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

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

Introduction

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

Methods

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

Results

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

Conclusions

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

Digital mammographic density and breast cancer risk: a case-control study of six alternative density assessment methods

Introduction

Mammographic density is a strong breast cancer risk factor and a major determinant of screening sensitivity. However, there is currently no validated estimation method for full-field digital mammography (FFDM).

Methods

The performance of three area-based approaches (BI-RADS, the semi-automated Cumulus, and the fully-automated ImageJ-based approach) and three fully-automated volumetric methods (Volpara, Quantra and single energy x-ray absorptiometry (SXA)) were assessed in 3168 FFDM images from 414 cases and 685 controls. Linear regression models were used to assess associations between breast cancer risk factors and density among controls, and logistic regression models to assess density-breast cancer risk associations, adjusting for age, body mass index (BMI) and reproductive variables.

Results

Quantra and the ImageJ-based approach failed to produce readings for 4% and 11% of the participants. All six density assessment methods showed that percent density (PD) was inversely associated with age, BMI, being parous and postmenopausal at mammography. PD was positively associated with breast cancer for all methods, but with the increase in risk per standard deviation increment in PD being highest for Volpara (1.83; 95% CI: 1.51 to 2.21) and Cumulus (1.58; 1.33 to 1.88) and lower for the ImageJ-based method (1.45; 1.21 to 1.74), Quantra (1.40; 1.19 to 1.66) and SXA (1.37; 1.16 to 1.63). Women in the top PD quintile (or BI-RADS 4) had 8.26 (4.28 to 15.96), 3.94 (2.26 to 6.86), 3.38 (2.00 to 5.72), 2.99 (1.76 to 5.09), 2.55 (1.46 to 4.43) and 2.96 (0.50 to 17.5) times the risk of those in the bottom one (or BI-RADS 1), respectively, for Volpara, Quantra, Cumulus, SXA, ImageJ-based method, and BI-RADS (P for trend <0.0001 for all). The ImageJ-based method had a slightly higher ability to discriminate between cases and controls (area under the curve (AUC) for PD = 0.68, P = 0.05), and Quantra slightly lower (AUC = 0.63; P = 0.06), than Cumulus (AUC = 0.65).

Conclusions

Fully-automated methods are valid alternatives to the labour-intensive "gold standard" Cumulus for quantifying density in FFDM. The choice of a particular method will depend on the aims and setting but the same approach will be required for longitudinal density assessments.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-014-0439-1) contains supplementary material, which is available to authorized users.

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.

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.

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.

Enhancement of mammographic density measures in breast cancer risk prediction

BACKGROUND

Mammographic density is a strong risk factor for breast cancer.

METHODS

We present a novel approach to enhance area density measures that takes advantage of the relative density of the pectoral muscle that appears in lateral mammographic views. We hypothesized that the grey scale of film mammograms is normalized to volume breast density but not pectoral density and thus pectoral density becomes an independent marker of volumetric density.

RESULTS

From analysis of data from a Swedish case-control study (1,286 breast cancer cases and 1,391 control subjects, ages 50-75 years), we found that the mean intensity of the pectoral muscle (MIP) was highly associated with breast cancer risk [per SD: OR = 0.82; 95% confidence interval (CI), 0.75-0.88; P = 6 × 10(-7)] after adjusting for a validated computer-assisted measure of percent density (PD), Cumulus. The area under curve (AUC) changed from 0.600 to 0.618 due to using PD with the pectoral muscle as reference instead of a standard area-based PD measure. We showed that MIP is associated with a genetic variant known to be associated with mammographic density and breast cancer risk, rs10995190, in a subset of women with genetic data. We further replicated the association between MIP and rs10995190 in an additional cohort of 2,655 breast cancer cases (combined P = 0.0002).

CONCLUSIONS

MIP is a marker of volumetric density that can be used to complement area PD in mammographic density studies and breast cancer risk assessment.

IMPACT

Inclusion of MIP in risk models should be considered for studies using area PD from analog films.

Comparison of fully and semi-automated area-based methods for measuring mammographic density and predicting breast cancer risk

BACKGROUND

Mammographic density is a strong risk factor for breast cancer but the lack of valid fully automated methods for quantifying it has precluded its use in clinical and screening settings. We compared the performance of a recently developed automated approach, based on the public domain ImageJ programme, to the well-established semi-automated Cumulus method.

METHODS

We undertook a case-control study within the intervention arm of the Age Trial, in which ∼54,000 British women were offered annual mammography at ages 40-49 years. A total of 299 breast cancer cases diagnosed during follow-up and 422 matched (on screening centre, date of birth and dates of screenings) controls were included. Medio-lateral oblique (MLO) images taken closest to age 41 and at least one year before the index case's diagnosis were digitised for each participant. Cumulus readings were performed in the left MLO and ImageJ-based readings in both left and right MLOs. Conditional logistic regression was used to examine density-breast cancer associations.

RESULTS

The association between density readings taken from one single MLO and breast cancer risk was weaker for the ImageJ-based method than for Cumulus (age-body mass index-adjusted odds ratio (OR) per one s.d. increase in percent density (95% CI): 1.52 (1.24-1.86) and 1.61 (1.33-1.94), respectively). The ImageJ-based density-cancer association strengthened when the mean of left-right MLO readings was used: OR=1.61 (1.31-1.98).

CONCLUSIONS

The mean of left-right MLO readings yielded by the ImageJ-based method was as strong a predictor of risk as Cumulus readings from a single MLO image. The ImageJ-based method, using the mean of two measurements, is a valid automated alternative to Cumulus for measuring density in analogue films.

Methods for assessing and representing mammographic density: an analysis of 4 case-control studies

To maximize statistical power in studies of mammographic density and breast cancer, it is advantageous to combine data from several studies, but standardization of the density assessment is desirable. Using data from 4 case-control studies, we describe the process of reassessment and the resulting correlation between values, identify predictors of differences in density readings, and evaluate the strength of the association between mammographic density and breast cancer risk using different representations of density values. The pooled analysis included 1,699 cases and 2,422 controls from California (1990-1998), Hawaii (1996-2003), Minnesota (1992-2001), and Japan (1999-2003). In 2010, a single reader reassessed all images for mammographic density using Cumulus software (Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada). The mean difference between original and reassessed percent density values was -0.7% (95% confidence interval: -1.1, -0.3), with a correlation of 0.82 that varied by location (r = 0.80-0.89). Case status, weight status, age, parity, density assessment method, mammogram view, and race/ethnicity were significant determinants of the difference between original and reassessed values; in combination, these factors explained 9.2% of the variation. The associations of mammographic density with breast cancer and the model fits were similar using the original values and the reassessed values but were slightly strengthened when a calibrated value based on 100 reassessed radiographs was used.

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.

Agreement of mammographic measures of volumetric breast density to MRI

Background

Clinical scores of mammographic breast density are highly subjective. Automated technologies for mammography exist to quantify breast density objectively, but the technique that most accurately measures the quantity of breast fibroglandular tissue is not known.

Purpose

To compare the agreement of three automated mammographic techniques for measuring volumetric breast density with a quantitative volumetric MRI-based technique in a screening population.

Materials and Methods

Women were selected from the UCSF Medical Center screening population that had received both a screening MRI and digital mammogram within one year of each other, had Breast Imaging Reporting and Data System (BI-RADS) assessments of normal or benign finding, and no history of breast cancer or surgery. Agreement was assessed of three mammographic techniques (Single-energy X-ray Absorptiometry [SXA], Quantra, and Volpara) with MRI for percent fibroglandular tissue volume, absolute fibroglandular tissue volume, and total breast volume.

Results

Among 99 women, the automated mammographic density techniques were correlated with MRI measures with R² values ranging from 0.40 (log fibroglandular volume) to 0.91 (total breast volume). Substantial agreement measured by kappa statistic was found between all percent fibroglandular tissue measures (0.72 to 0.63), but only moderate agreement for log fibroglandular volumes. The kappa statistics for all percent density measures were highest in the comparisons of the SXA and MRI results. The largest error source between MRI and the mammography techniques was found to be differences in measures of total breast volume.

Conclusion

Automated volumetric fibroglandular tissue measures from screening digital mammograms were in substantial agreement with MRI and if associated with breast cancer could be used in clinical practice to enhance risk assessment and prevention.

Breast density measurements with ultrasound tomography: a comparison with film and digital mammography

PURPOSE

To investigate the use of the whole-breast sound speed measurement as a marker of breast density (BD), a known risk factor for breast cancer.

METHODS

As part of an ongoing study of breast cancer detection, 249 patients were scanned with a clinical prototype that operates on the principles of ultrasound tomography. Typically, 40-100 sound speed tomograms were reconstructed from the scan data, corresponding to the entire volume of the breast of each patient. The data were used to estimate the volume averaged sound speed (VASS) of the breast for each patient. The corresponding mammograms were used to calculate mammographic percent density (MPD) using CUMULUS software. Film mammograms were available for 164 patients while 85 digital mammograms were available for the remaining patients. Standard statistical techniques were used to determine associations of breast sound speed with a variety of mammographic measures such as percent density, area of dense tissue, and area of nondense tissue. Furthermore, associations of breast sound speed with continuous variables such as age and weight and dichotomous variables such as parity and menopausal status were also assessed.

RESULTS

VASS was found to be significantly associated with MPD. The Spearman correlation coefficient (r(s)) between VASS and MPD was found to be 0.77 and 0.71 for film and digital mammography, respectively. VASS was positively correlated with dense areas by mammography, both digital (r(s) = 0.46) and film (r(s) = 0.56). VASS was negatively associated with nondense area by mammography, both digital (r(s) = -0.58) and film (r(s) = -0.63). BD by all methods was less in postmenopausal than in premenopausal women. The MPD was lower in the postmenopausal group (by 6.6%, p < 0.08, for the digital group and 7.73%, p < 0.007, for the film group). The VASS was also lower in the postmenopausal group (by 15 m∕s, p < 0.001 for the digital group and 8 m∕s, p < 0.08, for the film group). The association of MPD with age was characterized with r(s) = -0.06 (p < 0.6) for digital mammography and r(s) = -0.53 (p < 0.002) for film mammography. For weight, the MPD associations were characterized by r(s) = -0.53 (p < 0.0001) for digital mammography and -0.38 (p < 0.0001) for film mammography. The association of VASS with age was r(s) = -0.33 (p < 0.002) for the digital group and -0.17 (p < 0.03) for the film group. For weight, the relationship was characterized with r(s) = -0.45 (p < 0.001) for the digital group and -0.37 (p < 0.0001) for the film group.

CONCLUSIONS

The association between VASS and MPD is strong for both film and digital mammography, suggesting that VASS is a viable measure of breast density. This result sets the stage for future work that will focus on directly testing the association of VASS with breast cancer risk.

Mammographic breast density and subsequent risk of breast cancer in postmenopausal women according to the time since the mammogram

BACKGROUND

Few studies have shown that the association between mammographic breast density and breast cancer persists for up to 10 years after the mammogram. We investigated associations of percent density, absolute dense, and nondense areas with breast cancer risk according to the time since the mammogram.

METHODS

This study included 1,028 incident breast cancer cases diagnosed within the Nurses' Health Study and 1,780 matched controls. Breast density was measured from digitized film images with computerized techniques. Information on breast cancer risk factors was obtained prospectively from the biennial questionnaires before the date of cancer diagnosis for cases and their matched controls. The data were analyzed with logistic regression.

RESULTS

Breast cancer risk increased with increasing percent density and increasing absolute dense area and decreased with increasing nondense area. In multivariate analysis, the magnitude of the association between percent density and breast cancer was similar when the time since the mammogram was <2, 2 to <5, and 5 to <10 years [density ≥50% vs.<10%: ORs, 3.12; 95% confidence interval (CI): 1.55-6.25, 5.35 (95% CI: 2.93-9.76), and 3.91 (95%CI: 2.22-6.88), respectively]. Similarly, the magnitude of association between quartiles of dense and nondense areas and breast cancer risk were similar across the time strata. We found no interactions between the time since the mammogram and breast density measures (Pinteraction > 0.05).

CONCLUSIONS

Patterns of the associations between percent density, absolute dense, and nondense area with breast cancer risk persist for up to 10 years after the mammogram.

IMPACT

A one-time density measure can be used for long-term breast cancer risk prediction.

Reported mammographic density: film-screen versus digital acquisition

PURPOSE

To test the hypothesis that American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) categories for breast density reported by radiologists are lower when digital mammography is used than those reported when film-screen (FS) mammography is used.

MATERIALS AND METHODS

This study was institutional review board approved and HIPAA compliant. Demographic data, risk factors, and BI-RADS breast density categories were collected from five mammography registries that were part of the Breast Cancer Surveillance Consortium. Active, passive, or waiver of consent was obtained for all participants. Women aged 40 years and older who underwent at least two screening mammographic examinations less than 36 months apart between January 1, 2000, and December 31, 2009, were included. Women with prior breast cancer, augmentation, or use of agents known to affect density were excluded. The main sample included 89 639 women with both FS and digital mammograms. The comparison group included 259 046 women with two FS mammograms and 87 066 women with two digital mammograms. BI-RADS density was cross-tabulated according to the order in which the two types of mammogram were acquired and by the first versus second interpretation.

RESULTS

Regardless of acquisition method, the percentage of women with a change in density from one reading to the next was similar. Breast density was lower in 19.8% of the women who underwent FS before digital mammography and 17.1% of those who underwent digital before FS mammography. Similarly, lower density classifications were reported on the basis of the second mammographic examination regardless of acquisition method (15.8%-19.8%). The percentage of agreement between density readings was similar regardless of mammographic types paired (67.3%-71.0%).

CONCLUSION

The study results showed no difference in reported BI-RADS breast density categories according to acquisition method. Reported BI-RADS density categories may be useful in the development of breast cancer risk models in which FS, digital, or both acquisition methods are used.

Density is in the eye of the beholder: visual versus semi-automated assessment of breast density on standard mammograms

OBJECTIVES

Visual inspection is generally used to assess breast density. Our study aim was to compare visual assessment of breast density of experienced and inexperienced readers with semi-automated analysis of breast density.

METHODS

Breast density was assessed by an experienced and an inexperienced reader in 200 mammograms and scored according to the quantitative BI-RADS classification. Breast density was also assessed by dedicated software using a semi-automated thresholding technique. Agreement between breast density classification of both readers as well as agreement between their assessment versus the semi-automated analysis as reference standard was expressed as the weighted kappa value.

RESULTS

Using the semi-automated analysis, agreement between breast density measurements of both breasts in both projections was excellent (ICC >0.9, P < 0.0001). Reproducibility of the semi-automated analysis was excellent (ICC >0.8, P < 0.0001). The experienced reader correctly classified the BI-RADS breast density classification in 58.5% of the cases. Classification was overestimated in 35.5% of the cases and underestimated in 6.0% of the cases. Results of the inexperienced reader were less accurate. Agreement between the classification of both readers versus the semi-automated analysis was considered only moderate with weighted kappa values of 0.367 (experienced reader) and 0.232 (inexperienced reader).

CONCLUSION

Visual assessment of breast density on mammograms is inaccurate and observer-dependent.

Mammographic density and breast cancer risk: current understanding and future prospects

Variations in percent mammographic density (PMD) reflect variations in the amounts of collagen and number of epithelial and non-epithelial cells in the breast. Extensive PMD is associated with a markedly increased risk of invasive breast cancer. The PMD phenotype is important in the context of breast cancer prevention because extensive PMD is common in the population, is strongly associated with risk of the disease, and, unlike most breast cancer risk factors, can be changed. Work now in progress makes it likely that measurement of PMD will be improved in the near future and that understanding of the genetics and biological basis of the association of PMD with breast cancer risk will also improve. Future prospects for the application of PMD include mammographic screening, risk prediction in individuals, breast cancer prevention research, and clinical decision making.

Nondense mammographic area and risk of breast cancer

Introduction

The mechanisms underlying the strong association between percentage dense area on a mammogram and the risk of breast cancer are unknown. We investigated separately the absolute dense area and the absolute nondense area on mammograms in relation to breast cancer risk.

Methods

We conducted a nested case-control study on prediagnostic mammographic density measurements and risk of breast cancer in the Nurses' Health Study and the Nurses' Health Study II. Premenopausal mammograms were available from 464 cases and 998 controls, and postmenopausal mammograms were available from 960 cases and 1,662 controls. We used a computer-assisted thresholding technique to measure mammographic density, and we used unconditional logistic regression to calculate OR and 95% CI data.

Results

Higher absolute dense area was associated with a greater risk of breast cancer among premenopausal women (OR_{tertile 3 vs 1} = 2.01, 95% CI = 1.45 to 2.77) and among postmenopausal women (OR_{quintile 5 vs 1} = 2.19, 95% CI = 1.65 to 2.89). However, increasing absolute nondense area was associated with a decreased risk of breast cancer among premenopausal women (OR_{tertile 3 vs 1} = 0.51, 95% CI = 0.36 to 0.72) and among postmenopausal women (OR_{quintile 5 vs 1} = 0.46, 95% CI = 0.34 to 0.62). These associations changed minimally when we included both absolute dense area and absolute nondense area in the same statistical model. As expected, the percentage dense area was the strongest risk factor for breast cancer in both groups.

Conclusions

Our results indicate that absolute dense area is independently and positively associated with breast cancer risk, whereas absolute nondense area is independently and inversely associated with breast cancer risk. Since adipose tissue is radiographically nondense, these results suggest that adipose breast tissue may have a protective role in breast carcinogenesis.

Quantitative assessment of mammary gland density in rodents using digital image analysis

Background

Rodent models have been used extensively to study mammary gland development and for studies of toxicology and carcinogenesis. Mammary gland gross morphology can visualized via the excision of intact mammary gland chains following fixation and staining with carmine using a tissue preparation referred to as a whole mount. Methods are described for the automated collection of digital images from an entire mammary gland whole mount and for the interrogation of digital data using a "masking" technique available with Image-Pro^® plus image analysis software (Mediacybernetics. Silver Spring, MD).

Results

Parallel to mammographic analysis in humans, measurements of rodent mammary gland density were derived from area-based or volume-based algorithms and included: total circumscribed mammary fat pad mass, mammary epithelial mass, and epithelium-free fat pad mass. These values permitted estimation of absolute mass of mammary epithelium as well as breast density. The biological plausibility of these measurements was evaluated in mammary whole mounts from rats and mice. During mammary gland development, absolute epithelial mass increased linearly without significant changes in mammographic density. Treatment of rodents with tamoxifen, 9-cis-retinoic acid, or ovariectomy, and occurrence of diet induced obesity decreased both absolute epithelial mass and mammographic density. The area and volumetric methods gave similar results.

Conclusions

Digital image analysis can be used for screening agents for potential impact on reproductive toxicity or carcinogenesis as well as for mechanistic studies, particularly for cumulative effects on mammary epithelial mass as well as translational studies of mechanisms that explain the relationship between epithelial mass and cancer risk.

Volume of mammographic density and risk of breast cancer

BACKGROUND

Assessing the volume of mammographic density might more accurately reflect the amount of breast volume at risk of malignant transformation and provide a stronger indication of risk of breast cancer than methods based on qualitative scores or dense breast area.

METHODS

We prospectively collected mammograms for women undergoing screening mammography. We determined the diagnosis of subsequent invasive or ductal carcinoma in situ for 275 cases, selected 825 controls matched for age, ethnicity, and mammography system, and assessed three measures of breast density: percent dense area, fibroglandular volume, and percent fibroglandular volume.

RESULTS

After adjustment for familial breast cancer history, body mass index, history of breast biopsy, and age at first live birth, the ORs for breast cancer risk in the highest versus lowest measurement quintiles were 2.5 (95% CI: 1.5-4.3) for percent dense area, 2.9 (95% CI: 1.7-4.9) for fibroglandular volume, and 4.1 (95% CI: 2.3-7.2) for percent fibroglandular volume. Net reclassification indexes for density measures plus risk factors versus risk factors alone were 9.6% (P = 0.07) for percent dense area, 21.1% (P = 0.0001) for fibroglandular volume, and 14.8% (P = 0.004) for percent fibroglandular volume. Fibroglandular volume improved the categorical risk classification of 1 in 5 women for both women with and without breast cancer.

CONCLUSION

Volumetric measures of breast density are more accurate predictors of breast cancer risk than risk factors alone and than percent dense area.

IMPACT

Risk models including dense fibroglandular volume may more accurately predict breast cancer risk than current risk models.