Mammographic parenchymal patterns and mode of detection: implications for the breast screening programme

Introduction of one-view tomosynthesis in population-based mammography screening: Impact on detection rate, interval cancer rate and false-positive rate

OBJECTIVE

To assess performance endpoints of a combination of digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) compared with FFDM only in breast cancer screening.

MATERIALS AND METHODS

This was a prospective population-based screening study, including eligible (50-69 years) women attending the Capital Region Mammography Screening Program in Denmark. All attending women were offered FFDM. A subgroup was consecutively allocated to a screening room with DBT. All FFDM and DBT underwent independent double reading, and all women were followed up for 2 years after screening date or until next screening date, whichever came first.

RESULTS

6353 DBT + FFDM and 395 835 FFDM were included in the analysis and were undertaken in 196 267 women in the period from 1 November 2012 to 12 December 2018. Addition of DBT increased sensitivity: 89.9% (95% confidence interval (CI): 81.0-95.5) for DBT + FFDM and 70.1% (95% CI: 68.6-71.6) for FFDM only, p < 0.001. Specificity remained similar: 98.2% (95% CI: 97.9-98.5) for DBT + FFDM and 98.3% (95% CI: 98.2-98.3) for FFDM only, p = 0.9. Screen-detected cancer rate increased statistically significantly: 11.18/1000 for DBT + FFDM and 6.49/1000 for FFDM only, p < 0.001. False-positive rate was unchanged: 1.75% for DBT + FFDM and 1.73% for FFDM only, p = 0.9. Positive predictive value for recall was 39.0% (95% CI: 31.9-46.5) for DBT + FFDM and 27.3% (95% CI: 26.4-28.2), for FFDM only, p < 0.0005. The interval cancer rate decreased: 1.26/1000 for DBT + FFDM and 2.76/1000 for FFDM only, p = 0.02.

CONCLUSION

DBT + FFDM yielded a statistically significant increase in cancer detection and program sensitivity.

A pictorial guide to artifacts on contrast mammography: How to avoid pitfalls and improve interpretation

Contrast-enhanced mammography (CEM) is an increasingly accepted emerging imaging modality that demonstrates a similar sensitivity to MRI but has the advantage of being less time consuming and inexpensive. The use of CEM continues to expand as it is recognized and utilized as a valuable tool for diagnostic and potentially screening examinations. As with any radiologic examination, artifacts occur and knowledge of these is important for adequate image interpretation. The purpose of this paper is to provide a pictorial review the common artifacts encountered on CEM examinations and identify causes and potential resolutions.

Factors Associated With Breast Cancer Screening Behaviors Among Women With Dense Breasts

OBJECTIVE

We sought to identify patient factors associated with patient-reported screening behaviors in women with dense breasts.

METHODS

An IRB-approved survey study of women with dense breasts presenting for annual screening mammography at an outpatient imaging center was previously conducted from March 2017 to February 2018. The survey included questions regarding mammographic screening frequency and recent participation in supplemental screening. These survey data were combined post hoc with clinical and demographic data and socioeconomic data imputed from census data. Logistic regression was used to identify patient factors associated with reported screening behaviors.

RESULTS

Surveys were completed by 508 women (median age, 59.0 years; range, 31.0-86.0 years) with dense breasts. Multivariable analysis demonstrated an independent association of undergoing mammographic screening annually with a history of discussing breast density with a doctor (adjusted odds ratio [AOR], 2.60; P = 0.019). Undergoing supplemental screening in the previous three years was independently associated with younger age (AOR, 1.59; P = 0.004), strong family history of breast cancer (AOR, 3.84; P = 0.027), higher perceived personal risk for breast cancer (AOR, 3.47; P = 0.004), and increased concern about radiation associated with screening examinations (AOR, 3.31; P = 0.006).

CONCLUSION

Women with dense breasts who had discussed breast density with a doctor were more likely to report undergoing annual screening mammography, while younger women and women with a strong family history of breast cancer, higher perceived personal risk for breast cancer, or greater concern about radiation were more likely to report recently undergoing supplemental screening.

Volumetric breast density evaluation using fully automated Volpara software, its comparison with BIRADS density types and correlation with the risk of malignancy

Background

Mammography is currently the modality of choice for mass screening of breast cancer, although its sensitivity is low in dense breasts. Besides, higher breast density has been identified as independent risk factor so it has been conceptualized that women with dense breasts should be encouraged for supplemental screening. In this study, we aimed to estimate the distribution of volumetric breast density using fully automated Volpara software and to analyze the level of agreement between volumetric density grades and Breast Imaging Reporting and Data System (BI-RADS) density grades. We also aim to estimate the distribution of breast cancer in different VDG and to find a correlation between VDG and risk of malignancy.

Results

VDG-c was most common followed by VDG-b and BIRADS grade B was commonest followed by grade C. The density distribution was found inversely related to the age. Level of agreement between VDG and BIRADS grades was moderate (κ = 0.5890). Statistically significant correlation was noted between VDG-c and d for risk of malignancy (p < 0.001).

Conclusion

Difficulties associated with the use of BI-RADS density categories may be avoided if assessed using a fully automated volumetric method. High VDG can be considered as independent risk factor for malignancy. Thus, awareness of a woman’s breast density might be useful in determining the frequency and imaging modality for screening.

Computational Breast Anatomy Simulation Using Multi-Scale Perlin Noise

Virtual clinical trials (VCTs) of medical imaging require realistic models of human anatomy. For VCTs in breast imaging, a multi-scale Perlin noise method is proposed to simulate anatomical structures of breast tissue in the context of an ongoing breast phantom development effort. Four Perlin noise distributions were used to replace voxels representing the tissue compartments and Cooper's ligaments in the breast phantoms. Digital mammography and tomosynthesis projections were simulated using a clinical DBT system configuration. Power-spectrum analyses and higher-order statistics properties using Laplacian fractional entropy (LFE) of the parenchymal texture are presented. These objective measures were calculated in phantom and patient images using a sample of 140 clinical mammograms and 500 phantom images. Power-law exponents were calculated using the slope of the curve fitted in the low frequency [0.1, 1.0] mm-1 region of the power spectrum. The results show that the images simulated with our prior and proposed Perlin method have similar power-law spectra when compared with clinical mammograms. The power-law exponents calculated are -3.10, -3.55, and -3.46, for the log-power spectra of patient, prior phantom and proposed phantom images, respectively. The results also indicate an improved agreement between the mean LFE estimates of Perlin-noise based phantoms and patients than our prior phantoms and patients. Thus, the proposed method improved the simulation of anatomic noise substantially compared to our prior method, showing close agreement with breast parenchyma measures.

Annual vs Biennial Screening: Diagnostic Accuracy Among Concurrent Cohorts Within the Ontario Breast Screening Program

BACKGROUND

The Ontario Breast Screening Program recommends annual mammography to women age 50-74 years at increased risk because of family history of breast or ovarian cancer or personal history of ovarian cancer or mammographic density 75% or greater. Few studies have examined the diagnostic accuracy of recommendations based on risk factors and included screen film as well as digital mammography.

METHODS

A retrospective design identified concurrent cohorts of women age 50-74 years screened annually or biennially with digital mammography only between 2011 and 2014 and followed until 2016 or breast cancer diagnosis. Diagnostic accuracy measures were compared between women screened annually because of first-degree relative of breast or ovarian cancer or personal history of ovarian cancer (n = 67 795 women), mammographic density 75% or greater (n = 51 956), or both (n = 3758) and those screened biennially (n = 526 815). The association between recommendation and sensitivity and specificity was assessed using generalized estimating equation models. All P values are two-sided.

RESULTS

For annual screening because of family or personal history vs biennial, sensitivity was statistically significantly higher (81.7% vs 70.6%; OR = 1.86, 95% CI = 1.48 to 2.34), particularly for invasive cancers and postmenopausal women. Although there was no statistically significant difference in sensitivity for annual screening for mammographic density 75% or greater, specificity was statistically significantly lower (91.3%; OR = 0.87, 95% CI = 0.80 to 0.96) vs biennial (92.3%), particularly for women age 50-59 years.

CONCLUSION

Compared with biennial screening, annual screening improved detection for women with a family or personal history of breast and/or ovarian cancer, supporting screening that is more frequent. The benefit for annual screening for women with higher mammographic density must be weighed against possible harms of increased false positives.

An overview of mammographic density and its association with breast cancer

In 2017, breast cancer became the most commonly diagnosed cancer among women in the US. After lung cancer, breast cancer is the leading cause of cancer-related mortality in women. The breast consists of several components, including milk storage glands, milk ducts made of epithelial cells, adipose tissue, and stromal tissue. Mammographic density (MD) is based on the proportion of stromal, epithelial, and adipose tissue. Women with high MD have more stromal and epithelial cells and less fatty adipose tissue, and are more likely to develop breast cancer in their lifetime compared to women with low MD. Because of this correlation, high MD is an independent risk factor for breast cancer. Further, mammographic screening is less effective in detecting suspicious lesions in dense breast tissue, which can lead to late-stage diagnosis. Molecular differences between dense and non-dense breast tissues explain the underlying biological reasons for why women with dense breasts are at a higher risk for developing breast cancer. The goal of this review is to highlight the current molecular understanding of MD, its association with breast cancer risk, the demographics pertaining to MD, and the environmental factors that modulate MD. Finally, we will review the current legislation regarding the disclosure of MD on a traditional screening mammogram and the supplemental screening options available to women with dense breast tissue.

Breast density and breast cancer-specific survival by detection mode

Background

Breast density is known to affect breast cancer risk and screening sensitivity, but it may also be associated with breast cancer survival. The interpretation of results from previous studies on breast density and survival is complicated by the association between detection mode and survival. Here, we studied the effect of breast density on breast cancer-specific survival for different detection modes (screen-detected, interval ≤ 24 or > 24 months, non-participant).

Methods

Data from the Nijmegen (Dutch) breast cancer screening programme were used. Women diagnosed with invasive breast cancer between 1975 and 2011 were included. Breast density was assessed visually, based on a dichotomized Wolfe scale: ‘fatty breasts’ (≤25%) and ‘dense breasts’ (> 25%). Cox proportional hazard regression was used to obtain hazard ratios (HR).

Results

We identified 2742 eligible women, with a breast pattern available for 2233 women. A diagnosis of interval cancer (HR 2.06, 95% CI 1.62–2.61) led to a significantly increased risk of breast cancer death compared with screen-detected cancer. No significant cause-specific survival difference between women with dense and fatty breasts was observed (HR 0.94, 95% CI 0.77–1.15). The hazard was only higher for women with dense breasts among interval cancers ≤24 m (HR 1.07, 95% CI 0.74–1.56). The hazard appeared to be lower for women with dense breasts than for women with fatty breasts among screen-detected (HR 0.77, 95% CI 0.53–1.11) and interval cancers > 24 m (HR 0.80, 95% CI 0.53–1.20). None of the effects were statistically significant.

Conclusions

Detection mode is strongly associated with breast cancer death. No clear association is apparent between breast density and breast cancer death, regardless of detection mode.

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.

Molecular Breast Imaging in Breast Cancer Screening and Problem Solving

In the United States, legislative actions in over 28 states require radiologists to notify women who undergo breast screening mammography of their breast density. This has led to increased public interest in supplemental screening, but radiologists have not come to a consensus on a supplemental screening modality. In choosing between the most common options, whole-breast ultrasonography (US) and magnetic resonance (MR) imaging, one must weigh the benefits and drawbacks of each modality, as increased cancer detection may be accompanied by increased examination costs and biopsy rates. There has been recent interest in molecular breast imaging (MBI) for supplemental screening because of its high sensitivity, as well as its high specificity. This article describes how MBI fits into clinical practice alongside digital breast tomosynthesis (DBT), targeted US, and MR imaging. The authors describe their approach to breast cancer screening, which uses DBT as the primary imaging modality. DBT is complemented by automated density calculations and supplemented with functional imaging techniques, including MR imaging or MBI, for women with dense breasts. An algorithm based on the patient's breast cancer risk is used to determine if either MR imaging or MBI for supplemental screening is appropriate. MBI is also used as a problem-solving tool for the evaluation of clinical indications following complex mammography or US, or for unexplained physical findings. This article describes aspects related to implementing MBI in clinical practice, including the clinical workflow, patient management, radioactive tracer administration, and procedure reimbursement. ^© RSNA, 2017.

Local breast density assessment using reacquired mammographic images

PURPOSE

The aim of this paper is to evaluate the spatial glandular volumetric tissue distribution as well as the density measures provided by Volpara™ using a dataset composed of repeated pairs of mammograms, where each pair was acquired in a short time frame and in a slightly changed position of the breast.

MATERIALS AND METHODS

We conducted a retrospective analysis of 99 pairs of repeatedly acquired full-field digital mammograms from 99 different patients. The commercial software Volpara™ Density Maps (Volpara Solutions, Wellington, New Zealand) is used to estimate both the global and the local glandular tissue distribution in each image. The global measures provided by Volpara™, such as breast volume, volume of glandular tissue, and volumetric breast density are compared between the two acquisitions. The evaluation of the local glandular information is performed using histogram similarity metrics, such as intersection and correlation, and local measures, such as statistics from the difference image and local gradient correlation measures.

RESULTS

Global measures showed a high correlation (breast volume R=0.99, volume of glandular tissue R=0.94, and volumetric breast density R=0.96) regardless the anode/filter material. Similarly, histogram intersection and correlation metric showed that, for each pair, the images share a high degree of information. Regarding the local distribution of glandular tissue, small changes in the angle of view do not yield significant differences in the glandular pattern, whilst changes in the breast thickness between both acquisition affect the spatial parenchymal distribution.

CONCLUSIONS

This study indicates that Volpara™ Density Maps is reliable in estimating the local glandular tissue distribution and can be used for its assessment and follow-up. Volpara™ Density Maps is robust to small variations of the acquisition angle and to the beam energy, although divergences arise due to different breast compression conditions.

Radiologic findings of screen-detected cancers in an organized population-based screening mammography program in Turkey

PURPOSE

Bahçeşehir Breast Cancer Screening Program is a population based organized screening program in Turkey, where asymptomatic women aged 40-69 years are screened biannually. In this prospective study, we aimed to determine the mammographic findings of screen-detected cancers and discuss the efficacy of breast cancer screening in a developing country.

METHODS

A total of 6912 women were screened in three rounds. The radiologic findings were grouped as mass, focal asymmetry, calcification, and architectural distortion. Masses were classified according to shape, border, and density. Calcifications were grouped according to morphology and distribution. Cancers were grouped according to the clinical stage.

RESULTS

Seventy cancers were detected with an incidence of 4.8/1000. Two cancers were detected in other centers and three were not visualized mammographically. Mammographic presentations of the remaining 65 cancers were mass (47.7%, n=31), calcification (30.8%, n=20), focal asymmetry (16.9%, n=11), architectural distortion (3.1%, n=2), and skin thickening (1.5%, n=1). The numbers of stage 0, 1, 2, 3, and 4 cancers were 13 (20.0%), 34 (52.3%), 14 (21.5%), 3 (4.6%), and 1 (1.5%), respectively. The numbers of interval and missed cancers were 5 (7.4%) and 7 (10.3%), respectively.

CONCLUSION

A high incidence of early breast cancer has been detected. The incidence of missed and interval cancers did not show major differences from western screening trials. We believe that this study will pioneer implementation of efficient population-based mammographic screenings in developing countries.

Mammographic density and structural features can individually and jointly contribute to breast cancer risk assessment in mammography screening: a case-control study

Background

Mammographic density is a well-established risk factor for breast cancer. We investigated the association between three different methods of measuring density or parenchymal pattern/texture on digitized film-based mammograms, and examined to what extent textural features independently and jointly with density can improve the ability to identify screening women at increased risk of breast cancer.

Methods

The study included 121 cases and 259 age- and time matched controls based on a cohort of 14,736 women with negative screening mammograms from a population-based screening programme in Denmark in 2007 (followed until 31 December 2010). Mammograms were assessed using the Breast Imaging-Reporting and Data System (BI-RADS) density classification, Tabár’s classification on parenchymal patterns and a fully automated texture quantification technique. The individual and combined association with breast cancer was estimated using binary logistic regression to calculate Odds Ratios (ORs) and the area under the receiver operating characteristic (ROC) curves (AUCs).

Results

Cases showed significantly higher BI-RADS and texture scores on average than controls (p < 0.001). All three methods were individually able to segregate women into different risk groups showing significant ORs for BI-RADS D3 and D4 (OR: 2.37; 1.32–4.25 and 3.93; 1.88–8.20), Tabár’s PIII and PIV (OR: 3.23; 1.20–8.75 and 4.40; 2.31–8.38), and the highest quartile of the texture score (3.04; 1.63–5.67). AUCs for BI-RADS, Tabár and the texture scores (continuous) were 0.63 (0.57–0–69), 0.65 (0.59–0–71) and 0.63 (0.57–0–69), respectively. Combining two or more methods increased model fit in all combinations, demonstrating the highest AUC of 0.69 (0.63-0.74) when all three methods were combined (a significant increase from standard BI-RADS alone).

Conclusion

Our findings suggest that the (relative) amount of fibroglandular tissue (density) and mammographic structural features (texture/parenchymal pattern) jointly can improve risk segregation of screening women, using information already available from normal screening routine, in respect to future personalized screening strategies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-016-2450-7) contains supplementary material, which is available to authorized users.

Digital versus screen-film mammography: impact of mammographic density and hormone therapy on breast cancer detection

Most studies that have examined the effects of mammographic density and hormone therapy use on breast cancer detection have included screen-film mammography. This study further examines this association in post-menopausal women screened by digital mammography. Approved by the University of Toronto Research Ethics Board, this study identified 688,418 women of age 50-74 years screened with digital or screen-film mammography from 2008 to 2009 within the Ontario Breast Screening Program. Of 2993 eligible women with invasive breast cancer, 2450 were contacted and 1421 participated (847 screen-film mammography, 574 digital direct radiography). Mammographic density was measured by study radiologists using the standard BI-RADS classification system and by a computer-assisted method. Information on hormone therapy use was collected by a telephone-administered questionnaire. Logistic regression and two-tailed tests for significance evaluated associations between factors and detection method by mammography type. Women with >75 % radiologist-measured mammographic density compared to those with <25 % were more likely to be diagnosed with an interval than screen-detected cancer, with the difference being greater for those screened with screen-film (OR = 6.40, 95 % CI 2.30-17.85) than digital mammography (OR = 2.41, 95 % CI 0.67-8.58) and aged 50-64 years screened with screen-film mammography (OR = 10.86, 95 % CI 2.96-39.57). Recent former hormone therapy users were also at an increased risk of having an interval cancer with the association being significant for women screened with digital mammography (OR = 2.08, 95 % CI 1.17-3.71). Breast screening using digital mammography lowers the risk of having an interval cancer for post-menopausal women aged 50-64 with greater mammographic density.

A Review on Automatic Mammographic Density and Parenchymal Segmentation

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

Inter-observer agreement according to three methods of evaluating mammographic density and parenchymal pattern in a case control study: impact on relative risk of breast cancer

Background

Mammographic breast density and parenchymal patterns are well-established risk factors for breast cancer. We aimed to report inter-observer agreement on three different subjective ways of assessing mammographic density and parenchymal pattern, and secondarily to examine what potential impact reproducibility has on relative risk estimates of breast cancer.

Methods

This retrospective case–control study included 122 cases and 262 age- and time matched controls (765 breasts) based on a 2007 screening cohort of 14,736 women with negative screening mammograms from Bispebjerg Hospital, Copenhagen. Digitised randomized film-based mammograms were classified independently by two readers according to two radiological visual classifications (BI-RADS and Tabár) and a computerized interactive threshold technique measuring area-based percent mammographic density (denoted PMD). Kappa statistics, Intraclass Correlation Coefficient (ICC) (equivalent to weighted kappa), Pearson’s linear correlation coefficient and limits-of-agreement analysis were used to evaluate inter-observer agreement. High/low-risk agreement was also determined by defining the following categories as high-risk: BI-RADS’s D3 and D4, Tabár’s PIV and PV and the upper two quartiles (within density range) of PMD. The relative risk of breast cancer was estimated using logistic regression to calculate odds ratios (ORs) adjusted for age, which were compared between the two readers.

Results

Substantial inter-observer agreement was seen for BI-RADS and Tabár (κ=0.68 and 0.64) and agreement was almost perfect when ICC was calculated for the ordinal BI-RADS scale (ICC=0.88) and the continuous PMD measure (ICC=0.93). The two readers judged 5% (PMD), 10% (Tabár) and 13% (BI-RADS) of the women to different high/low-risk categories, respectively. Inter-reader variability showed different impact on the relative risk of breast cancer estimated by the two readers on a multiple-category scale, however, not on a high/low-risk scale. Tabár’s pattern IV demonstrated the highest ORs of all density patterns investigated.

Conclusions

Our study shows the Tabár classification has comparable inter-observer reproducibility with well tested density methods, and confirms the association between Tabár’s PIV and breast cancer. In spite of comparable high inter-observer agreement for all three methods, impact on ORs for breast cancer seems to differ according to the density scale used. Automated computerized techniques are needed to fully overcome the impact of subjectivity.

AutoDensity: an automated method to measure mammographic breast density that predicts breast cancer risk and screening outcomes

Introduction

While Cumulus – a semi-automated method for measuring breast density – is utilised extensively in research, it is labour-intensive and unsuitable for screening programmes that require an efficient and valid measure on which to base screening recommendations. We develop an automated method to measure breast density (AutoDensity) and compare it to Cumulus in terms of association with breast cancer risk and breast cancer screening outcomes.

Methods

AutoDensity automatically identifies the breast area in the mammogram and classifies breast density in a similar way to Cumulus, through a fast, stand-alone Windows or Linux program. Our sample comprised 985 women with screen-detected cancers, 367 women with interval cancers and 4,975 controls (women who did not have cancer), sampled from first and subsequent screening rounds of a film mammography screening programme. To test the validity of AutoDensity, we compared the effect estimates using AutoDensity with those using Cumulus from logistic regression models that tested the association between breast density and breast cancer risk, risk of small and large screen-detected cancers and interval cancers, and screening programme sensitivity (the proportion of cancers that are screen-detected). As a secondary analysis, we report on correlation between AutoDensity and Cumulus measures.

Results

AutoDensity performed similarly to Cumulus in all associations tested. For example, using AutoDensity, the odds ratios for women in the highest decile of breast density compared to women in the lowest quintile for invasive breast cancer, interval cancers, large and small screen-detected cancers were 3.2 (95% CI 2.5 to 4.1), 4.7 (95% CI 3.0 to 7.4), 6.4 (95% CI 3.7 to 11.1) and 2.2 (95% CI 1.6 to 3.0) respectively. For Cumulus the corresponding odds ratios were: 2.4 (95% CI 1.9 to 3.1), 4.1 (95% CI 2.6 to 6.3), 6.6 (95% CI 3.7 to 11.7) and 1.3 (95% CI 0.9 to 1.8). Correlation between Cumulus and AutoDensity measures was 0.63 (P < 0.001).

Conclusions

Based on the similarity of the effect estimates for AutoDensity and Cumulus in models of breast density and breast cancer risk and screening outcomes, we conclude that AutoDensity is a valid automated method for measuring breast density from digitised film mammograms.

Mammographic features associated with interval breast cancers in screening programs

INTRODUCTION

Percent mammographic density (PMD) is associated with an increased risk of interval breast cancer in screening programs, as are younger age, pre-menopausal status, lower body mass index and hormone therapy. These factors are also associated with variations in PMD. We have examined whether these variables influence the relative frequency of interval and screen-detected breast cancer, independently or through their associations with PMD. We also examined the association of tumor size with PMD and dense and non-dense areas in screen-detected and interval breast cancers.

METHODS

We used data from three case-control studies nested in screened populations. Interval breast cancer was defined as invasive breast cancer detected within 12 months of a negative mammogram. We used a computer-assisted method of measuring the dense and total areas of breast tissue in the first (baseline) mammogram taken at entry to screening programs and calculated the non-dense area and PMD. We compared these mammographic features, and other risk factors at baseline, in women with screen-detected (n = 718) and interval breast cancer (n = 125).

RESULTS

In multi-variable analysis, the baseline characteristics of younger age, greater dense area and smaller non-dense mammographic area were significantly associated with interval breast cancer compared to screen-detected breast cancer. Compared to screen-detected breast cancers, interval cancers had a larger maximum tumor diameter within each mammographic measure.

CONCLUSIONS

Age and the dense and non-dense areas in the baseline mammogram were independently associated with interval breast cancers in screening programs. These results suggest that decreased detection of cancers caused by the area of dense tissue, and more rapid growth associated with a smaller non-dense area, may both contribute to risk of interval breast cancer. Tailoring screening to individual mammographic characteristics at baseline may reduce the number of interval cancers.

Beyond mammography: new frontiers in breast cancer screening

Breast cancer screening remains a subject of intense and, at times, passionate debate. Mammography has long been the mainstay of breast cancer detection and is the only screening test proven to reduce mortality. Although it remains the gold standard of breast cancer screening, there is increasing awareness of subpopulations of women for whom mammography has reduced sensitivity. Mammography also has undergone increased scrutiny for false positives and excessive biopsies, which increase radiation dose, cost, and patient anxiety. In response to these challenges, new technologies for breast cancer screening have been developed, including low-dose mammography, contrast-enhanced mammography, tomosynthesis, automated whole breast ultrasound, molecular imaging, and magnetic resonance imaging. Here we examine some of the current controversies and promising new technologies that may improve detection of breast cancer both in the general population and in high-risk groups, such as women with dense breasts. We propose that optimal breast cancer screening will ultimately require a personalized approach based on metrics of cancer risk with selective application of specific screening technologies best suited to the individual's age, risk, and breast density.

Evaluation of the kinetic properties of background parenchymal enhancement throughout the phases of the menstrual cycle

PURPOSE

To develop and apply a semiautomatic method of segmenting fibroglandular tissue to quantify magnetic resonance (MR) imaging contrast material-enhancement kinetics of breast background parenchyma (BP) and lesions throughout the phases of the menstrual cycle in women with benign and malignant lesions.

MATERIALS AND METHODS

The institutional review board approved this retrospective HIPAA-compliant study, and informed consent was waived. From December 2008 to August 2011, 58 premenopausal women who had undergone contrast material-enhanced MR imaging and MR imaging-guided biopsy were identified. The longest time from the start of the last known period was 34 days. One lesion per patient (37 benign and 21 malignant) was analyzed. The patient groups were stratified according to the week of the menstrual cycle when MR imaging was performed. A method based on principal component analysis (PCA) was applied for quantitative analysis of signal enhancement in the BP and lesions by using the percentage of slope and percentage of enhancement. Linear regression and the Mann-Whitney U test were used to assess the association between the kinetic parameters and the week of the menstrual cycle.

RESULTS

In the women with benign lesions, percentages of slope and enhancement for both BP and lesions during week 2 were significantly (P < .05) lower than those in week 4. Percentage of enhancement in the lesion in week 2 was lower than that in week 3 (P < .05). The MR images of women with malignant lesions showed no significant difference between the weeks for any of the parameters. There was a strong positive correlation between lesion and BP percentage of slope (r = 0.72) and between lesion and BP percentage of enhancement (r = 0.67) in the benign group. There was also a significant (P = .03) difference in lesion percentage of slope between the benign and malignant groups at week 2.

CONCLUSION

The PCA-based method can quantify contrast enhancement kinetics of BP semiautomatically, and kinetics of BP and lesions vary according to the week of the menstrual cycle in benign but not in malignant lesions.

Relationship between mammographic density and breast cancer death in the Breast Cancer Surveillance Consortium

BACKGROUND

Women with elevated mammographic density have an increased risk of developing breast cancer. However, among women diagnosed with breast cancer, it is unclear whether higher density portends reduced survival, independent of other factors.

METHODS

We evaluated relationships between mammographic density and risk of death from breast cancer and all causes within the US Breast Cancer Surveillance Consortium. We studied 9232 women diagnosed with primary invasive breast carcinoma during 1996-2005, with a mean follow-up of 6.6 years. Mammographic density was assessed using the Breast Imaging Reporting and Data System (BI-RADS) density classification. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated by Cox proportional hazards regression; women with scattered fibroglandular densities (BI-RADS 2) were the referent group. All statistical tests were two-sided.

RESULTS

A total of 1795 women died, of whom 889 died of breast cancer. In multivariable analyses (adjusted for site, age at and year of diagnosis, American Joint Committee on Cancer stage, body mass index, mode of detection, treatment, and income), high density (BI-RADS 4) was not related to risk of death from breast cancer (HR = 0.92, 95% CI = 0.71 to 1.19) or death from all causes (HR = 0.83, 95% CI = 0.68 to 1.02). Analyses stratified by stage and other prognostic factors yielded similar results, except for an increased risk of breast cancer death among women with low density (BI-RADS 1) who were either obese (HR = 2.02, 95% CI = 1.37 to 2.97) or had tumors of at least 2.0 cm (HR = 1.55, 95% CI = 1.14 to 2.09).

CONCLUSIONS

High mammographic breast density was not associated with risk of death from breast cancer or death from any cause after accounting for other patient and tumor characteristics. Thus, risk factors for the development of breast cancer may not necessarily be the same as factors influencing the risk of death after breast cancer has developed.

Mammographic density and its interaction with other breast cancer risk factors in an Asian population

BACKGROUND

Joint effects of mammographic density and other risk factors on breast cancer risk remain unclear.

METHODS

From The Singapore Breast Screening Project, we selected 491 cases and 982 controls. Mammographic density was measured quantitatively. Data analysis was by conditional logistic regression.

RESULTS

Density was a significant risk factor, adjusting for other factors. Density of 76-100% had an odds ratio of 5.54 (95% CI 2.38-12.90) compared with 0-10%. Density had significant interactions with body mass index and oral contraceptive use (P=0.02).

CONCLUSIONS

Percent density increases breast cancer risk in addition to effects of other risk factors, and modifies the effects of BMI and OCs.

Breast tissue composition and susceptibility to breast cancer

Breast density, as assessed by mammography, reflects breast tissue composition. Breast epithelium and stroma attenuate x-rays more than fat and thus appear light on mammograms while fat appears dark. In this review, we provide an overview of selected areas of current knowledge about the relationship between breast density and susceptibility to breast cancer. We review the evidence that breast density is a risk factor for breast cancer, the histological and other risk factors that are associated with variations in breast density, and the biological plausibility of the associations with risk of breast cancer. We also discuss the potential for improved risk prediction that might be achieved by using alternative breast imaging methods, such as magnetic resonance or ultrasound. After adjustment for other risk factors, breast density is consistently associated with breast cancer risk, more strongly than most other risk factors for this disease, and extensive breast density may account for a substantial fraction of breast cancer. Breast density is associated with risk of all of the proliferative lesions that are thought to be precursors of breast cancer. Studies of twins have shown that breast density is a highly heritable quantitative trait. Associations between breast density and variations in breast histology, risk of proliferative breast lesions, and risk of breast cancer may be the result of exposures of breast tissue to both mitogens and mutagens. Characterization of breast density by mammography has several limitations, and the uses of breast density in risk prediction and breast cancer prevention may be improved by other methods of imaging, such as magnetic resonance or ultrasound tomography.

Validation of a method for measuring the volumetric breast density from digital mammograms

The purpose of this study was to evaluate the performance of an algorithm used to measure the volumetric breast density (VBD) from digital mammograms. The algorithm is based on the calibration of the detector signal versus the thickness and composition of breast-equivalent phantoms. The baseline error in the density from the algorithm was found to be 1.25 +/- 2.3% VBD units (PVBD) when tested against a set of calibration phantoms, of thicknesses 3-8 cm, with compositions equivalent to fibroglandular content (breast density) between 0% and 100% and under x-ray beams between 26 kVp and 32 kVp with a Rh/Rh anode/filter. The algorithm was also tested against images from a dedicated breast computed tomography (CT) scanner acquired on 26 volunteers. The CT images were segmented into regions representing adipose, fibroglandular and skin tissues, and then deformed using a finite-element algorithm to simulate the effects of compression in mammography. The mean volume, VBD and thickness of the compressed breast for these deformed images were respectively 558 cm(3), 23.6% and 62 mm. The displaced CT images were then used to generate simulated digital mammograms, considering the effects of the polychromatic x-ray spectrum, the primary and scattered energy transmitted through the breast, the anti-scatter grid and the detector efficiency. The simulated mammograms were analyzed with the VBD algorithm and compared with the deformed CT volumes. With the Rh/Rh anode filter, the root mean square difference between the VBD from CT and from the algorithm was 2.6 PVBD, and a linear regression between the two gave a slope of 0.992 with an intercept of -1.4 PVBD and a correlation with R(2) = 0.963. The results with the Mo/Mo and Mo/Rh anode/filter were similar.

Mammographic density and markers of socioeconomic status: a cross-sectional study

Background

Socioeconomic status (SES) is known to be positively associated with breast cancer risk but its relationship with mammographic density, a marker of susceptibility to breast cancer, is unclear. This study aims to investigate whether mammographic density varies by SES and to identify the underlying anthropometric, lifestyle and reproductive factors leading to such variation.

Methods

In a cross-sectional study of mammographic density in 487 pre-menopausal women, SES was assessed from questionnaire data using highest achieved level of formal education, quintiles of Census-derived Townsend scores and urban/rural classification of place of residence. Mammographic density was measured on digitised films using a computer-assisted method. Linear regression models were fitted to assess the association between SES variables and mammographic density, adjusting for correlated variables.

Results

In unadjusted models, percent density was positively associated with SES, with an absolute difference in percent density of 6.3% (95% CI 1.6%, 10.5%) between highest and lowest educational categories, and of 6.6% (95% CI -0.7%, 12.9%) between highest and lowest Townsend quintiles. These associations were mainly driven by strong negative associations between these SES variables and lucent area and were attenuated upon adjustment for body mass index (BMI). There was little evidence that reproductive factors explained this association. SES was not associated with the amount of dense tissue in the breast before or after BMI adjustment. The effect of education on percent density persisted after adjustment for Townsend score. Mammographic measures did not vary according to urban/rural place of residence.

Conclusions

The observed SES gradients in percent density paralleled known SES gradients in breast cancer risk. Although consistent with the hypothesis that percent density may be a mediator of the SES differentials in breast cancer risk, the SES gradients in percent density were mainly driven by the negative association between SES and BMI. Nevertheless, as density affects the sensitivity of screen-film mammography, the higher percent density found among high SES women would imply that these women have a higher risk of developing cancer but a lower likelihood of having it detected earlier.

[Breast cancer screening imaging: what do we do]

The purpose of this review is to evaluate the value of different breast imaging technics and their place for individual and mass screening of breast cancer according to the randomized studies on digital mammography and ultrasound screening. Analogic and numerical mammograms are validated for screening of women aged from 50 and 74 years. The additional value of ultrasound is therefore proven when the increased risk is moderate. When risk is higher (genetic or familial), MRI is the method of choice associated with conventional imaging. Individual screening is recommended before 50 for women aged from 45 and 50 and for those over 74 using the same procedures as organized screening.

Tumour Size at Detection According to Different Measures of Mammographic Breast Density

Objectives

Breast cancer prognosis is better for smaller tumours. Women with high breast density are at higher risk of breast cancer and have larger screen-detected and interval cancers in mammographic screening programmes. We assess which continuous measures of breast density are the strongest predictors of breast tumour size at detection and therefore the best measures to identify women who might benefit from more intensive mammographic screening or alternative screening strategies.

Setting and methods

We compared the association between breast density and tumour size for 1007 screen-detected and 341 interval cancers diagnosed in an Australian mammographic screening programme between 1994 and 1996, for three semi-automated continuous measures of breast density: per cent density, dense area and dense area adjusted for non-dense area.

Results

After adjustment for age, hormone therapy use, family history of breast cancer and mode of detection (screen-detected or interval cancers), all measures of breast density shared a similar positive and significant association with tumour size. For example, tumours increased in size with dense area from an estimated mean 2.2 mm larger in the second quintile (β = 2.2; 95% Cl 0.4–3.9, P < 0.001) to mean 6.6 mm larger in the highest decile of dense area (β = 6.6; 95% Cl 4.4–8.9, P < 0.001), when compared with first quintile of breast density.

Conclusions

Of the breast density measures assessed, either dense area or per cent density are suitable measures for identifying women who might benefit from more intensive mammographic screening or alternative screening strategies.

[Retrospective study conducted in northern Finistère about the role of breast MRI in normal breast screening, experience in 51 patients]

OBJECTIVES

To study the role and indications of breast MRI in normal breast screening.

PATIENTS AND METHODS

Retrospective study of 51 patients (mean age of 51 years) conducted in northern Finistère. Each patient had a normal (BI-RADS 1 or 2) breast screening (mammography and echography). Four indications for MRI were chosen: screening of high-risk patients, high-density breasts, radio-clinical discordance, and breasts prostheses. Breast MRI were reviewed according to BI-RADS classification. Abnormalities categorized in BI-RADS 4 or 5 were confirmed histologically.

RESULTS

Thirteen patients underwent histological analysis. Nine invasive carcinomas were identified (six invasive lobular carcinomas (ILC), two mixed carcinomas, one invasive ductal carcinoma). For these patients, the reason for performing MRI was a radio-clinical discordance.

DISCUSSION AND CONCLUSION

The study demonstrates the breast MRI value for radio-clinical discordance and the key role of MRI in diagnostic challenge of ILC. In literature review, MRI has a role even if breast screening is normal: radio-clinical discordance, screening of patients with high-risk, breasts prostheses in certain cases. Breast density comes as an additional criteria to perform this exam.

[Mammographic evaluation of dense breasts: techniques and limits]

Breast density is a radiological concept based on the proportion of radiopaque glandular tissue relative to radiolucent fatty tissue. Mammographic evaluation of dense breasts is more difficult, related to technical difficulties, with decreased rates for detection and characterization of breast lesions, resulting in reduced sensitivity with increased number of interval cancers at routine follow-up when compared to radiolucent breasts. We will review the definition of dense breasts and their frequency, especially their relationship with the age of patients. We will discuss the current technical problems and the impact of breast density on the efficacy of conventional mammography. We will discuss the value of digital mammography, the role of computer assisted diagnosis (CAD) systems and tomosynthesis in the evaluation of dense breasts.

Mammographic density, breast cancer risk and risk prediction

In this review, we examine the evidence for mammographic density as an independent risk factor for breast cancer, describe the risk prediction models that have incorporated density, and discuss the current and future implications of using mammographic density in clinical practice. Mammographic density is a consistent and strong risk factor for breast cancer in several populations and across age at mammogram. Recently, this risk factor has been added to existing breast cancer risk prediction models, increasing the discriminatory accuracy with its inclusion, albeit slightly. With validation, these models may replace the existing Gail model for clinical risk assessment. However, absolute risk estimates resulting from these improved models are still limited in their ability to characterize an individual's probability of developing cancer. Promising new measures of mammographic density, including volumetric density, which can be standardized using full-field digital mammography, will likely result in a stronger risk factor and improve accuracy of risk prediction models.

Influence of mammographic parenchymal pattern in screening-detected and interval invasive breast cancers on pathologic features, mammographic features, and patient survival

OBJECTIVE

The aim of our study was to assess the effect of mammographic parenchymal pattern on patient survival, mammographic features, and pathologic features of breast cancer in a screened population.

MATERIALS AND METHODS

We classified the parenchymal pattern (according to BI-RADS) of 759 screened women who presented with a screening-detected (n = 455) or interval (n = 304) invasive breast cancer. Pathologic details (tumor size, histologic grade, lymph node stage, vascular invasion, and histologic type) and mammographic appearances were recorded. Breast cancer-specific survival was ascertained, with a median follow-up of 9.0 years.

RESULTS

An excess of interval cancers was seen in women with dense breasts (p < 0.0001). Screening-detected (but not interval) tumors were significantly smaller in fatty breasts (p = 0.014). Tumor grade, lymph node stage, vascular invasion, and histologic type did not vary significantly with mammographic parenchymal pattern in screening-detected or interval cancers. Screening-detected cancers in fatty breasts were more likely to appear as indistinct (p = 0.003) or spiculated (p = 0.002) masses in contrast to cancers in dense breasts, which more commonly appeared as architectural distortions (p < 0.0001). No significant breast cancer-specific survival difference was seen by mammographic parenchymal pattern for screening-detected cancers (p = 0.75), interval cancers (p = 0.82), or both groups combined (p = 0.12).

CONCLUSION

The prognosis of screened women presenting with breast cancer is unrelated to dense mammographic parenchymal pattern despite an excess of interval cancers and larger screening-detected tumors in this group. These data support the mammographic screening of women with dense parenchymal patterns.

NHSBSP type 1 interval cancers: a scientifically valid grouping?

AIM

To assess whether there are differences in the pathological features or survival between the new National Health Service Breast Screening Programme (NHSBSP) interval cancer classification system category of type 1 interval cancers, and the previously used, separate categories of occult, unclassified, and true interval cancers.

MATERIALS AND METHODS

The prognostic pathological features (grade, lymph node stage, size, vascular invasion, oestrogen receptor status, and histological type) and survival of 428 type 1 interval invasive breast cancers were analysed by subgroup (occult, unclassified and true interval).

RESULTS

Occult cancers compared with other type 1 interval cancers were of significantly lower grade [38 of 52 (73%) versus 151 of 340 (44%) grade 1 or 2, p=0.0005], more likely to be smaller size [37 of 51 (73%) versus 158 of 341 (46%) <20mm, p=0.0003] and more frequently of lobular type at histology [14 of 42 (32%) versus 50 of 286 (17%), p=0.03]. There was no significant difference in pathological features of unclassified tumours compared with other type 1 tumours. There was no significant survival difference between different type 1 subgroups (p=0.12).

CONCLUSION

The NHSBSP type 1 interval cancers are a heterogeneous grouping with markedly differing pathological features. However, no significant survival difference is seen between the different type 1 subgroups.

Mammographic density and the risk and detection of breast cancer

BACKGROUND

Extensive mammographic density is associated with an increased risk of breast cancer and makes the detection of cancer by mammography difficult, but the influence of density on risk according to method of cancer detection is unknown.

METHODS

We carried out three nested case-control studies in screened populations with 1112 matched case-control pairs. We examined the association of the measured percentage of density in the baseline mammogram with risk of breast cancer, according to method of cancer detection, time since the initiation of screening, and age.

RESULTS

As compared with women with density in less than 10% of the mammogram, women with density in 75% or more had an increased risk of breast cancer (odds ratio, 4.7; 95% confidence interval [CI], 3.0 to 7.4), whether detected by screening (odds ratio, 3.5; 95% CI, 2.0 to 6.2) or less than 12 months after a negative screening examination (odds ratio, 17.8; 95% CI, 4.8 to 65.9). Increased risk of breast cancer, whether detected by screening or other means, persisted for at least 8 years after study entry and was greater in younger than in older women. For women younger than the median age of 56 years, 26% of all breast cancers and 50% of cancers detected less than 12 months after a negative screening test were attributable to density in 50% or more of the mammogram.

CONCLUSIONS

Extensive mammographic density is strongly associated with the risk of breast cancer detected by screening or between screening tests. A substantial fraction of breast cancers can be attributed to this risk factor.

Influence of Patterns of Hormone Replacement Therapy Use and Mammographic Density on Breast Cancer Detection

BACKGROUND

There is evidence that factors such as current hormone replacement therapy (HRT) use and mammographic density may each lower the sensitivity of mammography and are associated with a greater risk of developing an interval cancer. This study explores this relationship further by examining the influence of patterns of HRT use and the percentage of mammographic density on the detection of breast cancer by classification of interval cancer.

METHODS

This study uses a case-case design nested within a cohort of women screened by the Ontario Breast Screening Program between 1994 and 2002. Interval cancers, both those missed at screening but seen on retrospective review (n = 87) or true intervals without visible tumor signs at screening (n = 288) were matched to 450 screen-detected cancers. The association between the percentage of mammographic density, measured by radiologists and a computer-assisted method, and HRT use, ascertained from a mailed questionnaire, and the risk of being diagnosed with an interval cancer was estimated using conditional logistic regression.

RESULTS

A monotonic gradient of increasing risk for interval cancers was found for each 25% increase in mammographic density [odds ratio (OR), 1.77; 95% confidence intervals (95% CI), 1.07-2.95 for missed intervals and OR, 2.16; 95% CI, 1.59-2.94 for true intervals]. After adjusting for mammographic density, a significantly increased risk for true-interval cancers remained for women taking estrogen alone (OR, 1.75; 95% CI, 1.11-2.83) as well as for missed- (OR, 2.84; 95% CI, 1.32-6.13) and true-interval cancers (OR, 1.79; 95% CI, 1.10-2.90) for women taking combined HRT.

CONCLUSIONS

Information on mammographic density and HRT use should routinely be collected at the time of screening. Women at risk should be made aware of the lower sensitivity of mammography and offered alternative procedures for screening.

Hormone replacement therapy, percent mammographic density, and sensitivity of mammography

OBJECTIVE

We examine to what extent the lower mammographic sensitivity found in hormone replacement therapy (HRT) users can be explained by any association of HRT use with higher mammographic density and more difficult to detect cancers.

METHODS

We used logistic regression to estimate the odds of a false-negative screen (a breast cancer diagnosed in the 24 months after a negative screening examination) for HRT users and to estimate, and adjust for, mammographic density (measured on a continuous scale, blinded, using a reliable, computer-assisted method), tumor characteristics (size, grade, and morphology), and potential confounders (age, symptom status, family history, and prior screening) among women ages > or =55 years who attended BreastScreen Victoria for first round screening mammography in 1994 and 1995 (1,086 breast cancers) and for subsequent round screening (471 breast cancers) in 1995 and 1996.

RESULTS

After adjusting for confounders, HRT users were more likely to have a false-negative screen [first round: odds ratio (OR), 1.99; 95% confidence interval (95% CI), 1.4-2.9; subsequent round: OR, 2.29; 95% CI, 1.4-3.8]. This effect was modestly attenuated by adjusting for mammographic density (first round: OR, 1.54; 95% CI, 1.0-2.3; subsequent round: OR, 1.97; 95% CI, 1.2-3.3). Adjusting for tumor characteristics resulted in a modest increase in the odds of a false negative at first round but had no effect at subsequent round.

CONCLUSIONS

Mammographic density only partly explains the effect of HRT on sensitivity. Further research needs to clarify whether hyperemic breast tissue changes affect cancer detectability in HRT users as well as the possibility that the quality of mammography may be poor in some HRT users.

Percentage density, Wolfe's and Tabár's mammographic patterns: agreement and association with risk factors for breast cancer

Introduction

The purpose of this report was to classify mammograms according to four methods and to examine their agreement and their relationship to selected risk factors for breast cancer.

Method

Mammograms and epidemiological data were collected from 987 women, aged 55 to 71 years, attending the Norwegian Breast Cancer Screening Program. Two readers each classified the mammograms according to a quantitative method (Cumulus or Madena software) and one reader according to two qualitative methods (Wolfe and Tabár patterns). Mammograms classified in the reader-specific upper quartile of percentage density, Wolfe's P2 and DY patterns, or Tabár's IV and V patterns, were categorized as high-risk density patterns and the remaining mammograms as low-risk density patterns. We calculated intra-reader and inter-reader agreement and estimated prevalence odds ratios of having high-risk mammographic density patterns according to selected risk factors for breast cancer.

Results

The Pearson correlation coefficient was 0.86 for the two quantitative density measurements. There was moderate agreement between the Wolfe and Tabár classifications (Kappa = 0.51; 95% confidence interval 0.46 to 0.56). Age at screening, number of children and body mass index (BMI) showed a statistically significant inverse relationship with high-risk density patterns for all four methods (all P < 0.05). After adjustment for percentage density, the Wolfe classification was not associated with any of the risk factors for breast cancer, whereas the association with number of children and BMI remained statistically significant for the Tabár classification. Adjustment for Wolfe or Tabár patterns did not alter the associations between these risk factors and percentage mammographic density.

Conclusion

The four assessments methods seem to capture the same overall associations with risk factors for breast cancer. Our results indicate that the quantitative methods convey additional information over the qualitative methods.

The normal breast and its variations in mammography

The mammographic appearance of the breast varies along the lifetime due to physiological modifications or use of hormonal therapies. Density of the glandular tissue is due to amount of cellular elements of the gland and to hydratation of the tissues. Normal variations are encountered as for example breast asymmetry. The currently breast composition should be described with the BI-RADS lexicon classification. Mammary asymmetry is frequent and has to be differentiated from pathologic changes. A good mammographic technique is mandatory for an adequate visualisation of the breast tissues.

Correlates of high-density mammographic parenchymal patterns by menopausal status in a rural population in Northern Greece

Reproductive factors affect breast cancer risk, but less is known of their associations with mammographic density and whether these differ by menopausal status. We report on a cross-sectional study of 1946 pre- and 3047 post-menopausal women who joined a breast screening programme in Northern Greece during 1993-1997. The odds of having a high-density Wolfe pattern (P2/DY) was inversely associated with age (P for linear trend <0.001) in both pre- and post-menopausal women and, for post-menopausal women, with years since menopause (P < 0.001). The odds of a P2/DY pattern declined with higher parity (P < 0.001) and younger age at first pregnancy (P = 0.05) in both pre- and post-menopausal women. They also decreased with the duration of breast-feeding in pre-menopausal women (P = 0.03 in pre- and P = 0.69 in post-menopausal women; test for interaction with menopausal status: P = 0.07). Age at menarche, age at menopause and the number of miscarriages/abortions were not associated with mammographic density. Age at first pregnancy and parity were strong correlates of mammographic density in pre- and post-menopausal women while duration of breast-feeding appeared to be particularly important in pre-menopausal women.

Correlation between mammographic density and volumetric fibroglandular tissue estimated on breast MR images

Previous studies have found that mammographic breast density is highly correlated with breast cancer risk. Therefore, mammographic breast density may be considered as an important risk factor in studies of breast cancer treatments. In this paper, we evaluated the accuracy of using mammograms for estimating breast density by analyzing the correlation between the percent mammographic dense area and the percent glandular tissue volume as estimated from MR images. A dataset of 67 cases having MR images (coronal 3-D SPGR T1-weighted pre-contrast) and corresponding 4-view mammograms was used in this study. Mammographic breast density was estimated by an experienced radiologist and an automated image analysis tool, Mammography Density ESTimator (MDEST) developed previously in our laboratory. For the estimation of the percent volume of fibroglandular tissue in breast MR images, a semiautomatic method was developed to segment the fibroglandular tissue from each slice. The tissue volume was calculated by integration over all slices containing the breast. Interobserver variation was measured for 3 different readers. It was found that the correlation between every two of the three readers for segmentation of MR volumetric fibroglandular tissue was 0.99. The correlations between the percent volumetric fibroglandular tissue on MR images and the percent dense area of the CC and MLO views segmented by an experienced radiologist were both 0.91. The correlation between the percent volumetric fibroglandular tissue on MR images and the percent dense area of the CC and MLO views segmented by MDEST was 0.91 and 0.89, respectively. The root-mean-square (rms) residual ranged from 5.4% to 6.3%. The mean bias ranged from 3% to 6%. The high correlation indicates that changes in mammographic density may be a useful indicator of changes in fibroglandular tissue volume in the breast.

Influence of personal characteristics of individual women on sensitivity and specificity of mammography in the Million Women Study: cohort study

OBJECTIVES

To examine how lifestyle, hormonal, and other factors influence the sensitivity and specificity of mammography.

METHODS

Women recruited into the Million Women Study completed a questionnaire about various personal factors before routine mammographic screening. A sample of 122,355 women aged 50-64 years were followed for outcome of screening and incident breast cancer in the next 12 months. Sensitivity and specificity were calculated by using standard definitions, with adjustment for potential confounding factors.

RESULTS

Breast cancer was diagnosed in 726 (0.6%) women, 629 in screen positive and 97 in screen negative women; 3885 (3.2%) were screen positive but had no subsequent diagnosis of breast cancer. Overall sensitivity was 86.6% and specificity was 96.8%. Three factors had an adverse effect on both measures: use of hormone replacement therapy (sensitivity: 83.0% (95% confidence interval 77.4% to 87.6%), 84.7% (73.9% to 91.6%), and 92.1% (87.6% to 95.0%); specificity: 96.8% (96.6% to 97.0%), 97.8% (97.5% to 98.0%), and 98.1% (98.0% to 98.2%), respectively, for current, past, and never use); previous breast surgery v no previous breast surgery (sensitivity: 83.5% (75.7% to 89.1%) v 89.4% (86.5% to 91.8%); specificity: 96.2% (95.8% to 96.5%) v 97.4% (97.3% to 97.5%), respectively); and body mass index < 25 v > or = 25 (sensitivity: 85.7% (81.2% to 89.3%) v 91.0% (87.5% to 93.6%); specificity: 97.2% (97.0% to 97.3%) v 97.4% (97.3% to 97.6%), respectively). Neither sensitivity nor specificity varied significantly according to age, family history of breast cancer, parity, past oral contraceptive use, tubal ligation, physical activity, smoking, or alcohol consumption.

CONCLUSIONS

The efficiency, and possibly the effectiveness, of mammographic screening is lower in users of hormone replacement therapy, in women with previous breast surgery, and in thin women compared with other women.