Mammographic tissue, breast cancer risk, serial image analysis, and digital mammography. Part 1. Tissue and related risk factors

Empirically-derived synthetic populations to mitigate small sample sizes

Limited sample sizes can lead to spurious modeling findings in biomedical research. The objective of this work is to present a new method to generate synthetic populations (SPs) from limited samples using matched case-control data (n = 180 pairs), considered as two separate limited samples. SPs were generated with multivariate kernel density estimations (KDEs) with unconstrained bandwidth matrices. We included four continuous variables and one categorical variable for each individual. Bandwidth matrices were determined with Differential Evolution (DE) optimization by covariance comparisons. Four synthetic samples (n = 180) were derived from their respective SPs. Similarity between observed samples with synthetic samples was compared assuming their empirical probability density functions (EPDFs) were similar. EPDFs were compared with the maximum mean discrepancy (MMD) test statistic based on the Kernel Two-Sample Test. To evaluate similarity within a modeling context, EPDFs derived from the Principal Component Analysis (PCA) scores and residuals were summarized with the distance to the model in X-space (DModX) as additional comparisons. Four SPs were generated from each sample. The probability of selecting a replicate when randomly constructing synthetic samples (n = 180) was infinitesimally small. MMD tests indicated that the observed sample EPDFs were similar to the respective synthetic EPDFs. For the samples, PCA scores and residuals did not deviate significantly when compared with their respective synthetic samples. The feasibility of this approach was demonstrated by producing synthetic data at the individual level, statistically similar to the observed samples. The methodology coupled KDE with DE optimization and deployed novel similarity metrics derived from PCA. This approach could be used to generate larger-sized synthetic samples. To develop this approach into a research tool for data exploration purposes, additional evaluation with increased dimensionality is required. Moreover, given a fully specified population, the degree to which individuals can be discarded while synthesizing the respective population accurately will be investigated. When these objectives are addressed, comparisons with other techniques such as bootstrapping will be required for a complete evaluation.

Microcalcification and BMP-2 in breast cancer: correlation with clinicopathological features and outcomes

Background

Microcalcification is a very important diagnostic information in breast cancer. The purpose of this study was to determine the relationship of clinicopathological features and prognosis of breast cancer with microcalcification and to detect biomarkers related to the possible mechanisms of microcalcifications.

Patients and methods

All 529 subjects with microcalcifications were selected from patients who had been examined using breast mammography. The control group did not have detectable microcalcifications, and was matched in a ratio of 1:3. The clinicopathological factors, progression-free survival (PFS), and overall survival were evaluated by SPSS.

Results

There was a significant difference in tumor size between the two groups, with larger tumors in the calcification group than the control group, and the proportion of patients in the calcification group with tumors of >5 cm was 20.4% vs 17.2% in the control group (P=0.041). The proportion of patients with lymph node metastasis in the calcification group was higher than that of the control group (35% vs 27.9%, P=0.027). The recurrence rate in ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) patients with microcalcification was higher than that in the control group (P=0.035 and 0.044). BMP-2 expression was higher in breast cancer tissues, especially in breast cancer tissues with microcalcifications. The recurrence rate in the BMP-2(+) group was higher than that in the BMP-2(-) group both in DCIS and IDC (P=0.044 and 0.049). Microcalcifications and the positive expression of BMP-2 were independent factors affecting the PFS of the breast cancer patients.

Conclusion

Through the analysis of this study, it was found that the prognosis of the patients with microcalcification was relatively poor. BMP-2 was highly expressed in the breast cancer with microcalcification and was associated with poor prognosis.

Mammographic breast density: How it affects performance indicators in screening programmes?

OBJECTIVES

To investigate how breast density affects screening performance indicators in a digital mammography context.

METHODS

We assessed the effect of breast density over the screen-detected and interval cancers rates, false-positives, specificity, sensitivity, recall rate, positive predictive value of recall (PPV-1), and PPV of invasive tests (PPV-2). Radiologists classified breast density using the BIRADS System. We used generalized estimating equations to account for within-woman correlation by means of the robust Huber-White variance estimator.

RESULTS

We included 177,164 women aged 50-69 years who underwent 499,251 digital mammograms from 2004 to 2015 in Spain. According to the fibroglandular tissue percentage, 24.7% of mammograms were classified as BI-RADS 1 (<25% glandular), 54.7% as BI-RADS 2 (25-50% glandular), 14.0% as BI-RADS 3 (51-75% glandular) and 6.6% as BI-RADS 4 (>75% glandular). Overall, women with BI-RADS 3 had the highest screen-detected cancer rate (5.9 per 1000) and BI-RADS 4 the highest interval cancer rate (2.4 per 1000). Sensitivity decreased from 89.2% in women with BI-RADS 1 to 67.9% in BI-RADS 4. Both PPV-1 and PPV-2 decreased from 10.4% to 5.7% and from 49.8% to 32.4% in women with BI-RADS 1 and BI-RADS 4, respectively. Women aged 60-69 years with BI-RADS 4 had the lowest sensitivity (54.9%) and the highest interval cancer rate (3.8 per 1000).

CONCLUSIONS

Performance screening measures are negatively affected by breast density falling to a lower sensitivity and PPV, and higher interval cancer rate as breast density increases. Particularly women aged 60-69 years with >75% glandular breasts had the worst results and therefore may be candidates for screening using other technologies.

Predicting the response to neoadjuvant therapy for early-stage breast cancer: tumor-, blood-, and imaging-related biomarkers

Neoadjuvant therapy (NAT) has been used increasingly in patients with locally advanced or early-stage breast cancer. However, the accurate evaluation and prediction of response to NAT remain the great challenge. Biomarkers could prove useful to identify responders or nonresponders, or even to distinguish between early and delayed responses. These biomarkers could include markers from the tumor itself, such as versatile proteins, genes, and ribonucleic acids, various biological factors or peripheral blood cells, and clinical and pathological features. Possible predictive markers could also include multiple features from functional imaging, such as standard uptake values in positron emission tomography, apparent diffusion coefficient in magnetic resonance, or radiomics imaging biomarkers. In addition, cells that indirectly present the immune status of tumor cells and/or their host could also potentially be used as biomarkers, eg, tumor-infiltrating lymphocytes, tumor-associated macrophages, and myeloid-derived suppressor cells. Though numerous biomarkers have been widely investigated, only estrogen and/or progesterone receptors and human epidermal growth factor receptor have been proven to be reliable biomarkers to predict the response to NAT. They are the only biomarkers recommended in several international guidelines. The other aforementioned biomarkers warrant further validation studies. Some multigene profiling assays that are commercially available, eg, Oncotype DX and MammaPrint, should be used with caution when extrapolated to NAT settings. A panel of combined multilevel biomarkers might be able to predict the response to NAT more robustly than individual biomarkers. To establish such a panel and its prediction model, reliable methods and extensive clinical validation are warranted.

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.

Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment

BACKGROUND

The assessment of a woman's risk for developing breast cancer has become increasingly important for establishing personalized screening recommendations and forming preventive strategies. Studies have consistently shown a strong relationship between breast cancer risk and mammographic parenchymal patterns, typically assessed by percent mammographic density. This paper will review the advancing role of mammographic texture analysis as a potential novel approach to characterize the breast parenchymal tissue to augment conventional density assessment in breast cancer risk estimation.

MAIN TEXT

The analysis of mammographic texture provides refined, localized descriptors of parenchymal tissue complexity. Currently, there is growing evidence in support of textural features having the potential to augment the typically dichotomized descriptors (dense or not dense) of area or volumetric measures of breast density in breast cancer risk assessment. Therefore, a substantial research effort has been devoted to automate mammographic texture analysis, with the aim of ultimately incorporating such quantitative measures into breast cancer risk assessment models. In this paper, we review current and emerging approaches in this field, summarizing key methodological details and related studies using novel computerized approaches. We also discuss research challenges for advancing the role of parenchymal texture analysis in breast cancer risk stratification and accelerating its clinical translation.

CONCLUSIONS

The objective is to provide a comprehensive reference for researchers in the field of parenchymal pattern analysis in breast cancer risk assessment, while indicating key directions for future research.

Parenchymal texture analysis in digital mammography: A fully automated pipeline for breast cancer risk assessment

PURPOSE

Mammographic percent density (PD%) is known to be a strong risk factor for breast cancer. Recent studies also suggest that parenchymal texture features, which are more granular descriptors of the parenchymal pattern, can provide additional information about breast cancer risk. To date, most studies have measured mammographic texture within selected regions of interest (ROIs) in the breast, which cannot adequately capture the complexity of the parenchymal pattern throughout the whole breast. To better characterize patterns of the parenchymal tissue, the authors have developed a fully automated software pipeline based on a novel lattice-based strategy to extract a range of parenchymal texture features from the entire breast region.

METHODS

Digital mammograms from 106 cases with 318 age-matched controls were retrospectively analyzed. The lattice-based approach is based on a regular grid virtually overlaid on each mammographic image. Texture features are computed from the intersection (i.e., lattice) points of the grid lines within the breast, using a local window centered at each lattice point. Using this strategy, a range of statistical (gray-level histogram, co-occurrence, and run-length) and structural (edge-enhancing, local binary pattern, and fractal dimension) features are extracted. To cover the entire breast, the size of the local window for feature extraction is set equal to the lattice grid spacing and optimized experimentally by evaluating different windows sizes. The association between their lattice-based texture features and breast cancer was evaluated using logistic regression with leave-one-out cross validation and further compared to that of breast PD% and commonly used single-ROI texture features extracted from the retroareolar or the central breast region. Classification performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC). DeLong's test was used to compare the different ROCs in terms of AUC performance.

RESULTS

The average univariate performance of the lattice-based features is higher when extracted from smaller than larger window sizes. While not every individual texture feature is superior to breast PD% (AUC: 0.59, STD: 0.03), their combination in multivariate analysis has significantly better performance (AUC: 0.85, STD: 0.02, p < 0.001). The lattice-based texture features also outperform the single-ROI texture features when extracted from the retroareolar or the central breast region (AUC: 0.60-0.74, STD: 0.03). Adding breast PD% does not make a significant performance improvement to the lattice-based texture features or the single-ROI features (p > 0.05).

CONCLUSIONS

The proposed lattice-based strategy for mammographic texture analysis enables to characterize the parenchymal pattern over the entire breast. As such, these features provide richer information compared to currently used descriptors and may ultimately improve breast cancer risk assessment. Larger studies are warranted to validate these findings and also compare to standard demographic and reproductive risk factors.

The Effect of Atorvastatin on Breast Cancer Biomarkers in High-Risk Women

Statins have the potential to reduce breast cancer incidence and recurrence as shown in both epidemiologic and laboratory studies. The purpose of this study was to evaluate the effect of a lipophilic statin, atorvastatin, on breast cancer biomarkers of risk [mammographic density (MD) and insulin growth factor 1 (IGF-1)] in high-risk premenopausal women.Premenopausal women at increased risk for breast cancer received either 40 mg of atorvastatin or placebo for 1 year. Biomarker assessment was performed prior to initiation and at completion of study medication. MD was determined using both Breast Imaging Reporting and Data System and the visual analogue scale. Serum IGF-1 was determined by ELISA assay at the end of the study.Sixty-three women were enrolled between December 2005 and May 2010. Sixteen (25%) women withdrew. The mean age of participants was 43 (range, 35-50), 100% were white, and the average body mass index (BMI) was 26.4. The statin group demonstrated a significant decrease in cholesterol and low-density lipoprotein (LDL), suggesting compliance with study medication. After accounting for BMI, there was no difference in change in MD between groups. There was a significant increase in serum IGF-1 in the statin group.In this multi-institutional randomized prospective clinical trial of premenopausal women at increased risk for breast cancer, we did not see an effect of atorvastatin on MD. Further investigation of statins may be warranted; however, design of prior trials and potential mechanism of action of the agent need to be considered in the design of future trials. Cancer Prev Res; 9(5); 379-84. ©2016 AACR.

Parenchymal texture analysis in digital mammography: robust texture feature identification and equivalence across devices

An analytical framework is presented for evaluating the equivalence of parenchymal texture features across different full-field digital mammography (FFDM) systems using a physical breast phantom. Phantom images (FOR PROCESSING) are acquired from three FFDM systems using their automated exposure control setting. A panel of texture features, including gray-level histogram, co-occurrence, run length, and structural descriptors, are extracted. To identify features that are robust across imaging systems, a series of equivalence tests are performed on the feature distributions, in which the extent of their intersystem variation is compared to their intrasystem variation via the Hodges-Lehmann test statistic. Overall, histogram and structural features tend to be most robust across all systems, and certain features, such as edge enhancement, tend to be more robust to intergenerational differences between detectors of a single vendor than to intervendor differences. Texture features extracted from larger regions of interest (i.e., [Formula: see text]) and with a larger offset length (i.e., [Formula: see text]), when applicable, also appear to be more robust across imaging systems. This framework and observations from our experiments may benefit applications utilizing mammographic texture analysis on images acquired in multivendor settings, such as in multicenter studies of computer-aided detection and breast cancer risk assessment.

Digital breast tomosynthesis versus full-field digital mammography: comparison of the accuracy of lesion measurement and characterization using specimens

BACKGROUND

Digital breast tomosynthesis (DBT) is one of the new techniques being developed to overcome the inherent limitations of mammography caused by superimposed structures in a 2D projection.

PURPOSE

To compare the diagnostic performances of digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) for lesion characterization and size measurement using breast specimens.

MATERIAL AND METHODS

Out of 156 women scheduled for surgery, we included in our study 114 women, each of whom had a single, breast lesion. Three breast radiologists independently evaluated the DBT and FFDM performance regarding the breast specimens obtained during surgery. Each reader measured the lesion size, and then categorized the probability of malignancy using the American College of Radiology Breast Imaging Reporting and Data system (BI-RADS). After both reading sessions, the readers selected the preferred modality of either FFDM or DBT in lesion characterization. We also analyzed the radiologists' evaluation performance in patients with dense versus fatty breasts when using DBT and FFDM.

RESULTS

The imaging findings of 84 cancers and 30 benign lesions, all of which had been pathologically proven, were reviewed. The size evaluation determined by DBT was more accurately correlated with that found by pathology (P = 0.001 for fatty breasts and <0.001 for dense breasts) than that determined by FFDM. The correlation coefficients of DBT and FFDM to the pathologically determined lesion size were 0.90 and 0.89, respectively (P < 0.001). Compared with the pathologically determined lesion size, the size determined by both imaging modalities was overestimated. Overall, assessment of the probability of malignancy by DBT and FFDM did not differ significantly (P = 0.07); however, in dense breast, DBT was more strongly correlated with the pathology determination than FFDM (P = 0.03).

CONCLUSION

DBT may be superior to FFDM for determining the preoperative size measurement of breast lesions irregardless of their parenchymal density. Particularly in dense breasts, DBT was more useful for differentiating the lesion malignancy rate.

Comparison of Danish dichotomous and BI-RADS classifications of mammographic density

Background

In the Copenhagen mammography screening program from 1991 to 2001, mammographic density was classified either as fatty or mixed/dense. This dichotomous mammographic density classification system is unique internationally, and has not been validated before.

Purpose

To compare the Danish dichotomous mammographic density classification system from 1991 to 2001 with the density BI-RADS classifications, in an attempt to validate the Danish classification system.

Material and Methods

The study sample consisted of 120 mammograms taken in Copenhagen in 1991–2001, which tested false positive, and which were in 2012 re-assessed and classified according to the BI-RADS classification system. We calculated inter-rater agreement between the Danish dichotomous mammographic classification as fatty or mixed/dense and the four-level BI-RADS classification by the linear weighted Kappa statistic.

Results

Of the 120 women, 32 (26.7%) were classified as having fatty and 88 (73.3%) as mixed/dense mammographic density, according to Danish dichotomous classification. According to BI-RADS density classification, 12 (10.0%) women were classified as having predominantly fatty (BI-RADS code 1), 46 (38.3%) as having scattered fibroglandular (BI-RADS code 2), 57 (47.5%) as having heterogeneously dense (BI-RADS 3), and five (4.2%) as having extremely dense (BI-RADS code 4) mammographic density. The inter-rater variability assessed by weighted kappa statistic showed a substantial agreement (0.75).

Conclusion

The dichotomous mammographic density classification system utilized in early years of Copenhagen’s mammographic screening program (1991–2001) agreed well with the BI-RADS density classification system.

Does mammographic density reflect the expression of breast cancer markers?

Mammographic density reflects variation in breast tissue composition as detected on mammogram. It is associated with a number of well-known breast cancer risk factors and itself is considered one of the strongest risk factors for breast cancer. If the expression of several proteins and genes within the breast tissue influences mammographic density in the same way as it influences breast cancer risk, then mammographic density might serve as an intermediate biomarker in future epidemiological studies on breast cancer. This has the potential to provide a quick means for predicting the effect of changes in the breast microenvironment on breast cancer risk without having to wait for an eventual development of breast cancer. In this review, the expression of several proteins and genes (growth factors, enzymes, proteoglycans and pro-inflammatory markers) within the breast tissue is shown to be associated with mammographic density. These proteins and genes are suspected to play a role in breast carcinogenesis. More studies assessing differential expression of proteins and genes in mammary epithelium and stroma and their association with mammographic density among premenopausal and postmenopausal women are required. Identification of proteins and genes influencing mammographic density may provide further insight on the molecular causes of breast cancer.

Quantification of breast density with spectral mammography based on a scanned multi-slit photon-counting detector: a feasibility study

A simple and accurate measurement of breast density is crucial for the understanding of its impact in breast cancer risk models. The feasibility to quantify volumetric breast density with a photon-counting spectral mammography system has been investigated using both computer simulations and physical phantom studies. A computer simulation model involved polyenergetic spectra from a tungsten anode x-ray tube and a Si-based photon-counting detector has been evaluated for breast density quantification. The figure-of-merit (FOM), which was defined as the signal-to-noise ratio of the dual energy image with respect to the square root of mean glandular dose, was chosen to optimize the imaging protocols, in terms of tube voltage and splitting energy. A scanning multi-slit photon-counting spectral mammography system has been employed in the experimental study to quantitatively measure breast density using dual energy decomposition with glandular and adipose equivalent phantoms of uniform thickness. Four different phantom studies were designed to evaluate the accuracy of the technique, each of which addressed one specific variable in the phantom configurations, including thickness, density, area and shape. In addition to the standard calibration fitting function used for dual energy decomposition, a modified fitting function has been proposed, which brought the tube voltages used in the imaging tasks as the third variable in dual energy decomposition. For an average sized 4.5 cm thick breast, the FOM was maximized with a tube voltage of 46 kVp and a splitting energy of 24 keV. To be consistent with the tube voltage used in current clinical screening exam (∼32 kVp), the optimal splitting energy was proposed to be 22 keV, which offered a FOM greater than 90% of the optimal value. In the experimental investigation, the root-mean-square (RMS) error in breast density quantification for all four phantom studies was estimated to be approximately 1.54% using standard calibration function. The results from the modified fitting function, which integrated the tube voltage as a variable in the calibration, indicated a RMS error of approximately 1.35% for all four studies. The results of the current study suggest that photon-counting spectral mammography systems may potentially be implemented for an accurate quantification of volumetric breast density, with an RMS error of less than 2%, using the proposed dual energy imaging technique.

Analysis of parenchymal texture with digital breast tomosynthesis: comparison with digital mammography and implications for cancer risk assessment

PURPOSE

To correlate the parenchymal texture features at digital breast tomosynthesis (DBT) and digital mammography with breast percent density (PD), an established breast cancer risk factor, in a screening population of women.

MATERIALS AND METHODS

This HIPAA-compliant study was approved by the institutional review board. Bilateral DBT images and digital mammograms from 71 women (mean age, 54 years; age range, 34-75 years) with negative or benign findings at screening mammography were retrospectively collected from a separate institutional review board-approved DBT screening trial (performed from July 2007 to March 2008) in which all women had given written informed consent. Parenchymal texture features of skewness, coarseness, contrast, energy, homogeneity, and fractal dimension were computed from the retroareolar region. Principal component analysis (PCA) was applied to obtain orthogonal texture components. Mammographic PD was estimated with software. Correlation analysis and multiple linear regression with generalized estimating equations were performed to determine the association between texture features and breast PD. Regression was adjusted for age to determine the independent association of texture to breast PD when age was also considered as a predictor variable.

RESULTS

Texture feature correlations to breast PD were stronger with DBT than with digital mammography. Statistically significant correlations (P < .001) were observed for contrast (r = 0.48), energy (r = -0.47), and homogeneity (r = -0.56) at DBT and for contrast (r = 0.26), energy (r = -0.26), and homogeneity (r = -0.33) at digital mammography. Multiple linear regression analysis of PCA texture components as predictors of PD also demonstrated significantly stronger associations with DBT. The association was strongest when age was also considered as a predictor of PD (R² = 0.41 for DBT and 0.28 for digital mammography; P < .001).

CONCLUSION

Parenchymal texture features are more strongly correlated to breast PD in DBT than in digital mammography. The authors' long-term hypothesis is that parenchymal texture analysis with DBT will result in quantitative imaging biomarkers that can improve the estimation of breast cancer risk.

Calibrated measures for breast density estimation

RATIONALE AND OBJECTIVES

Breast density is a significant breast cancer risk factor measured from mammograms. Evidence suggests that the spatial variation in mammograms may also be associated with risk. We investigated the variation in calibrated mammograms as a breast cancer risk factor and explored its relationship with other measures of breast density using full field digital mammography (FFDM).

MATERIALS AND METHODS

A matched case-control analysis was used to assess a spatial variation breast density measure in calibrated FFDM images, normalized for the image acquisition technique variation. Three measures of breast density were compared between cases and controls: (a) the calibrated average measure, (b) the calibrated variation measure, and (c) the standard percentage of breast density (PD) measure derived from operator-assisted labeling. Linear correlation and statistical relationships between these three breast density measures were also investigated.

RESULTS

Risk estimates associated with the lowest to highest quartiles for the calibrated variation measure were greater in magnitude (odds ratios: 1.0 [ref.], 3.5, 6.3, and 11.3) than the corresponding risk estimates for quartiles of the standard PD measure (odds ratios: 1.0 [ref.], 2.3, 5.6, and 6.5) and the calibrated average measure (odds ratios: 1.0 [ref.], 2.4, 2.3, and 4.4). The three breast density measures were highly correlated, showed an inverse relationship with breast area, and related by a mixed distribution relationship.

CONCLUSION

The three measures of breast density capture different attributes of the same data field. These preliminary findings indicate the variation measure is a viable automated method for assessing breast density. Insights gained by this work may be used to develop a standard for measuring breast density.

Polymorphisms in genes involved in the estrogen pathway and mammographic density

Background

Single nucleotide polymorphisms (SNPs) in genes involved in the estrogen pathway appear to be associated with breast cancer risk and possibly with mammographic density (MD), but little is known of these associations among premenopausal women. This study examines the association of 11 polymorphisms in five estrogen-related genes (estrogen receptors alpha and beta (ERα, ERβ), 17β-hydroxysteroid dehydrogenase 1 (HSD17B1), catechol-O-methyltransferase (COMT), cytochrome P450 1B1 (CYP1B1)) with premenopausal MD. Effect modification of four estrogen-related factors (parity, age at menarche, hormonal derivatives use and body mass index (BMI)) on this relation is also assessed.

Methods

Polymorphisms were genotyped in 741 premenopausal Caucasian women whose MD was measured in absolute density (AD, cm²) and percent density using a computer-assisted method. Multivariate linear models were used to examine the associations (P_trend) and interactions (P_i).

Results

None of the SNPs showed a statistically significant association with AD. However, each additional rare allele of rs1056836 CYP1B1 was associated with a reduction in AD among nulliparous women (P_trend = 0.004), while no association was observed among parous women (P_trend = 0.62; P_i = 0.02). An increase in the number of rare alleles of the HSD17B1 SNP (rs598126 and rs2010750) was associated with an increase in AD among women who never used hormonal derivatives (P_trend = 0.06 and P_trend = 0.04, respectively), but with a decrease in AD among past hormonal derivatives users (P_trend = 0.04; P_i = 0.02 and P_trend = 0.08; P_i = 0.01, respectively). Moreover, a negative association of rs598126 HSD17B1 SNP with AD was observed among women with higher BMI (>median) (P_trend = 0.01; P_i = 0.02). A negative association between an increased number of rare alleles of COMT rs4680 SNP and AD was limited to women who never used hormonal derivatives (P_trend = 0.02; P_i = 0.03) or with late age at menarche (>median) (P_trend = 0.03; P_i = 0.02). No significant association was observed between polymorphisms in the ERα or ERβ genes and AD. Similar results, although less significant, were observed when MD was assessed in percent density.

Conclusion

SNPs located in CYP1B1, COMT or HSD17B1 genes seem to be associated with MD in some strata of estrogen-related factors. Our findings suggest that modifying effects of estrogen-related factors should be considered when evaluating associations of polymorphisms in estrogen-related genes with premenopausal mammographic density.

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.

Expression of syndecan-1 in histologically normal breast tissue from postmenopausal women with breast cancer according to mammographic density

OBJECTIVE

To analyze the expression of Syndecan-1 in dense and non-dense human breast tissue.

METHODS

Specimens of histologically normal tissue were obtained from postmenopausal women undergoing surgery for breast cancer. Each tissue block was subject to radiological examination and pair-wise samples of dense and non-dense tissue were collected. Semi-quantitative assessment of immunohistochemical staining intensity for Syndecan-1 and estrogen receptor subtypes was performed.

RESULTS

The expression of Syndecan-1 in all tissue compartments was significantly higher in dense than in non-dense specimens. The strongest staining was recorded in stromal tissue. There was a strong correlation between epithelial estrogen receptor alpha and stromal cell Syndecan-1 expression in dense tissue (rs = 0.7; p = 0.02). This association was absent in non-dense tissue.

CONCLUSION

An increase of Syndecan-1 in all tissue compartments and a redistribution from epithelium to stroma may be a characteristic feature for dense breast tissue.

Parenchymal texture analysis in digital breast tomosynthesis for breast cancer risk estimation: a preliminary study

RATIONALE AND OBJECTIVES

Studies have demonstrated a relationship between mammographic parenchymal texture and breast cancer risk. Although promising, texture analysis in mammograms is limited by tissue superposition. Digital breast tomosynthesis (DBT) is a novel tomographic x-ray breast imaging modality that alleviates the effect of tissue superposition, offering superior parenchymal texture visualization compared to mammography. The aim of this study was to investigate the potential advantages of DBT parenchymal texture analysis for breast cancer risk estimation.

MATERIALS AND METHODS

DBT and digital mammographic (DM) images of 39 women were analyzed. Texture features, shown in previous studies with mammograms to correlate with cancer risk, were computed from the retroareolar breast region. The relative performances of the DBT and DM texture features were compared in correlating with two measures of breast cancer risk: (1) the Gail and Claus risk estimates and (2) mammographic breast density. Linear regression was performed to model the association between texture features and increasing levels of risk.

RESULTS

No significant correlation was detected between parenchymal texture and the Gail and Claus risk estimates. Significant correlations were observed between texture features and breast density. Overall, the DBT texture features demonstrated stronger correlations with breast percent density than DM features (P < or = .05). When dividing the study population into groups of increasing breast percent density, the DBT texture features appeared to be more discriminative, having regression lines with overall lower P values, steeper slopes, and higher R(2) estimates.

CONCLUSION

Although preliminary, the results of this study suggest that DBT parenchymal texture analysis could provide more accurate characterization of breast density patterns, which could ultimately improve breast cancer risk estimation.

Characterizing anatomical variability in breast CT images

Previous work [Burgess et al., Med. Phys. 28, 419-437 (2001)] has shown that anatomical noise in projection mammography results in a power spectrum well modeled over a range of frequencies by a power law, and the exponent (beta) of this power law plays a critical role in determining the size at which a growing lesion reaches the threshold for detection. In this study, the authors evaluated the power-law model for breast computed tomography (bCT) images, which can be thought of as thin sections through a three-dimensional (3D) volume. Under the assumption of a 3D power law describing the distribution of attenuation coefficients in the breast parenchyma, the authors derived the relationship between the power-law exponents of bCT and projection images and found it to be betasection=betaproj-1. They evaluated this relationship on clinical images by comparing bCT images from a set of 43 patients to Burgess' findings in mammography. They were able to make a direct comparison for 6 of these patients who had both a bCT exam and a digitized film-screen mammogram. They also evaluated segmented bCT images to investigate the extent to which the bCT power-law exponent can be explained by a binary model of attenuation coefficients based on the different attenuation of glandular and adipose tissue. The power-law model was found to be a good fit for bCT data over frequencies from 0.07 to 0.45 cyc/mm, where anatomical variability dominates the spectrum. The average exponent for bCT images was 1.86. This value is close to the theoretical prediction using Burgess' published data for projection mammography and for the limited set of mammography data available from the authors' patient sample. Exponents from the segmented bCT images (average value: 2.06) were systematically slightly higher than bCT images, with substantial correlation between the two (r=0.84).

Classification of breast computed tomography data

Differences in breast tissue composition are important determinants in assessing risk, identifying disease in images and following changes over time. This paper presents an algorithm for tissue classification that separates breast tissue into its three primary constituents of skin, fat and glandular tissue. We have designed and built a dedicated breast CT scanner. Fifty-five normal volunteers and patients with mammographically identified breast lesions were scanned. Breast CT voxel data were filtered using a 5 pt median filter and the image histogram was computed. A two compartment Gaussian fit of histogram data was used to provide an initial estimate of tissue compartments. After histogram analysis, data were input to region-growing algorithms and classified as to belonging to skin, fat or gland based on their value and architectural features. Once tissues were classified, a more detailed analysis of glandular tissue patterns and a more quantitative analysis of breast composition was made. Algorithm performance assessment demonstrated very good or excellent agreement between algorithm and radiologist observers in 97.7% of the segmented data. We observed that even in dense breasts the fraction of glandular tissue seldom exceeded 50%. For most individuals the composition is better characterized as being a 70% (fat)-30% (gland) composition than a 50% (fat)-50% (gland) composition.

Power spectral analysis of mammographic parenchymal patterns for breast cancer risk assessment

PURPOSE

The purpose of the study was to evaluate the usefulness of power law spectral analysis on mammographic parenchymal patterns in breast cancer risk assessment.

MATERIALS AND METHODS

Mammograms from 172 subjects (30 women with the BRCA1/BRCA2 gene mutation and 142 low-risk women) were retrospectively collected and digitized. Because age is a very important risk factor, 60 low-risk women were randomly selected from the 142 low-risk subjects and were age matched to the 30 gene mutation carriers. Regions of interest were manually selected from the central breast region behind the nipple of these digitized mammograms and subsequently used in power spectral analysis. The power law spectrum of the form P(f) = B/f(beta) was evaluated for the mammographic patterns. The performance of exponent beta as a decision variable for differentiating between gene mutation carriers and low-risk women was assessed using receiver operating characteristic analysis for both the entire database and the age-matched subset.

RESULTS

Power spectral analysis of mammograms demonstrated a statistically significant difference between the 30 BRCA1/BRCA2 gene mutation carriers and the 142 low risk women with an average beta values of 2.92 (+/-0.28) and 2.47(+/-0.20), respectively. An A (z) value of 0.90 was achieved in distinguishing between gene mutation carriers and low-risk women in the entire database, with an A (z) value of 0.89 being achieved on the age-matched subset.

CONCLUSIONS

The BRCA1/BRCA2 gene mutation carriers and low-risk women have different mammographic parenchymal patterns. It is expected that women identified as high risk by computerized feature analyses might potentially be more aggressively screened for breast cancer.

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

We conducted a cross-sectional study nested within a prospective cohort of breast cancer risk factors and two novel measures of breast density volume among 590 women who had attended Glasgow University (1948–1968), replied to a postal questionnaire (2001) and attended breast screening in Scotland (1989–2002). Volumetric breast density was estimated using a fully automated computer programme applied to digitised film-screen mammograms, from medio-lateral oblique mammograms at the first-screening visit. This measured the proportion of the breast volume composed of dense (non-fatty) tissue (Standard Mammogram Form (SMF)%) and the absolute volume of this tissue (SMF volume, cm³). Median age at first screening was 54.1 years (range: 40.0–71.5), median SMF volume 70.25 cm³ (interquartile range: 51.0–103.0) and mean SMF% 26.3%, s.d.=8.0% (range: 12.7–58.8%). Age-adjusted logistic regression models showed a positive relationship between age at last menstrual period and SMF%, odds ratio (OR) per year later: 1.05 (95% confidence interval: 1.01–1.08, P=0.004). Number of pregnancies was inversely related to SMF volume, OR per extra pregnancy: 0.78 (0.70–0.86, P<0.001). There was a suggestion of a quadratic relationship between birthweight and SMF%, with lowest risks in women born under 2.5 and over 4 kg. Body mass index (BMI) at university (median age 19) and in 2001 (median age 62) were positively related to SMF volume, OR per extra kg m⁻² 1.21 (1.15–1.28) and 1.17 (1.09–1.26), respectively, and inversely related to SMF%, OR per extra kg m⁻² 0.83 (0.79–0.88) and 0.82 (0.76–0.88), respectively, P<0.001. Standard Mammogram Form% and absolute SMF volume are related to several, but not all, breast cancer risk factors. In particular, the positive relationship between BMI and SMF volume suggests that volume of dense breast tissue will be a useful marker in breast cancer studies.

Semiautomatic mammographic parenchymal patterns classification using multiple statistical features

RATIONALE AND OBJECTIVES

Our project was to investigate a complete methodology for the semiautomatic assessment of digital mammograms according to their density, an indicator known to be correlated to breast cancer risk. The BI-RADS four-grade density scale is usually employed by radiologists for reporting breast density, but it allows for a certain degree of subjective input, and an objective qualification of density has therefore often been reported hard to assess. The goal of this study was to design an objective technique for determining breast BI-RADS density.

MATERIALS AND METHODS

The proposed semiautomatic method makes use of complementary pattern recognition techniques to describe manually selected regions of interest (ROIs) in the breast with 36 statistical features. Three different classifiers based on a linear discriminant analysis or Bayesian theories were designed and tested on a database consisting of 1408 ROIs from 88 patients, using a leave-one-ROI-out technique. Classifications in optimal feature subspaces with lower dimensionality and reduction to a two-class problem were studied as well.

RESULTS

Comparison with a reference established by the classifications of three radiologists shows excellent performance of the classifiers, even though extremely dense breasts continue to remain more difficult to classify accurately. For the two best classifiers, the exact agreement percentages are 76% and above, and weighted kappa values are 0.78 and 0.83. Furthermore, classification in lower dimensional spaces and two-class problems give excellent results.

CONCLUSION

The proposed semiautomatic classifiers method provides an objective and reproducible method for characterizing breast density, especially for the two-class case. It represents a simple and valuable tool that could be used in screening programs, training, education, or for optimizing image processing in diagnostic tasks.

Comparing measurements of breast density

Breast density measurements can be made from mammograms using either area-based methods, such as the six category classification (SCC), or volumetric based methods, such as the standard mammogram form (SMF). Previously, we have shown how both types of methods generate breast density estimates which are generally close. In this paper, we switch our attention to the question of why, for certain cases, they provide widely differing estimates. First, we show how the underlying physical models of the breast employed in the methods need to be consistent, and how area-based methods are susceptible to projection effects. We then analyse a set of patients whose mammograms show large differences between their SCC and SMF assessments. More precisely, 12% of 657 patients were found to fall into this category. Of these, 2.7% were attributable to errors either in the SMF segmentation algorithms, human error in SCC categorization or poor image exposure. More importantly, 9.3% of the cases appear to be due to fundamental differences between the area- and volume-based techniques. We conclude by suggesting how we might remove half of those discrepancies by introducing a new categorization of the SMF estimates based on the breast thickness. We note however, that this still leaves 6% of patients with large differences between SMF and SCC estimates. We discuss why it might not be appropriate to assume SMF (or any volume measure) has a similar breast cancer risk prediction capability to SCC.

Computing mammographic density from a multiple regression model constructed with image-acquisition parameters from a full-field digital mammographic unit

Breast density (the percentage of fibroglandular tissue in the breast) has been suggested to be a useful surrogate marker for breast cancer risk. It is conventionally measured using screen-film mammographic images by a labor-intensive histogram segmentation method (HSM). We have adapted and modified the HSM for measuring breast density from raw digital mammograms acquired by full-field digital mammography. Multiple regression model analyses showed that many of the instrument parameters for acquiring the screening mammograms (e.g. breast compression thickness, radiological thickness, radiation dose, compression force, etc) and image pixel intensity statistics of the imaged breasts were strong predictors of the observed threshold values (model R(2) = 0.93) and %-density (R(2) = 0.84). The intra-class correlation coefficient of the %-density for duplicate images was estimated to be 0.80, using the regression model-derived threshold values, and 0.94 if estimated directly from the parameter estimates of the %-density prediction regression model. Therefore, with additional research, these mathematical models could be used to compute breast density objectively, automatically bypassing the HSM step, and could greatly facilitate breast cancer research studies.

Fractal analysis of mammographic parenchymal patterns in breast cancer risk assessment

RATIONALE AND OBJECTIVES

To evaluate fractal-based computerized image analyses of mammographic parenchymal patterns in the task of differentiating between women at high risk and women at low risk for developing breast cancer.

MATERIALS AND METHODS

The fractal-based texture analyses are based on a box-counting method and a Minkowski dimension, and were performed within the parenchymal regions of normal mammograms. Four approaches were evaluated: 1) a conventional box-counting method, 2) a modified box-counting technique using linear discriminant analysis (LDA), 3) a global Minkowski dimension, and 4) a modified Minkowski technique using LDA. These fractal based texture features were extracted from regions of interest to assess the mammographic parenchymal patterns of the images. Receiver operating characteristic analysis was used to evaluate the performance of these features in the task of differentiating between the two groups of women.

RESULTS

Receiver operating characteristic analysis yielded an A(z) value of 0.74 based on the conventional box-counting technique and an A(z) value of 0.84 based on the global Minkowski dimension in the task of distinguishing between the two groups. By using LDA to assess the characteristics of mammograms, A(z) values of 0.90 and 0.93 were obtained in differentiating the two groups, for the modified box-counting and Minkowski techniques, respectively. Statistically significant improvement was achieved (P < .05) with the new techniques compared to the conventional fractal analysis methods. A simulation study, which used the slope and intercept extracted from the least square fit of the experimental data with the LDA approaches, yielded A(z) values similar to those obtained with the conventional approaches in the task of differentiating between the two groups.

CONCLUSIONS

The proposed LDA approach improved significantly the separation between the two groups based on experimental data. Because this approach was used as a linear classifier rather than as a regression function, it combined the fractal analysis with the knowledge of the high- and low-risk patterns, and thus better characterized the multifractal nature of the parenchymal patterns. We believe that the proposed analyses based on the LDA technique to characterize mammographic parenchymal patterns may potentially yield radiographic markers for assessing breast cancer risk.

Breast cancer risk factors in relation to breast density (United States)

Objectives

Evaluate known breast cancer risk factors in relation to breast density.

Methods

We examined factors in relation to breast density in 144,018 New Hampshire (NH) women with at least one mammogram recorded in a statewide mammography registry. Mammographic breast density was measured by radiologists using the BI-RADS classification; risk factors of interest were obtained from patient intake forms and questionnaires.

Results

Initial analyses showed a strong inverse influence of age and body mass index (BMI) on breast density. In addition, women with late age at menarche, late age at first birth, premenopausal women, and those currently using hormone therapy (HT) tended to have higher breast density, while those with greater parity tended to have less dense breasts. Analyses stratified on age and BMI suggested interactions, which were formally assessed in a multivariable model. The impact of current HT use, relative to nonuse, differed across age groups, with an inverse association in younger women, and a positive association in older women (p < 0.0001 for the interaction). The positive effects of age at menarche and age at first birth, and the inverse influence of parity were less apparent in women with low BMI than in those with high BMI (p = 0.04, p < 0.0001 and p = 0.01, respectively, for the interactions). We also noted stronger positive effects for age at first birth in postmenopausal women (p = 0.004 for the interaction). The multivariable model indicated a slight positive influence of family history of breast cancer.

Conclusions

The influence of age at menarche and reproductive factors on breast density is less evident in women with high BMI. Density is reduced in young women using HT, but increased in HT users of age 50 or more.

Chapter 7: The Wisconsin Breast Cancer Epidemiology Simulation Model

The Wisconsin Breast Cancer Epidemiology Simulation Model is a discrete-event, stochastic simulation model using a systems-science modeling approach to replicate breast cancer incidence and mortality in the U.S. population from 1975 to 2000. Four interacting processes are modeled over time: (1) natural history of breast cancer, (2) breast cancer detection, (3) breast cancer treatment, and (4) competing cause mortality. These components form a complex interacting system simulating the lives of 2.95 million women (approximately 1/50 the U.S. population) from 1950 to 2000 in 6-month cycles. After a "burn in" of 25 years to stabilize prevalent occult cancers, the model outputs age-specific incidence rates by stage and age-specific mortality rates from 1975 to 2000. The model simulates occult as well as detected disease at the individual level and can be used to address "What if?" questions about effectiveness of screening and treatment protocols, as well as to estimate benefits to women of specific ages and screening histories.

Mammographic Density Measured with Quantitative Computer-aided Method: Comparison with Radiologists' Estimates and BI-RADS Categories

PURPOSE

To retrospectively compare computer-aided mammographic density estimation (MDEST) with radiologist estimates of percentage density and Breast Imaging Reporting and Data System (BI-RADS) density classification.

MATERIALS AND METHODS

Institutional Review Board approval was obtained for this HIPAA-compliant study; patient informed consent requirements were waived. A fully automated MDEST computer program was used to measure breast density on digitized mammograms in 65 women (mean age, 53 years; range, 24-89 years). Pixel gray levels in detected breast borders were analyzed, and dense areas were segmented. Percentage density was calculated by dividing the number of dense pixels by the total number of pixels within the borders. Seven breast radiologists (five trained with MDEST, two not trained) prospectively assigned qualitative BI-RADS density categories and visually estimated percentage density on 260 mammograms. Qualitative BI-RADS assessments were compared with new quantitative BI-RADS standards. The reference standard density for this study was established by allowing the five trained radiologists to manipulate the MDEST gray-level thresholds, which segmented mammograms into dense and nondense areas. Statistical tests performed include Pearson correlation coefficients, Bland-Altman agreement method, kappa statistics, and unpaired t tests.

RESULTS

There was a close correlation between the reference standard and radiologist-estimated density (R = 0.90-0.95) and MDEST density (R = 0.89). Untrained radiologists overestimated percentage density by an average of 37%, versus 6% for trained radiologists (P < .001). MDEST showed better agreement with the reference standard (average overestimate, 1%; range, -15% to +18%). MDEST correlated better with percentage density than with qualitative BI-RADS categories. There were large overlaps and ranges of percentage density in qualitative BI-RADS categories 2-4. Qualitative BI-RADS categories correlated poorly with new quantitative BI-RADS categories, and 16 (6%) of 260 views were erroneously classified by MDEST.

CONCLUSION

MDEST compared favorably with radiologist estimates of percentage density and is more reproducible than radiologist estimates when qualitative BI-RADS density categories are used. Qualitative and quantitative BI-RADS density assessments differed markedly.

In vivo absorption, scattering, and physiologic properties of 58 malignant breast tumors determined by broadband diffuse optical spectroscopy

Diffuse optical imaging (DOI) may be a beneficial diagnostic method for women with mammographically dense breast tissue. In order to evaluate the utility of DOI, we are developing broadband diffuse optical spectroscopy (DOS) to characterize the functional origins of optical signals in breast cancer patients. Broadband DOS combines multifrequency intensity-modulated and continuous-wave near-infrared light to quantify tissue absorption and scattering spectra from 650 to 1000 nm. Values of intrinsic physiological properties (oxy- and deoxy-hemoglobin, water, lipid, and scatter power) derived from absorption and scattering spectra provide detailed information on breast physiology. We present the results of clinical studies of 58 stage II/III malignant breast tumors using a noninvasive, handheld, broadband DOS probe. On average, eight positions were scanned over tumor and contralateral normal breast for each subject. Intrinsic physiological properties were statistically significantly different for malignant vs. normal tissues for all subjects, without patient age or tumor size/type stratification. Breast tissues containing malignant tumors displayed reduced lipid content ( approximately 20%) and increased water, deoxy-, and oxy-hemoglobin (>50% each) compared to normal breast tissues. Functional perturbations by the tumor were significantly larger than functional variations in normal tissues. A tissue optical index (TOI) derived from intrinsic physiological properties yielded an average two-fold contrast difference between malignant tumors and intrinsic tissue properties. Our results demonstrate that intrinsic optical signals can be influenced by functional perturbations characteristic of malignant transformation; cellular metabolism, extracellular matrix composition, and angiogenesis. Our findings further underscore the importance of broadband measurements and patient age stratification in breast cancer DOI.

Breast composition measurements using retrospective standard mammogram form (SMF)

The standard mammogram form (SMF) representation of an x-ray mammogram is a standardized, quantitative representation of the breast from which the volume of non-fat tissue and breast density can be easily estimated, both of which are of significant interest in determining breast cancer risk. Previous theoretical analysis of SMF had suggested that a complete and substantial set of calibration data (such as mAs and kVp) would be needed to generate realistic breast composition measures and yet there are many interesting trials that have retrospectively collected images with no calibration data. The main contribution of this paper is to revisit our previous theoretical analysis of SMF with respect to errors in the calibration data and to show how and why that theoretical analysis did not match the results from the practical implementations of SMF. In particular, we show how by estimating breast thickness for every image we are, effectively, compensating for any errors in the calibration data. To illustrate our findings, the current implementation of SMF (version 2.2beta) was run over 4028 digitized film-screen mammograms taken from six sites over the years 1988-2002 with and without using the known calibration data. Results show that the SMF implementation running without any calibration data at all generates results which display a strong relationship with when running with a complete set of calibration data, and, most importantly, to an expert's visual assessment of breast composition using established techniques. SMF shows considerable promise in being of major use in large epidemiological studies related to breast cancer which require the automated analysis of large numbers of films from many years previously where little or no calibration data is available.

Initial experiences of using an automated volumetric measure of breast density: the standard mammogram form

Limitations of area based measures of breast density have led several research groups to develop volumetric measures of breast density, for use in predicting risk and in epidemiological research. In this paper, we describe our initial experiences using an automated algorithm (standard mammogram form, SMF) to estimate the volume of the breast that is dense from digitized film mammograms. We performed analyses on 3816 mammograms of 626 women, who were part of the Glasgow Alumni Cohort and had mammograms taken within the Scottish Breast Screening Programme between 1989 and 2002. Absolute volume of dense breast tissue (SMF volume) and the percentage of the volume of the breast that is dense (SMF%) were calculated. The median (interquartile range) of SMF volume was 66 cm3 (48 to 98), and of SMF% was 23.4% (18.6 to 29.7). SMF%, but not SMF volume, was positively related to a six category classification (SCC) of visually assigned area-based breast density (increase in ln(SMF%) per category increase in SCC: 0.04% (95% CI: 0.03-0.05). The SMF algorithm produced lower SMF volume for craniocaudal (CC) compared with mediolateral oblique (MLO) views, but CC/MLO differences for SMF% were small. The mean right/left difference for ln(SMF volume) was -0.027 cm3 (95% confidence interval (CI) -0.044 to -0.009) and of ln(SMF%) was 0.005% (95% CI -0.008% to 0.019%). We present these initial data as a background for future analytical work using SMF.

Volumetric breast density estimation from full-field digital mammograms

A method is presented for estimation of dense breast tissue volume from mammograms obtained with full-field digital mammography (FFDM). The thickness of dense tissue mapping to a pixel is determined by using a physical model of image acquisition. This model is based on the assumption that the breast is composed of two types of tissue, fat and parenchyma. Effective linear attenuation coefficients of these tissues are derived from empirical data as a function of tube voltage (kVp), anode material, filtration, and compressed breast thickness. By employing these, tissue composition at a given pixel is computed after performing breast thickness compensation, using a reference value for fatty tissue determined by the maximum pixel value in the breast tissue projection. Validation has been performed using 22 FFDM cases acquired with a GE Senographe 2000D by comparing the volume estimates with volumes obtained by semi-automatic segmentation of breast magnetic resonance imaging (MRI) data. The correlation between MRI and mammography volumes was 0.94 on a per image basis and 0.97 on a per patient basis. Using the dense tissue volumes from MRI data as the gold standard, the average relative error of the volume estimates was 13.6%.

Influence of Insulin-like Growth Factors on the Strength of the Relation of Vitamin D and Calcium Intakes to Mammographic Breast Density

Diets with higher vitamin D and calcium contents were found associated with lower mammographic breast density and breast cancer risk in premenopausal women. Because laboratory studies suggest that the actions of vitamin D, calcium, insulin-like growth factor (IGF)-I, and IGF-binding protein-3 (IGFBP-3) on human breast cancer cells are interrelated, we examined whether IGF-I and IGFBP-3 levels could affect the strength of the association of vitamin D and calcium intakes with breast density. Among 771 premenopausal women, breast density was measured by a computer-assisted method, vitamin D and calcium intakes by a food frequency questionnaire, and levels of plasma IGF-I and IGFBP-3 by ELISA methods. Multivariate linear regression models were used to examine the associations and the interactions. The negative associations of vitamin D or calcium intakes with breast density were stronger among women with IGF-I levels above the median (beta = -2.8, P = 0.002 and beta = -2.5, P = 0.002, respectively) compared with those with IGF-I levels below or equal to the median (beta = -0.8, P = 0.38 and beta = -1.1, P = 0.21; P(interaction) = 0.09 and 0.16, respectively). Similar results were observed within levels of IGFBP-3 (P(interaction) = 0.06 and 0.03, respectively). This is the first study to report that the negative relation of vitamin D and calcium intakes with breast density may be seen primarily among women with high IGF-I or high IGFBP-3 levels. Our findings suggest that the IGF axis should be taken into account when the effects of vitamin D and calcium on breast density (and perhaps breast cancer risk) are examined at least among premenopausal women.

Breast cancer risk factors and mammographic breast density in women over age 70

BACKGROUND

Breast density is a strong risk factor for breast cancer, but little is known about factors associated with breast density in women over 70.

METHODS

Percent breast density, sex hormone levels and breast cancer risk factor data were obtained on 239 women ages 70-92 recruited from 1986 to 1988 in the United States. Multivariable linear regression was used to develop a model to describe factors associated with percent density.

RESULTS

Median (range) percent density among women was 23.7% (0-85%). Body mass index (beta=-0.345, p<0.001 adjusted for age and parity) and parity (beta=-0.277, p<0.001 adjusted for age and BMI) were significantly and inversely associated with percent breast density. After adjusting for parity and BMI, age was not associated with breast density (beta=0.05, p=0.45). Parous women had lower percent density than nulliparous women (23.7 versus 34.7%, p=0.005). Women who had undergone surgical menopause had greater breast density than those who had had a natural menopause (33.4 versus 24.8%, p=0.048), as did women who were not current smokers (26.0 versus 17.3% for smokers, p=0.02). Breast density was not associated with age at menarche, age at menopause, age at first birth, breastfeeding, estrogen levels or androgen levels. In a multivariable model, 24% of the variance in percent breast density was explained by BMI (beta=-0.35), parity (beta=-0.29), surgical menopause (beta=0.13) and current smoking (beta=-0.12).

CONCLUSION

Factors associated with breast density in older, post-menopausal women differ from traditional breast cancer risk factors and from factors associated with breast density in pre-menopausal and younger post-menopausal women.

Computerized texture analysis of mammographic parenchymal patterns of digitized mammograms

RATIONALE AND OBJECTIVES

Mammographic density and parenchymal patterns have been shown to be related to the risk of developing breast cancer. Thus, computerized texture analysis of breast parenchymal patterns on mammograms may be useful in assessing breast cancer risk.

MATERIALS AND METHODS

A comparative evaluation was conducted of various computer-extracted texture features of mammographic parenchymal patterns of women with BRCA1/BRCA2 gene mutations and those of women at low risk of developing breast cancer. Mammograms from 172 subjects (30 women with either the BRCA1 or BRCA2 gene mutation and 142 low-risk women) were analyzed. Computerized texture features were extracted from regions-of-interest to assess the mammographic parenchymal patterns in the images. Receiver operating characteristic analysis was used to assess the performance of these features in the task of distinguishing between the two groups of women.

RESULTS

Quantitative texture analysis on digitized mammograms demonstrated that gene-mutation carriers and low-risk women have different mammographic parenchymal patterns. Gene-mutation carriers presented with parenchymal patterns that were denser, coarser, and lower in contrast than those of the low-risk group. For the gene-mutation carriers, their mammographic patterns appear to contain less high-frequency component as indicated by higher coarseness values, lower fractal dimensions, and smaller edge gradients, which yielded corresponding A(z) values of 0.79, 0.84, and 0.78, respectively, in the task of distinguishing between gene-mutation carriers and the low-risk group with the entire dataset. The contrast measure calculated from co-occurrence matrix method, which describes local image variation, yielded an A(z) value of 0.86 in distinguishing between the two groups of women.

CONCLUSION

Computerized texture analysis of mammograms provides radiographic descriptors of mammographic parenchymal patterns. The computer-extracted features may be useful for identifying women at high risk for breast cancer and for monitoring the treatment of breast cancer patients.

Mammographic Features and Subsequent Risk of Breast Cancer: A Comparison of Qualitative and Quantitative Evaluations in the Guernsey Prospective Studies

Mammographic features are known to be associated with breast cancer but the magnitude of the effect differs markedly from study to study. Methods to assess mammographic features range from subjective qualitative classifications to computer-automated quantitative measures. We used data from the UK Guernsey prospective studies to examine the relative value of these methods in predicting breast cancer risk. In all, 3,211 women ages > or =35 years who had a mammogram taken in 1986 to 1989 were followed-up to the end of October 2003, with 111 developing breast cancer during this period. Mammograms were classified using the subjective qualitative Wolfe classification and several quantitative mammographic features measured using computer-based techniques. Breast cancer risk was positively associated with high-grade Wolfe classification, percent breast density and area of dense tissue, and negatively associated with area of lucent tissue, fractal dimension, and lacunarity. Inclusion of the quantitative measures in the same model identified area of dense tissue and lacunarity as the best predictors of breast cancer, with risk increasing by 59% [95% confidence interval (95% CI), 29-94%] per SD increase in total area of dense tissue but declining by 39% (95% CI, 53-22%) per SD increase in lacunarity, after adjusting for each other and for other confounders. Comparison of models that included both the qualitative Wolfe classification and these two quantitative measures to models that included either the qualitative or the two quantitative variables showed that they all made significant contributions to prediction of breast cancer risk. These findings indicate that breast cancer risk is affected not only by the amount of mammographic density but also by the degree of heterogeneity of the parenchymal pattern and, presumably, by other features captured by the Wolfe classification.

Evaluating the effect of a wavelet enhancement method in characterization of simulated lesions embedded in dense breast parenchyma

Presence of dense parenchyma in mammographic images masks lesions resulting in either missed detections or mischaracterizations, thus decreasing mammographic sensitivity and specificity. The aim of this study is evaluating the effect of a wavelet enhancement method on dense parenchyma for a lesion contour characterization task, using simulated lesions. The method is recently introduced, based on a two-stage process, locally adaptive denoising by soft-thresholding and enhancement by linear stretching. Sixty simulated low-contrast lesions of known image characteristics were generated and embedded in dense breast areas of normal mammographic images selected from the DDSM database. Evaluation was carried out by an observer performance comparative study between the processed and initial images. The task for four radiologists was to classify each simulated lesion with respect to contour sharpness/unsharpness. ROC analysis was performed. Combining radiologists' responses, values of the area under ROC curve (Az) were 0.93 (95% CI 0.89, 0.96) and 0.81 (CI 0.75, 0.86) for processed and initial images, respectively. This difference in Az values was statistically significant (Student's t-test, P<0.05), indicating the effectiveness of the enhancement method. The specific wavelet enhancement method should be tested for lesion contour characterization tasks in softcopy-based mammographic display environment using naturally occurring pathological lesions and normal cases.

Hormones and mammographic breast density

Mammographic density reveals information about the hormonal environment along with the heritability in which breast cancer develops. This is made possible by the widespread use of population screening by mammography. Increasingly this is an important observation not just for population studies, which reveal disease determinants, but also for the individual. Density reveals the effect of the intrinsic hormonal environment and its background genetics, and also the effect of pharmaceuticals--agents used for disease control and prevention and hormone replacement therapy (HRT) used for well-being around the menopause. Increasingly this focus on the individual will need methods of measurement of density that can be monitored with greater accuracy than the widely used BI-RADS 4 categories. For this purpose studies are under way to measure volume of dense tissue as a continuous variable. In due course, measurement of density will be used as a biomarker of risk, employed in risk models and to monitor interventions. Before this can happen more knowledge will be needed of the change occurring naturally through the menopause and the differences between individuals. This will need specific study backed up with detailed information about the patient on large numbers of women and their mammograms. Currently the widespread use of HRT has increased the prevalence of the dense patterns and potentially may adversely affect the effectiveness of mammographic screening programmes. There is a large literature recording this from which we see that combined continuous preparations of oestrogen progestin are more likely to cause increased density than oestrogen alone or tibolone. Breast density, measured more accurately, has the potential to be an important adjunct to risk estimation and to monitor interventions for breast cancer prevention with pharmaceuticals (such as SERMS) and by change in lifestyle behaviours.

Computerized analysis of mammographic parenchymal patterns for assessing breast cancer risk: Effect of ROI size and location

The long-term goal of our research is to develop computerized radiographic markers for assessing breast density and parenchymal patterns that may be used together with clinical measures for determining the risk of breast cancer and assessing the response to preventive treatment. In our earlier studies, we found that women at high risk tended to have dense breasts with mammographic patterns that were coarse and low in contrast. With our method, computerized texture analysis is performed on a region of interest (ROI) within the mammographic image. In our current study, we investigate the effect of ROI size and ROI location on the computerized texture features obtained from 90 subjects (30 BRCA1/BRCA2 gene-mutation carriers and 60 age-matched women deemed to be at low risk for breast cancer). Mammograms were digitized at 0.1 mm pixel size and various ROI sizes were extracted from different breast regions in the craniocaudal (CC) view. Seventeen features, which characterize the density and texture of the parenchymal patterns, were extracted from the ROIs on these digitized mammograms. Stepwise feature selection and linear discriminant analysis were applied to identify features that differentiate between the low-risk women and the BRCA1/BRCA2 gene-mutation carriers. ROC analysis was used to assess the performance of the features in the task of distinguishing between these two groups. Our results show that there was a statistically significant decrease in the performance of the computerized texture features, as the ROI location was varied from the central region behind the nipple. However, we failed to show a statistically significant decrease in the performance of the computerized texture features with decreasing ROI size for the range studied.

Mammographic density is related to stroma and stromal proteoglycan expression

BACKGROUND

Mammographic density and certain histological changes in breast tissues are both risk factors for breast cancer. However, the relationship between these factors remains uncertain. Previous studies have focused on the histology of the epithelial changes, even though breast stroma is the major tissue compartment by volume. We have previously identified lumican and decorin as abundant small leucine-rich proteoglycans in breast stroma that show altered expression after breast tumorigenesis. In this study we have examined breast biopsies for a relationship between mammographic density and stromal alterations.

METHODS

We reviewed mammograms from women aged 50-69 years who had enrolled in a provincial mammography screening program and had undergone an excision biopsy for an abnormality that was subsequently diagnosed as benign or pre-invasive breast disease. The overall mammographic density was classified into density categories. All biopsy tissue sections were reviewed and tissue blocks from excision margins distant from the diagnostic lesion were selected. Histological composition was assessed in sections stained with haematoxylin and eosin, and the expression of lumican and decorin was assessed by immunohistochemistry; both were quantified by semi-quantitative scoring.

RESULTS

Tissue sections corresponding to regions of high in comparison with low mammographic density showed no significant difference in the density of ductal and lobular units but showed significantly higher collagen density and extent of fibrosis. Similarly, the expression of lumican and decorin was significantly increased.

CONCLUSION

Alteration in stromal composition is correlated with increased mammographic density. Although epithelial changes define the eventual pathway for breast cancer development, mammographic density might correspond more directly to alterations in stromal composition.

Can the stroma provide the clue to the cellular basis for mammographic density?

Mammographic density is recognised as a useful phenotypic biomarker of breast cancer risk. Deeper understanding is needed of the cellular basis, but evidence is limited because of difficulty in designing studies to validate hypotheses. The ductal epithelial components do not adequately explain the physical and dynamic features observed. The stroma is thought to interact with ductal structures in cancer initiation. Stromal tissues might account for the mammographic features, and this interplay can be hypothesised to relate risk to density. In a paper in this issue of Breast Cancer Research, Alowami has shown a relationship between density and stromal proteins, which might provide useful insight into mammographic density.