = 0.018) and the participant's birth size (p t < 0.001 for birthweight). A 1-SD increment in weight (473 g), length (2.3 cm), head circumference (1.2 cm) and Ponderal Index (4.1 g/cm3) at birth were associated with 3 % (95 % CI 2-5 %), 2 % (95 % CI 0-3 %), 3 % (95 % CI 1-4 %) and 1 % (95 % CI 0-3 %), respectively, increases in mean adjusted percent water. The effect of maternal height on the participants' percent water was partly mediated through birth size, but there was little evidence that the effect of birthweight was primarily mediated via adult body size. The meta-analysis supported the study findings, with breast density being positively associated with birth size.

CONCLUSIONS

These findings provide strong evidence of pre-natal influences on breast tissue composition. The positive association between birth size and relative amount of fibroglandular tissue indicates that breast density and breast cancer risk may share a common pre-natal origin.

Breast Density Estimation with Fully Automated Volumetric Method: Comparison to Radiologists' Assessment by BI-RADS Categories

RATIONALE AND OBJECTIVES

The objective of our study was to calculate mammographic breast density with a fully automated volumetric breast density measurement method and to compare it to breast imaging reporting and data system (BI-RADS) breast density categories assigned by two radiologists.

MATERIALS AND METHODS

A total of 476 full-field digital mammography examinations with standard mediolateral oblique and craniocaudal views were evaluated by two blinded radiologists and BI-RADS density categories were assigned. Using a fully automated software, mean fibroglandular tissue volume, mean breast volume, and mean volumetric breast density were calculated. Based on percentage volumetric breast density, a volumetric density grade was assigned from 1 to 4.

RESULTS

The weighted overall kappa was 0.895 (almost perfect agreement) for the two radiologists' BI-RADS density estimates. A statistically significant difference was seen in mean volumetric breast density among the BI-RADS density categories. With increased BI-RADS density category, increase in mean volumetric breast density was also seen (P < 0.001). A significant positive correlation was found between BI-RADS categories and volumetric density grading by fully automated software (ρ = 0.728, P < 0.001 for first radiologist and ρ = 0.725, P < 0.001 for second radiologist). Pairwise estimates of the weighted kappa between Volpara density grade and BI-RADS density category by two observers showed fair agreement (κ = 0.398 and 0.388, respectively).

CONCLUSIONS

In our study, a good correlation was seen between density grading using fully automated volumetric method and density grading using BI-RADS density categories assigned by the two radiologists. Thus, the fully automated volumetric method may be used to quantify breast density on routine mammography.

Imaging Breast Density: Established and Emerging Modalities

Mammographic density has been proven as an independent risk factor for breast cancer. Women with dense breast tissue visible on a mammogram have a much higher cancer risk than women with little density. A great research effort has been devoted to incorporate breast density into risk prediction models to better estimate each individual’s cancer risk. In recent years, the passage of breast density notification legislation in many states in USA requires that every mammography report should provide information regarding the patient’s breast density. Accurate definition and measurement of breast density are thus important, which may allow all the potential clinical applications of breast density to be implemented. Because the two-dimensional mammography-based measurement is subject to tissue overlapping and thus not able to provide volumetric information, there is an urgent need to develop reliable quantitative measurements of breast density. Various new imaging technologies are being developed. Among these new modalities, volumetric mammographic density methods and three-dimensional magnetic resonance imaging are the most well studied. Besides, emerging modalities, including different x-ray–based, optical imaging, and ultrasound-based methods, have also been investigated. All these modalities may either overcome some fundamental problems related to mammographic density or provide additional density and/or compositional information. The present review article aimed to summarize the current established and emerging imaging techniques for the measurement of breast density and the evidence of the clinical use of these density methods from the literature.

The effect of change in body mass index on volumetric measures of mammographic density

BACKGROUND

Understanding how changes in body mass index (BMI) relate to changes in mammographic density is necessary to evaluate adjustment for BMI gain/loss in studies of change in density and breast cancer risk. Increase in BMI has been associated with a decrease in percent density, but the effect on change in absolute dense area or volume is unclear.

METHODS

We examined the association between change in BMI and change in volumetric breast density among 24,556 women in the San Francisco Mammography Registry from 2007 to 2013. Height and weight were self-reported at the time of mammography. Breast density was assessed using single x-ray absorptiometry measurements. Cross-sectional and longitudinal associations between BMI and dense volume (DV), non-dense volume (NDV), and percent dense volume (PDV) were assessed using multivariable linear regression models, adjusted for demographics, risk factors, and reproductive history.

RESULTS

In cross-sectional analysis, BMI was positively associated with DV [β, 2.95 cm(3); 95% confidence interval (CI), 2.69-3.21] and inversely associated with PDV (β, -2.03%; 95% CI, -2.09, -1.98). In contrast, increasing BMI was longitudinally associated with a decrease in both DV (β, -1.01 cm(3); 95% CI, -1.59, -0.42) and PDV (β, -1.17%; 95% CI, -1.31, -1.04). These findings were consistent for both pre- and postmenopausal women.

CONCLUSION

Our findings support an inverse association between change in BMI and change in PDV. The association between increasing BMI and decreasing DV requires confirmation.

IMPACT

Longitudinal studies of PDV and breast cancer risk, or those using PDV as an indicator of breast cancer risk, should evaluate adjustment for change in BMI.

An Investigation into the Consistency in Mammographic Density Identification by Radiologists: Effect of Radiologist Expertise and Mammographic Appearance

The aim of this work is to investigate how radiologist expertise and image appearance may have an impact on inter-reader variability of mammographic density (MD) identification. Seventeen radiologists, divided into three expertise groups, were asked to manually segment the areas they consider to be MD in 40 clinical images. The variation in identification of MD for each image was quantified by finding the range of segmentation areas. The impact of radiologist expertise and image appearance on this variation was explored. The range of areas chosen by participating radiologists varied from 7 to 73% across the 40 images, with a mean range of 35 ± 13%. Participants with high expertise were more likely to choose similar areas to one another, compared to participants with medium and low expertise levels (mean range were 19 ± 10%, 29 ± 13% and 25 ± 14 %, respectively, p < 0.0001). There was a significantly higher average grey level for the area segmented by all radiologists as MD compared to the area of variation, with mean grey level value for 8-bit images being 146 ± 19 vs. 99 ± 14, respectively. MD segmentation borders were consistent in areas where there was a sharp intensity change within a short distance. In conclusion, radiologists with high expertise tend to have a higher agreement when identifying MD. Tissues which have a lower contrast and a less visually sharp gradient change at the interface between high density tissue and adipose background lead to inter-reader variation in choosing mammographic density.

Relationship of serum estrogens and metabolites with area and volume mammographic densities

Elevated mammographic density is a breast cancer risk factor, which has a suggestive, but unproven, relationship with increased exposure to sex steroid hormones. We examined associations of serum estrogens and estrogen metabolites with area and novel volume mammographic density measures among 187 women, ages 40-65, undergoing diagnostic breast biopsies at an academic facility in Vermont. Serum parent estrogens, estrone and estradiol, and their 2-, 4-, and 16-hydroxylated metabolites were measured using liquid chromatography-tandem mass spectrometry. Area mammographic density was measured in the breast contralateral to the biopsy using thresholding software; volume mammographic density was quantified using a density phantom. Linear regression was used to estimate associations of estrogens with mammographic densities, adjusted for age and body mass index, and stratified by menopausal status and menstrual cycle phase. Weak, positive associations between estrogens, estrogen metabolites, and mammographic density were observed, primarily among postmenopausal women. Among premenopausal luteal phase women, the 16-pathway metabolite estriol was associated with percent area (p = 0.04) and volume (p = 0.05) mammographic densities and absolute area (p = 0.02) and volume (p = 0.05) densities. Among postmenopausal women, levels of total estrogens, the sum of parent estrogens, and 2-, 4- and 16-hydroxylation pathway metabolites were positively associated with area density measures (percent: p = 0.03, p = 0.04, p = 0.01, p = 0.02, p = 0.07; absolute: p = 0.02, p = 0.02, p = 0.01, p = 0.02, p = 0.03, respectively) but not volume density measures. Our data suggest that serum estrogen profiles are weak determinants of mammographic density and that analysis of different density metrics may provide complementary information about relationships of estrogen exposure to breast tissue composition.

The effect of weight change on changes in breast density measures over menopause in a breast cancer screening cohort

INTRODUCTION

High weight and high percentage mammographic breast density are both breast cancer risk factors but are negatively correlated. Therefore, we wanted to obtain more insight into this apparent paradox.

METHODS

We investigated in a longitudinal study how weight change over menopause is related to changes in mammographic breast features. Five hundred ninety-one participants of the EPIC-NL cohort were divided into three groups according to their prospectively measured weight change over menopause: (1) weight loss (more than -3.0 %), (2) stable weight (between -3.0 % and +3.0 %), and (3) weight gain (more than 3.0 %). SPSS GLM univariate analysis was used to determine both the mean breast measure changes in, and the trend over, the weight change groups.

RESULTS

Over a median period of 5 years, the mean changes in percent density in these groups were -5.0 % (95 % confidence interval (CI) -8.0; -2.1), -6.8 % (95 % CI -9.0; -4.5), and -10.2 % (95 % CI -12.5; -7.9), respectively (P-trend = 0.001). The mean changes in dense area were -16.7 cm(2) (95 % CI -20.1; -13.4), -16.4 cm(2) (95 % CI -18.9; -13.9), and -18.1 cm(2) (95 % CI -20.6; -15.5), respectively (P-trend = 0.437). Finally, the mean changes in nondense area were -6.1 cm(2) (95 % CI -11.9; -0.4), -0.6 cm(2) (95 % CI -4.9; 3.8), and 5.3 cm(2) (95 % CI 0.9; 9.8), respectively (P-trend < 0.001).

CONCLUSIONS

Going through menopause is associated with a decrease in both percent density and dense area. Owing to an increase in the nondense tissue, the decrease in percent density is largest in women who gain weight. The decrease in dense area is not related to weight change. So the fact that both high percent density and high weight or weight gain are associated with high postmenopausal breast cancer risk can probably not be explained by an increase (or slower decrease) of dense area in women gaining weight compared with women losing weight or maintaining a stable weight. These results suggest that weight and dense area are presumably two independent postmenopausal breast cancer risk factors.

Comparison of mammographic density assessed as volumes and areas among women undergoing diagnostic image-guided breast biopsy

BACKGROUND

Mammographic density (MD), the area of non-fatty-appearing tissue divided by total breast area, is a strong breast cancer risk factor. Most MD analyses have used visual categorizations or computer-assisted quantification, which ignore breast thickness. We explored MD volume and area, using a volumetric approach previously validated as predictive of breast cancer risk, in relation to risk factors among women undergoing breast biopsy.

METHODS

Among 413 primarily white women, ages 40 to 65 years, undergoing diagnostic breast biopsies between 2007 and 2010 at an academic facility in Vermont, MD volume (cm(3)) was quantified in craniocaudal views of the breast contralateral to the biopsy target using a density phantom, whereas MD area (cm(2)) was measured on the same digital mammograms using thresholding software. Risk factor associations with continuous MD measurements were evaluated using linear regression.

RESULTS

Percent MD volume and area were correlated (r = 0.81) and strongly and inversely associated with age, body mass index (BMI), and menopause. Both measures were inversely associated with smoking and positively associated with breast biopsy history. Absolute MD measures were correlated (r = 0.46) and inversely related to age and menopause. Whereas absolute dense area was inversely associated with BMI, absolute dense volume was positively associated.

CONCLUSIONS

Volume and area MD measures exhibit some overlap in risk factor associations, but divergence as well, particularly for BMI.

IMPACT

Findings suggest that volume and area density measures differ in subsets of women; notably, among obese women, absolute density was higher with volumetric methods, suggesting that breast cancer risk assessments may vary for these techniques.

Breast Imaging Reporting and Data System (BI-RADS) breast composition descriptors: automated measurement development for full field digital mammography

PURPOSE

The Breast Imaging Reporting and Data System (BI-RADS) breast composition descriptors are used for standardized mammographic reporting and are assessed visually. This reporting is clinically relevant because breast composition can impact mammographic sensitivity and is a breast cancer risk factor. New techniques are presented and evaluated for generating automated BI-RADS breast composition descriptors using both raw and calibrated full field digital mammography (FFDM) image data.

METHODS

A matched case-control dataset with FFDM images was used to develop three automated measures for the BI-RADS breast composition descriptors. Histograms of each calibrated mammogram in the percent glandular (pg) representation were processed to create the new BR(pg) measure. Two previously validated measures of breast density derived from calibrated and raw mammograms were converted to the new BR(vc) and BR(vr) measures, respectively. These three measures were compared with the radiologist-reported BI-RADS compositions assessments from the patient records. The authors used two optimization strategies with differential evolution to create these measures: method-1 used breast cancer status; and method-2 matched the reported BI-RADS descriptors. Weighted kappa (κ) analysis was used to assess the agreement between the new measures and the reported measures. Each measure's association with breast cancer was evaluated with odds ratios (ORs) adjusted for body mass index, breast area, and menopausal status. ORs were estimated as per unit increase with 95% confidence intervals.

RESULTS

The three BI-RADS measures generated by method-1 had κ between 0.25-0.34. These measures were significantly associated with breast cancer status in the adjusted models: (a) OR = 1.87 (1.34, 2.59) for BR(pg); (b) OR = 1.93 (1.36, 2.74) for BR(vc); and (c) OR = 1.37 (1.05, 1.80) for BR(vr). The measures generated by method-2 had κ between 0.42-0.45. Two of these measures were significantly associated with breast cancer status in the adjusted models: (a) OR = 1.95 (1.24, 3.09) for BR(pg); (b) OR = 1.42 (0.87, 2.32) for BR(vc); and (c) OR = 2.13 (1.22, 3.72) for BR(vr). The radiologist-reported measures from the patient records showed a similar association, OR = 1.49 (0.99, 2.24), although only borderline statistically significant.

CONCLUSIONS

A general framework was developed and validated for converting calibrated mammograms and continuous measures of breast density to fully automated approximations for the BI-RADS breast composition descriptors. The techniques are general and suitable for a broad range of clinical and research applications.

Automated Percentage of Breast Density Measurements for Full-field Digital Mammography Applications

RATIONALE AND OBJECTIVES

Increased mammographic breast density is a significant risk factor for breast cancer. A reproducible, accurate, and automated breast density measurement is required for full-field digital mammography (FFDM) to support clinical applications. We evaluated a novel automated percentage of breast density measure (PDa) and made comparisons with the standard operator-assisted measure (PD) using FFDM data.

METHODS

We used a nested breast cancer case-control study matched on age, year of mammogram and diagnosis with images acquired from a specific direct x-ray conversion FFDM technology. PDa was applied to the raw and clinical display (or processed) representation images. We evaluated the transformation (pixel mapping) of the raw image, giving a third representation (raw-transformed), to improve the PDa performance using differential evolution optimization. We applied PD to the raw and clinical display images as a standard for measurement comparison. Conditional logistic regression was used to estimate the odd ratios (ORs) for breast cancer with 95% confidence intervals (CI) for all measurements; analyses were adjusted for body mass index. PDa operates by evaluating signal-dependent noise (SDN), captured as local signal variation. Therefore, we characterized the SDN relationship to understand the PDa performance as a function of data representation and investigated a variation analysis of the transformation.

RESULTS

The associations of the quartiles of operator-assisted PD with breast cancer were similar for the raw (OR: 1.00 [ref.]; 1.59 [95% CI, 0.93-2.70]; 1.70 [95% CI, 0.95-3.04]; 2.04 [95% CI, 1.13-3.67]) and clinical display (OR: 1.00 [ref.]; 1.31 [95% CI, 0.79-2.18]; 1.14 [95% CI, 0.65-1.98]; 1.95 [95% CI, 1.09-3.47]) images. PDa could not be assessed on the raw images without preprocessing. However, PDa had similar associations with breast cancer when assessed on 1) raw-transformed (OR: 1.00 [ref.]; 1.27 [95% CI, 0.74-2.19]; 1.86 [95% CI, 1.05-3.28]; 3.00 [95% CI, 1.67-5.38]) and 2) clinical display (OR: 1.00 [ref.]; 1.79 [95% CI, 1.04-3.11]; 1.61 [95% CI, 0.90-2.88]; 2.94 [95% CI, 1.66-5.19]) images. The SDN analysis showed that a nonlinear relationship between the mammographic signal and its variation (ie, the biomarker for the breast density) is required for PDa. Although variability in the transform influenced the respective PDa distribution, it did not affect the measurement's association with breast cancer.

CONCLUSIONS

PDa assessed on either raw-transformed or clinical display images is a valid automated breast density measurement for a specific FFDM technology and compares well against PD. Further work is required for measurement generalization.

Automated measurement of volumetric mammographic density: a tool for widespread breast cancer risk assessment

INTRODUCTION

Mammographic density is a strong risk factor for breast cancer and an important determinant of screening sensitivity, but its clinical utility is hampered due to the lack of objective and automated measures. We evaluated the performance of a fully automated volumetric method (Volpara).

METHODS

A prospective cohort study included 41,102 women attending mammography screening, of whom 206 were diagnosed with breast cancer after a median follow-up of 15.2 months. Percent and absolute dense volumes were estimated from raw digital mammograms. Genotyping was performed in a subset of the cohort (N = 2,122). We examined the agreement by side and view and compared density distributions across different mammography systems. We also studied associations with established density determinants and breast cancer risk.

RESULTS

The method showed good agreement by side and view, and distributions of percent and absolute dense volume were similar across mammography systems. Volumetric density was positively associated with nulliparity, age at first birth, hormone use, benign breast disease, and family history of breast cancer, and negatively with age and postmenopausal status. Associations were also observed with rs10995190 in the ZNF365 gene (P < 1.0 × 10(-6)) and breast cancer risk [HR for the highest vs. lowest quartile, 2.93; 95% confidence interval, 1.73-4.96 and 1.63 (1.10-2.42) for percent and absolute dense volume, respectively].

CONCLUSIONS

In a high-throughput setting, Volpara performs well and in accordance with the behavior of established density measures.

IMPACT

Automated measurement of volumetric mammographic density is a promising tool for widespread breast cancer risk assessment.

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.

Radiologist assessment of breast density by BI-RADS categories versus fully automated volumetric assessment

OBJECTIVE

The objective of our study was to estimate mammographic breast density using a fully automated volumetric breast density measurement method in comparison with BI-RADS breast density categories determined by radiologists.

MATERIALS AND METHODS

A total of 791 full-field digital mammography examinations with standard views were evaluated by three blinded radiologists as BI-RADS density categories 1-4. For fully automated volumetric analysis, volumetric breast density was calculated with fully automated software. The volume of fibroglandular tissue, the volume of the breast, and the volumetric percentage density were provided.

RESULTS

The weighted overall kappa was 0.48 (moderate agreement) for the three radiologists' estimates of BI-RADS density. Pairwise comparisons of the radiologists' measurements of BI-RADS density revealed moderate to substantial agreement, with kappa values ranging from 0.51 to 0.64. There was a significant difference in mean volumetric breast density among the BI-RADS density categories, and the mean volumetric breast density increased as the BI-RADS density category increased (p<0.001). A significant positive correlation was found between BI-RADS categories and fully automated volumetric breast density (ρ=0.765, p<0.001).

CONCLUSION

Our study showed good correlation of the fully automated volumetric method with radiologist-assigned BI-RADS density categories. Mammographic density assessment with the fully automated volumetric method may be used to assign BI-RADS density categories.

High mammographic density in women of Ashkenazi Jewish descent

Introduction

Percent mammographic density (PMD) adjusted for age and body mass index is one of the strongest risk factors for breast cancer and is known to be approximately 60% heritable. Here we report a finding of an association between genetic ancestry and adjusted PMD.

Methods

We selected self-identified Caucasian women in the California Pacific Medical Center Research Institute Cohort whose screening mammograms placed them in the top or bottom quintiles of age-adjusted and body mass index-adjusted PMD. Our final dataset included 474 women with the highest adjusted PMD and 469 with the lowest genotyped on the Illumina 1 M platform. Principal component analysis (PCA) and identity-by-descent analyses allowed us to infer the women's genetic ancestry and correlate it with adjusted PMD.

Results

Women of Ashkenazi Jewish ancestry, as defined by the first principal component of PCA and identity-by-descent analyses, represented approximately 15% of the sample. Ashkenazi Jewish ancestry, defined by the first principal component of PCA, was associated with higher adjusted PMD (P = 0.004). Using multivariate regression to adjust for epidemiologic factors associated with PMD, including age at parity and use of postmenopausal hormone therapy, did not attenuate the association.

Conclusions

Women of Ashkenazi Jewish ancestry, based on genetic analysis, are more likely to have high age-adjusted and body mass index-adjusted PMD. Ashkenazi Jews may have a unique set of genetic variants or environmental risk factors that increase mammographic density.

Height, adiposity and body fat distribution and breast density in young women

Introduction

Breast density is one of the strongest risk factors for breast cancer, but determinants of breast density in young women remain largely unknown.

Methods

Associations of height, adiposity and body fat distribution with percentage dense breast volume (%DBV) and absolute dense breast volume (ADBV) were evaluated in a cross-sectional study of 174 healthy women, 25 to 29 years old. Adiposity and body fat distribution were measured by anthropometry and dual-energy X-ray absorptiometry (DXA), while %DBV and ADBV were measured by magnetic resonance imaging. Associations were evaluated using linear mixed-effects models. All tests of statistical significance are two-sided.

Results

Height was significantly positively associated with %DBV but not ADBV; for each standard deviation (SD) increase in height, %DBV increased by 18.7% in adjusted models. In contrast, all measures of adiposity and body fat distribution were significantly inversely associated with %DBV; a SD increase in body mass index (BMI), percentage fat mass, waist circumference and the android:gynoid fat mass ratio (A:G ratio) was each associated significantly with a 44.4 to 47.0% decrease in %DBV after adjustment for childhood BMI and other covariates. Although associations were weaker than for %DBV, all measures of adiposity and body fat distribution also were significantly inversely associated with ADBV before adjustment for childhood BMI. After adjustment for childhood BMI, however, only the DXA measures of percentage fat mass and A:G ratio remained significant; a SD increase in each was associated with a 13.8 to 19.6% decrease in ADBV. In mutually adjusted analysis, the percentage fat mass and the A:G ratio remained significantly inversely associated with %DBV, but only the A:G ratio was significantly associated with ADBV; a SD increase in the A:G ratio was associated with an 18.5% decrease in ADBV.

Conclusion

Total adiposity and body fat distribution are independently inversely associated with %DBV, whereas in mutually adjusted analysis only body fat distribution (A:G ratio) remained significantly inversely associated with ADBV in young women. Research is needed to identify biological mechanisms underlying these associations.

Childhood factors associated with mammographic density in adult women

Growth and development factors could contribute to the development of breast cancer associated with an increase in mammographic density. This study examines the influence of certain childhood-related, socio-demographic and anthropometric variables on mammographic density in adult woman. The study covered 3574 women aged 45-68 years, participating in breast cancer-screening programmes in seven Spanish cities. Based on a craniocaudal mammogram, blind, anonymous measurement of mammographic density was made by a single radiologist, using Boyd's semiquantitative scale. Data associated with the early stages of life were obtained from a direct survey. Ordinal logistic regression and generalised linear models were employed to estimate the association between mammographic density and the variables covered by the questionnaire. Screening programme was introduced as a random effects term. Age, number of children, body mass index (BMI) and other childhood-related variables were used as adjustment variables, and stratified by menopausal status. A total of 811 women (23%) presented mammographic density of over 50%, and 5% of densities exceeded 75%. Our results show a greater prevalence of high mammographic density in women with low prepubertal weight (OR: 1.18; 95% CI: 1.02-1.36); marked prepubertal height (OR: 1.25; 95% CI: 0.97-1.60) and advanced age of their mothers at their birth (>39 years: OR: 1.28; 95% CI: 1.03-1.60); and a lower prevalence of high mammographic density in women with higher prepubertal weight, low birth weight and earlier menarche. The influence of these early-life factors may be explained by greater exposure to hormones and growth factors during the development of the breast gland, when breast tissue would be particularly susceptible to proliferative and carcinogenic stimulus.

Assessment of Breast Cancer Risk Based on Mammary Gland Volume Measured with CT

We investigated the relationship between mammary gland volume (MGV) of the breast as measured with three-dimensional chest computed tomography (CT) and breast cancer risk. Univariate analysis was used to assess the relationship between MGV and known risk factors in 427 healthy women. A case control study (97 cases and 194 controls) was conducted to assess breast cancer risk. MGV was significantly smaller for postmenopausal women than for premenopausal women, and was significantly larger for women with a family history of breast cancer than for women without. MGV, body mass index (BMI), and rate of family history of breast cancer were significantly higher among breast cancer patients than among healthy women, and number of deliveries was significantly lower among breast cancer patients. In postmenopausal women, age at menarche was significantly younger for breast cancer patients. MGV correlated well with breast cancer risk factors. The highest odds ratio was 4.9 for premenopausal women with the largest MGV. Regardless of menopausal status, the greater the MGV, the higher the odds ratio. Our results constitute the first reliable data on the relationship between MGV and breast cancer obtained through exact volume analysis.

Screen-film mammographic density and breast cancer risk: a comparison of the volumetric standard mammogram form and the interactive threshold measurement methods

BACKGROUND

Mammographic density is a strong risk factor for breast cancer, usually measured by an area-based threshold method that dichotomizes the breast area on a mammogram into dense and nondense regions. Volumetric methods of breast density measurement, such as the fully automated standard mammogram form (SMF) method that estimates the volume of dense and total breast tissue, may provide a more accurate density measurement and improve risk prediction.

METHODS

In 2000-2003, a case-control study was conducted of 367 newly confirmed breast cancer cases and 661 age-matched breast cancer-free controls who underwent screen-film mammography at several centers in Toronto, Canada. Conditional logistic regression was used to estimate odds ratios of breast cancer associated with categories of mammographic density, measured with both the threshold and the SMF (version 2.2beta) methods, adjusting for breast cancer risk factors.

RESULTS

Median percent density was higher in cases than in controls for the threshold method (31% versus 27%) but not for the SMF method. Higher correlations were observed between SMF and threshold measurements for breast volume/area (Spearman correlation coefficient = 0.95) than for percent density (0.68) or for absolute density (0.36). After adjustment for breast cancer risk factors, odds ratios of breast cancer in the highest compared with the lowest quintile of percent density were 2.19 (95% confidence interval, 1.28-3.72; P(t) <0.01) for the threshold method and 1.27 (95% confidence interval, 0.79-2.04; Pt = 0.32) for the SMF method.

CONCLUSION

Threshold percent density is a stronger predictor of breast cancer risk than the SMF version 2.2beta method in digitized images.

Birth weight and mammographic density among postmenopausal women in Sweden

Birth weight is a significant predictor of breast cancer risk in adult life and mammary gland mass could be an intermediate stage in this long process. We have studied the association of birth size measurements with mammographic density, a marker of mammary gland mass. For a population-based sample of 893 postmenopausal women without previous cancer in Sweden, we retrieved information on birth size from birth records and their most recent mammography. Film mammograms of the medio-lateral oblique view were digitized and the Cumulus software was used for computer-assisted semi-automated thresholding of mammographic density. Results were analyzed using generalized linear models controlling for possible confounders. Mean percent mammographic density increased when comparing the extreme categories of birth weight (from 15.6% to 18.6%) and head circumference (from 15.5% to 20.4%), and the corresponding linear trends were statistically significant (p values 0.02 and 0.007, respectively). The associations were particularly strong when the cutoff for high versus low mammographic density was set at the relatively high value of 50%. Compared to women weighing 3001-3500 grams at birth, women with birth weights >4000g were at almost 3-fold risk of developing high mammographic density (odds ratio: 2.9, 95% confidence interval 1.1 to 7.9). No association with mammographic density was evident with respect to birth length which, however, is known to be less accurately measured. These results indicate that adult breast density, a powerful predictor of breast cancer risk, has intrauterine roots, as reflected in birth size.

Dense Breast Tissue as an Important Risk Factor for Breast Cancer and Implications for Early Detection

More than 30 years ago, John Wolfe was the first to observe and describe the association between breast density on mammography and increased breast cancer risk. Following this pioneer work, there is now compelling evidence that density in the highest quartile represents a 4-6 times higher risk of breast cancer. This magnitude of risk is only topped by age and BRCA1/2 mutation. The density-based risk is independent of age and other risk factors. Apart from epidemiologic risk factors, additional genetic factors seem to influence density. This could be the reason behind the well-known interaction between genes and environment. Reliable and reproducible breast density measurements are a prerequisite for the use of breast density to monitor primary prevention strategies and for the use of mammographic density to define women at higher breast cancer risk who would benefit from intensified early detection and surveillance protocols.

Breast size, bra fit and thoracic pain in young women: a correlational study

INTRODUCTION

A single sample study was undertaken to determine the strength and direction of correlations between: a) breast size and thoracic spine or posterior chest wall pain; b) bra fit and thoracic spine or posterior chest wall pain and; c) breast size and bra fit, in thirty nulliparous women (18-26 years), with thoracic spine or posterior chest wall pain, who wore bras during daytime.

MEASURES

Pain (Short Form McGill Pain Questionnaire), bra size (Triumph International), bra fit (Triumph International).

RESULTS

Most (80%) women wore incorrectly sized bras: 70% wore bras that were too small, 10% wore bras that were too large. Breast size was negatively correlated with both bra size (r = -0.78) and bra fit (r = -0.50). These results together indicate that large breasted women were particularly likely to be wearing incorrectly sized and fitted bras. Negligible relationships were found between pain and bra fit, and breast size and pain. Menstrual cycle stage was moderately positively correlated with bra fit (r = 0.32).

CONCLUSION

In young, nulliparous women, thoracic pain appears unrelated to breast size. Bra fit is moderately related to stage of menstrual cycle suggesting that this research may be somewhat confounded by hormonal changes or reproductive stage. Further research is needed to clarify whether there is a relationship between breast size or bra fit and thoracic pain in women during times of hormonal change.