Volumetric breast density estimation from full-field digital mammograms

Effect of taxane-based neoadjuvant chemotherapy on fibroglandular tissue volume and percent breast density in the contralateral normal breast evaluated by 3T MR

The aim of this study was to evaluate the change of breast density in the normal breast of patients receiving neoadjuvant chemotherapy (NAC). Forty-four breast cancer patients were studied. MRI acquisition was performed before treatment (baseline), and 4 and 12 weeks after treatment. A computer-algorithm-based program was used to segment breast tissue and calculate breast volume (BV), fibroglandular tissue volume (FV), and percent density (PD) (the ratio of FV over BV × 100%). The reduction of FV and PD after treatment was compared with baseline using paired t-tests with a Bonferroni-Holm correction. The association of density reduction with age was analyzed. FV and PD after NAC showed significant decreases compared with the baseline. FV was 110.0 ml (67.2, 189.8) (geometric mean (interquartile range)) at baseline, 104.3 ml (66.6, 164.4) after 4 weeks (p < 0.0001), and 94.7 ml (60.2, 144.4) after 12 weeks (comparison with baseline, p < 0.0001; comparison with 4 weeks, p = 0.016). PD was 11.2% (6.4, 22.4) at baseline, 10.6% (6.6, 20.3) after 4 weeks (p < 0.0001), and 9.7% (6.2, 17.9) after 12 weeks (comparison with baseline, p = 0.0001; comparison with 4 weeks, p = 0.018). Younger patients tended to show a higher density reduction, but overall correlation with age was only moderate (r = 0.28 for FV, p = 0.07, and r = 0.52 for PD, p = 0.0003). Our study showed that breast density measured from MR images acquired at 3T MR can be accurately quantified using a robust computer-aided algorithm based on non-parametric non-uniformity normalization (N3) and an adaptive fuzzy C-means algorithm. Similar to doxorubicin and cyclophosphamide regimens, the taxane-based NAC regimen also caused density atrophy in the normal breast and showed reduction in FV and PD. The effect of breast density reduction was age related and duration related.

Automated chest wall line detection for whole-breast segmentation in sagittal breast MR images

PURPOSE

Breast magnetic resonance imaging (MRI) plays an important role in the clinical management of breast cancer. Computerized analysis is increasingly used to quantify breast MRI features in applications such as computer-aided lesion detection and fibroglandular tissue estimation for breast cancer risk assessment. Automated segmentation of the whole-breast as an organ from the other parts imaged is an important step in aiding lesion localization and fibroglandular tissue quantification. For this task, identifying the chest wall line (CWL) is most challenging due to image contrast variations, intensity discontinuity, and bias field.

METHODS

In this work, the authors develop and validate a fully automated image processing algorithm for accurate delineation of the CWL in sagittal breast MRI. The CWL detection is based on an integrated scheme of edge extraction and CWL candidate evaluation. The edge extraction consists of applying edge-enhancing filters and an edge linking algorithm. Increased accuracy is achieved by the synergistic use of multiple image inputs for edge extraction, where multiple CWL candidates are evaluated by the dynamic time warping algorithm coupled with the construction of a CWL reference. Their method is quantitatively validated by a dataset of 60 3D bilateral sagittal breast MRI scans (in total 3360 2D MR slices) that span the full American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) breast density range. Agreement with manual segmentation obtained by an experienced breast imaging radiologist is assessed by both volumetric and boundary-based metrics, including four quantitative measures.

RESULTS

In terms of breast volume agreement with manual segmentation, the overlay percentage expressed by the Dice's similarity coefficient is 95.0% and the difference percentage is 10.1%. More specifically, for the segmentation accuracy of the CWL boundary, the CWL overlay percentage is 92.7% and averaged deviation distance is 2.3 mm. Their method requires ≈ 4.5 min for segmenting each 3D breast MRI scan (56 slices) in comparison to ≈ 35 min required for manual segmentation. Further analysis indicates that the segmentation performance of their method is relatively stable across the different BI-RADS density categories and breast volume, and also robust with respect to a varying range of the major parameters of the algorithm.

CONCLUSIONS

Their fully automated method achieves high segmentation accuracy in a time-efficient manner. It could support large scale quantitative breast MRI analysis and holds the potential to become integrated into the clinical workflow for breast cancer clinical applications in the future.

Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer

The role of breast image analysis in radiologists' interpretation tasks in cancer risk assessment, detection, diagnosis, and treatment continues to expand. Breast image analysis methods include segmentation, feature extraction techniques, classifier design, biomechanical modeling, image registration, motion correction, and rigorous methods of evaluation. We present a review of the current status of these task-based image analysis methods, which are being developed for the various image acquisition modalities of mammography, tomosynthesis, computed tomography, ultrasound, and magnetic resonance imaging. Depending on the task, image-based biomarkers from such quantitative image analysis may include morphological, textural, and kinetic characteristics and may depend on accurate modeling and registration of the breast images. We conclude with a discussion of future directions.

Optical imaging correlates with magnetic resonance imaging breast density and reveals composition changes during neoadjuvant chemotherapy

Introduction

In addition to being a risk factor for breast cancer, breast density has been hypothesized to be a surrogate biomarker for predicting response to endocrine-based chemotherapies. The purpose of this study was to evaluate whether a noninvasive bedside scanner based on diffuse optical spectroscopic imaging (DOSI) provides quantitative metrics to measure and track changes in breast tissue composition and density. To access a broad range of densities in a limited patient population, we performed optical measurements on the contralateral normal breast of patients before and during neoadjuvant chemotherapy (NAC). In this work, DOSI parameters, including tissue hemoglobin, water, and lipid concentrations, were obtained and correlated with magnetic resonance imaging (MRI)-measured fibroglandular tissue density. We evaluated how DOSI could be used to assess breast density while gaining new insight into the impact of chemotherapy on breast tissue.

Methods

This was a retrospective study of 28 volunteers undergoing NAC treatment for breast cancer. Both 3.0-T MRI and broadband DOSI (650 to 1,000 nm) were obtained from the contralateral normal breast before and during NAC. Longitudinal DOSI measurements were used to calculate breast tissue concentrations of oxygenated and deoxygenated hemoglobin, water, and lipid. These values were compared with MRI-measured fibroglandular density before and during therapy.

Results

Water (r = 0.843; P < 0.001), deoxyhemoglobin (r = 0.785; P = 0.003), and lipid (r = -0.707; P = 0.010) concentration measured with DOSI correlated strongly with MRI-measured density before therapy. Mean DOSI parameters differed significantly between pre- and postmenopausal subjects at baseline (water, P < 0.001; deoxyhemoglobin, P = 0.024; lipid, P = 0.006). During NAC treatment measured at about 90 days, significant reductions were observed in oxyhemoglobin for pre- (-20.0%; 95% confidence interval (CI), -32.7 to -7.4) and postmenopausal subjects (-20.1%; 95% CI, -31.4 to -8.8), and water concentration for premenopausal subjects (-11.9%; 95% CI, -17.1 to -6.7) compared with baseline. Lipid increased slightly in premenopausal subjects (3.8%; 95% CI, 1.1 to 6.5), and water increased slightly in postmenopausal subjects (4.4%; 95% CI, 0.1 to 8.6). Percentage change in water at the end of therapy compared with baseline correlated strongly with percentage change in MRI-measured density (r = 0.864; P = 0.012).

Conclusions

DOSI functional measurements correlate with MRI fibroglandular density, both before therapy and during NAC. Although from a limited patient dataset, these results suggest that DOSI may provide new functional indices of density based on hemoglobin and water that could be used at the bedside to assess response to therapy and evaluate disease risk.

Consistency of breast density measured from the same women in four different MR scanners

PURPOSE

To compare the breast volume (BV), fibroglandular tissue volume (FV), and percent density (PD) measured from breast MRI of the same women using four different MR scanners.

METHODS

The study was performed in 34 healthy Asian volunteers using two 1.5T (GE and Siemens) and two 3T (GE and Philips) MR scanners. The BV, FV, and PD were measured on nonfat-suppressed T1-weighted images using a comprehensive computer algorithm-based segmentation method. The scanner-to-scanner measurement difference, and the coefficient of variation (CV) among the four scanners were calculated. The measurement variation between two density morphological patterns presenting as the central type and the intermingled type was separately analyzed and compared.

RESULTS

All four scanners provided satisfactory image quality allowing for successful completion of the segmentation processes. The measured parameters between each pair of MR scanners were highly correlated, with R(2) ≥ 0.95 for BV, R(2) ≥ 0.99 for FV, and R(2) ≥ 0.97 for PD in all comparisons. The mean percent differences between each pair of scanners were 5.9%-7.8% for BV, 5.3%-6.5% for FV, 4.3%-7.3% for PD; with the overall CV of 5.8% for BV, 4.8% for FV, and 4.9% for PD. The variation of FV was smaller in the central type than in the intermingled type (p = 0.04).

CONCLUSIONS

The results showed that the variation of FV and PD measured from four different MR scanners is around 5%, suggesting the parameters measured using different scanners can be used for a combined analysis in a multicenter study.

Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation

PURPOSE

The amount of fibroglandular tissue content in the breast as estimated mammographically, commonly referred to as breast percent density (PD%), is one of the most significant risk factors for developing breast cancer. Approaches to quantify breast density commonly focus on either semiautomated methods or visual assessment, both of which are highly subjective. Furthermore, most studies published to date investigating computer-aided assessment of breast PD% have been performed using digitized screen-film mammograms, while digital mammography is increasingly replacing screen-film mammography in breast cancer screening protocols. Digital mammography imaging generates two types of images for analysis, raw (i.e., "FOR PROCESSING") and vendor postprocessed (i.e., "FOR PRESENTATION"), of which postprocessed images are commonly used in clinical practice. Development of an algorithm which effectively estimates breast PD% in both raw and postprocessed digital mammography images would be beneficial in terms of direct clinical application and retrospective analysis.

METHODS

This work proposes a new algorithm for fully automated quantification of breast PD% based on adaptive multiclass fuzzy c-means (FCM) clustering and support vector machine (SVM) classification, optimized for the imaging characteristics of both raw and processed digital mammography images as well as for individual patient and image characteristics. Our algorithm first delineates the breast region within the mammogram via an automated thresholding scheme to identify background air followed by a straight line Hough transform to extract the pectoral muscle region. The algorithm then applies adaptive FCM clustering based on an optimal number of clusters derived from image properties of the specific mammogram to subdivide the breast into regions of similar gray-level intensity. Finally, a SVM classifier is trained to identify which clusters within the breast tissue are likely fibroglandular, which are then aggregated into a final dense tissue segmentation that is used to compute breast PD%. Our method is validated on a group of 81 women for whom bilateral, mediolateral oblique, raw and processed screening digital mammograms were available, and agreement is assessed with both continuous and categorical density estimates made by a trained breast-imaging radiologist.

RESULTS

Strong association between algorithm-estimated and radiologist-provided breast PD% was detected for both raw (r = 0.82, p < 0.001) and processed (r = 0.85, p < 0.001) digital mammograms on a per-breast basis. Stronger agreement was found when overall breast density was assessed on a per-woman basis for both raw (r = 0.85, p < 0.001) and processed (0.89, p < 0.001) mammograms. Strong agreement between categorical density estimates was also seen (weighted Cohen's κ ≥ 0.79). Repeated measures analysis of variance demonstrated no statistically significant differences between the PD% estimates (p > 0.1) due to either presentation of the image (raw vs processed) or method of PD% assessment (radiologist vs algorithm).

CONCLUSIONS

The proposed fully automated algorithm was successful in estimating breast percent density from both raw and processed digital mammographic images. Accurate assessment of a woman's breast density is critical in order for the estimate to be incorporated into risk assessment models. These results show promise for the clinical application of the algorithm in quantifying breast density in a repeatable manner, both at time of imaging as well as in retrospective studies.

Breast density as indicator for the use of mammography or MRI to screen women with familial risk for breast cancer (FaMRIsc): a multicentre randomized controlled trial

BACKGROUND

To reduce mortality, women with a family history of breast cancer often start mammography screening at a younger age than the general population. Breast density is high in over 50% of women younger than 50 years. With high breast density, breast cancer incidence increases, but sensitivity of mammography decreases. Therefore, mammography might not be the optimal method for breast cancer screening in young women. Adding MRI increases sensitivity, but also the risk of false-positive results. The limitation of all previous MRI screening studies is that they do not contain a comparison group; all participants received both MRI and mammography. Therefore, we cannot empirically assess in which stage tumours would have been detected by either test.The aim of the Familial MRI Screening Study (FaMRIsc) is to compare the efficacy of MRI screening to mammography for women with a familial risk. Furthermore, we will assess the influence of breast density.

METHODS/DESIGN

This Dutch multicentre, randomized controlled trial, with balanced randomisation (1:1) has a parallel grouped design. Women with a cumulative lifetime risk for breast cancer due to their family history of ≥20%, aged 30-55 years are eligible. Identified BRCA1/2 mutation carriers or women with 50% risk of carrying a mutation are excluded. Group 1 receives yearly mammography and clinical breast examination (n = 1000), and group 2 yearly MRI and clinical breast examination, and mammography biennially (n = 1000).Primary endpoints are the number and stage of the detected breast cancers in each arm. Secondary endpoints are the number of false-positive results in both screening arms. Furthermore, sensitivity and positive predictive value of both screening strategies will be assessed. Cost-effectiveness of both strategies will be assessed. Analyses will also be performed with mammographic density as stratification factor.

DISCUSSION

Personalized breast cancer screening might optimize mortality reduction with less over diagnosis. Breast density may be a key discriminator for selecting the optimal screening strategy for women < 55 years with familial breast cancer risk; mammography or MRI. These issues are addressed in the FaMRIsc study including high risk women due to a familial predisposition.

TRIAL REGISTRATION

Netherland Trial Register NTR2789.

X-ray phase-shifts-based method of volumetric breast density measurement

PURPOSE

The high breast density is one of the biggest risk factors for breast cancer. Identifying patient having persistent high breast density is important for breast cancer screening and prevention. In this work the authors propose for the first time an x-ray phase-shifts-based method of breast density measurement.

METHODS

When x ray traverses the breast, x ray gets not only its intensity attenuated but also its phase shifted. Studying the x-ray phase-shifts generated by the breast tissues, we derived a general formula for determining the volumetric breast density from the breast phase map. The volumetric breast density is reconstructed by retrieving the breast phase map from just a single phase-sensitive projection of the breast, through the use of an innovative phase retrieval method based on the phase-attenuation duality. In order to numerically validate this phase-shifts-based method for measuring the volumetric breast density, the authors performed computer simulations with a digitally simulated anthropomorphic breast phantom.

RESULTS

Using the proposed phase-shifts-based method, we reconstructed the breast phantom's volumetric breast density, which differs from the phantom's intrinsic breast density by only 0.06%. In the presence of noises in the projection image, the reconstructed volumetric breast density differs from the phantom's intrinsic breast density by only 1.79% for a projection signal-to-noise-ratio (SNR) of 34. The error in reconstructed breast density is further reduced to 1.61% and 1.55% for SNR = 68 and SNR = 134, respectively, achieving good accuracies in the breast density determination.

CONCLUSIONS

The authors proposed an x-ray phase-shifts-based method of measuring the volumetric breast density. The simulation results numerically validated the proposed method as a novel method of breast density measurement with good accuracies.

Effect of compression paddle tilt correction on volumetric breast density estimation

For the acquisition of a mammogram, a breast is compressed between a compression paddle and a support table. When compression is applied with a flexible compression paddle, the upper plate may be tilted, which results in variation in breast thickness from the chest wall to the breast margin. Paddle tilt has been recognized as a major problem in volumetric breast density estimation methods. In previous work, we developed a fully automatic method to correct the image for the effect of compression paddle tilt. In this study, we investigated in three experiments the effect of paddle tilt and its correction on volumetric breast density estimation. Results showed that paddle tilt considerably affected accuracy of volumetric breast density estimation, but that effect could be reduced by tilt correction. By applying tilt correction, a significant increase in correspondence between mammographic density estimates and measurements on MRI was established. We argue that in volumetric breast density estimation, tilt correction is both feasible and essential when mammographic images are acquired with a flexible compression paddle.

A novel automated mammographic density measure and breast cancer risk

BACKGROUND

Mammographic breast density is a strong breast cancer risk factor but is not used in the clinical setting, partly because of a lack of standardization and automation. We developed an automated and objective measurement of the grayscale value variation within a mammogram, evaluated its association with breast cancer, and compared its performance with that of percent density (PD).

METHODS

Three clinic-based studies were included: a case-cohort study of 217 breast cancer case subjects and 2094 non-case subjects and two case-control studies comprising 928 case subjects and 1039 control subjects and 246 case subjects and 516 control subjects, respectively. Percent density was estimated from digitized mammograms using the computer-assisted Cumulus thresholding program, and variation was estimated from an automated algorithm. We estimated hazards ratios (HRs), odds ratios (ORs), the area under the receiver operating characteristic curve (AUC), and 95% confidence intervals (CIs) using Cox proportional hazards models for the cohort and logistic regression for case-control studies, with adjustment for age and body mass index. We performed a meta-analysis using random study effects to obtain pooled estimates of the associations between the two mammographic measures and breast cancer. All statistical tests were two-sided.

RESULTS

The variation measure was statistically significantly associated with the risk of breast cancer in all three studies (highest vs lowest quartile: HR = 2.0 [95% CI = 1.3 to 3.1]; OR = 2.7 [95% CI = 2.1 to 3.6]; OR = 2.4 [95% CI = 1.4 to 3.9]; [corrected] all P (trend) < .001). [corrected]. The risk estimates and AUCs for the variation measure were similar to [corrected] those for percent density (AUCs for variation = 0.60-0.62 and [corrected] AUCs for percent density = 0.61-0.65). [corrected]. A meta-analysis of the three studies demonstrated similar associations [corrected] between variation and breast cancer (highest vs lowest quartile: RR = 1.8, 95% CI = 1.4 to 2.3) and [corrected] percent density and breast cancer (highest vs lowest quartile: RR = 2.3, 95% CI = 1.9 to 2.9).

CONCLUSION

The association between the automated variation measure and the risk of breast cancer is at least as strong as that for percent density. Efforts to further evaluate and translate the variation measure to the clinical setting are warranted.

Segmentation of the breast skin and its influence in the simulation of the breast compression during an X-ray mammography

A novel method of skin segmentation is presented aimed to obtain as many pixels belonging to the real skin as possible. This method is validated by experts in radiology. In addition, a biomechanical model of the breast, which considers the skin segmented in this way, is constructed to study the influence of considering real skin in the simulation of the breast compression during an X-ray mammography. The reaction forces of the plates are obtained and compared with the reaction forces obtained using classical methods that model the skin as a 2D membranes that cover all the breast. The results of this work show that, in most of the cases, the method of skin segmentation is accurate and that real skin should be considered in the simulation of the breast compression during the X-ray mammographies.

Comparison of breast density measured on MR images acquired using fat-suppressed versus nonfat-suppressed sequences

PURPOSE

To investigate the difference of MR percent breast density measured from fat-suppressed versus nonfat-suppressed imaging sequences.

METHODS

Breast magnetic resonance imaging (MRI) with and without fat suppression was acquired from 38 subjects. Breasts were divided into subgroups of different morphological patterns ("central" and "intermingled" types). Breast volume, fibroglandular tissue volume, and percent density were measured. The results were compared using nonparametric statistical tests and regarded as significant at p < 0.05.

RESULTS

Breast volume, fibroglandular volume, and percent density between fat-suppressed and nonfat-suppressed sequences were highly correlated. Breast volumes measured on these two sequences were almost identical. Fibroglandular tissue volume and percent density, however, had small (<5%) yet significant differences between the two sequences-they were both higher on the fat-suppressed sequence. Intraobserver variability was within 4% for both sequences and different morphological types. The fibroglandular tissue volume measured on downsampled images showed a small (<5%) yet significant difference.

CONCLUSIONS

The measurement of breast density made on MRI acquired using fat-suppressed and nonfat-suppressed T1W images was about 5% difference, only slightly higher than the intraobserver variability of 3%-4%. When the density data from multiple centers were to be combined, evaluating the degree of difference is needed to take this difference into account.

Full field digital mammography and breast density: comparison of calibrated and noncalibrated measurements

RATIONALE AND OBJECTIVES

Mammographic breast density is an important and widely accepted risk factor for breast cancer. A statement about breast density in the mammographic report is becoming a requirement in many States. However, there is significant inter-observer variation between radiologists in their interpretation of breast density. A properly designed automated system could provide benefits in maintaining consistency and reproducibility. We have developed a new automated and calibrated measure of breast density using full field digital mammography (FFDM). This new measure assesses spatial variation within a mammogram and produced significant associations with breast cancer in a small study. The costs of this automation are delays from advanced image and data analyses before the study can be processed. We evaluated this new calibrated variation measure using a larger dataset than previously. We also explored the possibility of developing an automated measure from unprocessed (raw data) mammograms as an approximation for this calibrated breast density measure.

MATERIALS AND METHODS

A case-control study comprised of 160 cases and 160 controls matched by age, screening history, and hormone replacement therapy was used to compare the calibrated variation measure of breast density with three variants of a noncalibrated measure of spatial variation. The operator-assisted percentage of breast density measure (PD) was used as a standard reference for comparison. Odds ratio (OR) quartile analysis was used to compare these measures. Linear regression analysis was applied to assess the calibration's impact on the raw pixel distribution.

RESULTS

All breast density measures showed significant breast cancer associations. The calibrated spatial variation measure produced the strongest associations (OR: 1.0 [ref.], 4.6, 4.3, 7.4). The associations for PD were diminished in comparison (OR: 1.0 [ref.], 2.7, 2.9, 5.2). Two additional non-calibrated measures restricted in region size also showed significant associations (OR: 1.0 [ref.], 2.9, 4.4, 5.4), and (OR: 1.0 [ref.], 3.5, 3.1, 4.9). Regression analyses indicated the raw image mean is influenced by the calibration more so than its standard deviation.

CONCLUSION

Breast density measures can be automated. The associated calibration produced risk information not retrievable from the raw data representation. Although the calibrated measure produced the stronger association, the non-calibrated measures may offer an alternative to PD and other operator based methods after further evaluation, because they can be implemented automatically with a simple processing algorithm.

Breast volumetry using a three-dimensional surface assessment technique

BACKGROUND

Breast volume is a relevant measure for the prevention and prediction of diseases and for aesthetic surgery. This study evaluated a new technique to determine breast volume and compared measures using a three-dimensional (3D) body surface scanner and magnetic resonance imaging (MRI) scans, with the latter used as the standard method.

METHODS

Both MRI scans and body surface 3D scans were obtained from 22 women. For each method, breast volumes were assessed. The MRI calculations of the breast volumes were performed by a specially trained radiologist using analysis software. A textured 3D image was generated by a calibrated digital texture camera after breast surface data acquisition. The volume assessment of the 3D photography was calculated using a software package after manual outlining of the breast and automated projection of a dorsal limit. Linear regression was used to predict the MRI volume assessment with the 3D image volume assessment.

RESULTS

The mean breast volume according to MRI volumetry was 442.8 ml on the left side and 471.8 ml on the right side. The mean breast volume using a 3D body surface volume assessment method was 273.8 ml (observer A) and 226.2 ml (observer B) on the left side and 284.4 ml (observer A) and 234.9 ml (observer B) on the right side. The use of linear regression models showed R (2) values of 0.59-0.77. The mean time for MRI recording and volume assessment was 68.0 ± 14.1 min for both sides and 11.6 ± 1.5 min for 3D recording and volume assessment.

CONCLUSIONS

The 3D surface-based volume measurements are feasible in terms of time and can predict the MRI breast volume with sufficient accuracy. This might facilitate the broad use of such an assessment technique in a large-scale epidemiologic study using breast volume as a study aim. Additionally, further development of volume assessments could help to implement this technique in breast surgery procedures.

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.

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.

A quantitative description of the percentage of breast density measurement using full-field digital mammography

RATIONALE AND OBJECTIVES

Breast density is a significant breast cancer risk factor that is measured from mammograms. However, uncertainty remains in both understanding its underlying physical properties as it relates to the breast and determining the optimal method for its measurement. A quantitative description of the information captured by the standard operator-assisted percentage of breast density (PD) measure was developed using full-field digital mammography (FFDM) images that were calibrated to adjust for interimage acquisition technique differences.

MATERIALS AND METHODS

The information captured by the standard PD measure was quantified by developing a similar measure of breast density (PD(c)) from calibrated mammograms automatically by applying a static threshold to each image. The specific threshold was estimated by first sampling the probability distributions for breast tissue in calibrated mammograms. A percent glandular (PG) measure of breast density was also derived from calibrated mammograms. The PD, PD(c), and PG breast density measures were compared using both linear correlation (R) and quartile odds ratio measures derived from a matched case-control study.

RESULTS

The standard PD measure is an estimate of the number of pixel values above a fixed idealized x-ray attenuation fraction. There was significant correlation (P < .0001) between the PD(c)-PD (r = 0.78), PD(c)-PG (r = 0.87), and PD-PG (r = 0.71) measures of breast density. Risk estimates associated with the lowest to highest quartiles for the PD(c) measure (odds ratio [OR]: 1.0 ref., 3.4, 3.6, and 5.6), and the standard PD measure (OR 1.0 ref., 2.9, 4.8, and 5.1) were similar and greater than that of the calibrated PG measure (OR 1.0 ref., 2.0, 2.4, and 2.4).

CONCLUSIONS

The information captured by the standard PD measure was quantified as it relates to calibrated mammograms and used to develop an automated method for measuring breast density. These findings represent an initial step for developing an automated measure built on an established calibration platform. A fully developed automated measure may be useful for both research- and clinical-based risk applications.

Comparative study of density analysis using automated whole breast ultrasound and MRI

PURPOSE

The purpose of this study is to compare the measurements of breast density using three-dimensional (3-D) automated whole breast ultrasound (ABUS) and magnetic resonance imaging (MRI).

METHODS

In this study, 3-D ABUS and MRI breast images were obtained from 40 patients-bilaterally in 27 patients and unilaterally (due to operation in the contralateral breast) in 13 patients, To differentiate the fibroglandular and fatty tissues in ABUS and MRI images, the fuzzy C-mean classifier was used. Calculated values for percent density and breast volume from the two modalities were compared to and correlated with linear regression analysis. Intraoperator and interoperator variations among eight cases were evaluated to verify the consistency of the density analysis.

RESULTS

Mean percent density and breast volume derived from ABUS (17.63 +/- 11.87% and 418.30 +/- 132.97 cm3, respectively) and MRI images (23.79 +/- 16.62% and 544.90 +/- 207.41 cm3) demonstrated good correlation (R = 0.917 and R = 0.884). Intraoperator and interoperator analyses yielded slightly larger coefficients of variation for percent density and breast volume in ABUS compared to MRI. However, the differences were not statistically significant.

CONCLUSIONS

ABUS and MRI showed high correlation for breast density and breast volume quantification. Both modalities could provide useful breast density information to physicians.

Effective radiation attenuation calibration for breast density: compression thickness influences and correction

Background

Calibrating mammograms to produce a standardized breast density measurement for breast cancer risk analysis requires an accurate spatial measure of the compressed breast thickness. Thickness inaccuracies due to the nominal system readout value and compression paddle orientation induce unacceptable errors in the calibration.

Method

A thickness correction was developed and evaluated using a fully specified two-component surrogate breast model. A previously developed calibration approach based on effective radiation attenuation coefficient measurements was used in the analysis. Water and oil were used to construct phantoms to replicate the deformable properties of the breast. Phantoms consisting of measured proportions of water and oil were used to estimate calibration errors without correction, evaluate the thickness correction, and investigate the reproducibility of the various calibration representations under compression thickness variations.

Results

The average thickness uncertainty due to compression paddle warp was characterized to within 0.5 mm. The relative calibration error was reduced to 7% from 48-68% with the correction. The normalized effective radiation attenuation coefficient (planar) representation was reproducible under intra-sample compression thickness variations compared with calibrated volume measures.

Conclusion

Incorporating this thickness correction into the rigid breast tissue equivalent calibration method should improve the calibration accuracy of mammograms for risk assessments using the reproducible planar calibration measure.

Three-dimensional microwave imaging of realistic numerical breast phantoms via a multiple-frequency inverse scattering technique

PURPOSE

Breast density measurement has the potential to play an important role in individualized breast cancer risk assessment and prevention decisions. Routine evaluation of breast density will require the availability of a low-cost, nonionizing, three-dimensional (3-D) tomographic imaging modality that exploits a strong properties contrast between dense fibroglandular tissue and less dense adipose tissue. The purpose of this computational study is to investigate the performance of 3-D tomography using low-power microwaves to reconstruct the spatial distribution of breast tissue dielectric properties and to evaluate the modality for application to breast density characterization.

METHODS

State-of-the-art 3-D numerical breast phantoms that are realistic in both structural and dielectric properties are employed. The test phantoms include one sample from each of four classes of mammographic breast density. Since the properties of these phantoms are known exactly, these testbeds serve as a rigorous benchmark for the imaging results. The distorted Born iterative imaging method is applied to simulated array measurements of the numerical phantoms. The forward solver in the imaging algorithm employs the finite-difference time-domain method of solving the time-domain Maxwell's equations, and the dielectric profiles are estimated using an integral equation form of the Helmholtz wave equation. A multiple-frequency, bound-constrained, vector field inverse scattering solution is implemented that enables practical inversion of the large-scale 3-D problem. Knowledge of the frequency-dependent characteristic of breast tissues at microwave frequencies is exploited to obtain a parametric reconstruction of the dispersive dielectric profile of the interior of the breast. Imaging is performed on a high-resolution voxel basis and the solution is bounded by a known range of dielectric properties of the constituent breast tissues. The imaging method is validated using a breast phantom with a single, high-contrast interior scattering target in an otherwise homogeneous interior. The method is then used to image a set of realistic numerical breast phantoms of varied fibroglandular tissue density.

RESULTS

Imaging results are presented for each numerical phantom and show robustness of the method relative to tissue density. In each case, the distribution of fibroglandular tissues is well represented in the resulting images. The resolution of the images at the frequencies employed is wider than the feature dimensions of the normal tissue structures, resulting in a smearing of their reconstruction.

CONCLUSIONS

The results of this study support the utility of 3-D microwave tomography for imaging the distribution of normal tissues in the breast, specifically, dense fibroglandular tissue versus less dense adipose tissue, and suggest that further investigation of its use for volumetric evaluation of breast density is warranted.

Reduction of breast density following tamoxifen treatment evaluated by 3-D MRI: preliminary study

This study analyzed the change in breast density in women receiving tamoxifen treatment using 3-D MRI. Sixteen women were studied. Each woman received breast MRI before and after tamoxifen. The breast and the fibroglandular tissue were segmented using a computer-assisted algorithm, based on T1-weighted images. The fibroglandular tissue volume (FV) and breast volume (BV) were measured and the ratio was calculated as the percent breast density (%BD). The changes in breast volume (ΔBV), fibroglandular tissue volume (ΔFV) and percent density (Δ%BD) between two MRI studies were analyzed and correlated with treatment duration and baseline breast density. The ΔFV showed a reduction in all 16 women. The Δ%BD showed a mean reduction of 5.8%. The reduction of FV was significantly correlated with baseline FV (P<.001) and treatment duration (P=.03). The percentage change in FV was correlated with duration (P=.049). The reduction in %BD was positively correlated with baseline %BD (P=.02). Women with higher baseline %BD showed more reduction of %BD. Three-dimensional MRI may be useful for the measurement of the small changes of ΔFV and Δ%BD after tamoxifen. These changes can potentially be used to correlate with the future reduction of cancer risk.

Age- and race-dependence of the fibroglandular breast density analyzed on 3D MRI

PURPOSE

The purpose of this study was to evaluate the age- and race-dependence of the breast fibroglandular tissue density based on three-dimensional breast MRI.

METHODS

The normal breasts of 321 consecutive patients including Caucasians, Asians, and Hispanics were studied. The subjects were separated into three age groups: Younger than 45, between 45 and 55, and older than 55. Computer algorithms based on body landmarks were used to segment the breast, and fuzzy c-means algorithm was used to segment the fibroglandular tissue. Linear regression analysis was applied to compare mean differences among different age groups and race/ethnicity groups. The obtained parameters were not normally distributed, and the transformed data, natural log (ln) for the fibroglandular tissue volume, and the square root for the percent density were used for statistical analysis.

RESULTS

On the average, the transformed fibroglandular tissue volume and percent density decreased significantly with age. Racial differences in mean transformed percent density were found among women older than 45, but not among women younger than 45. Mean percent density was higher in Asians compared to Caucasians and Hispanics; the difference remained significant after adjustment for age, but not significant after adjusted for both age and breast volume. There was no significant difference in the density between the Caucasians and the Hispanics.

CONCLUSIONS

The results analyzed using the MRI-based method show age- and race-dependence, which is consistent with literature using mammography-based methods.

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.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Impact of skin removal on quantitative measurement of breast density using MRI

PURPOSE

In breast MRI, skin and fibroglandular tissue commonly possess similar signal intensities, and as such, the inclusion of skin as dense tissue leads to an overestimation in the measured density. This study investigated the impact of skin to the quantitative measurement of breast density using MRI.

METHODS

The analysis was performed on the normal breasts of 50 women using nonfat-saturated (nonfat-sat) T1 weighted MR images. The skin was segmented by using a dynamic searching algorithm, which was based on the change in signal intensities from the background air (dark), to the skin (moderate), and then to the fatty tissue (bright). Tissue with moderate intensities that fell between the two boundaries determined based on the intensity gradients (from air to skin, and from skin to fat) was categorized as skin. The percent breast density measured with and without skin exclusion was compared. Also the relationship between the skin volume and the breast volume was investigated. Then, this relationship was used to estimate the skin volume from the breast volume for skin correction.

RESULTS

The percentage of the skin volume normalized to the breast volume ranged from 5.0% to 15.2% (median 8.6%, mean +/- STD 8.8 +/- 2.6%) among 50 women. The percent breast densities measured with skin (y) and without skin (x) were highly correlated, y = 1.23x+7% (r = 0.94, p < 0.001). The relationship between the skin volume and the breast volume was analyzed based on transformed data (the square root of the skin volume vs the cube root of breast volume) using the linear regression, and yielded r = 0.87, p < 0.001. When this model was used to estimate the skin volume for correction in the density analysis, it provided a better fit to the measured density with skin exclusion (with adjusted R2 = 0.98, and root mean square error = 1.6) compared to the correction done by using a fixed cutoff value of 8% (adjusted R2 = 0.83, root mean square error = 4.7).

CONCLUSIONS

The authors have shown that the skin volume is related to the breast volume, and this relationship may be used to correct for the skin effect in the MRI-based density measurement. A reliable quantitative density analysis method will aid in clinical investigation to evaluate the role of breast density for cancer risk assessment or for prediction of the efficacy of risk-modifying drugs using hormonal therapy.

The myth of the 50-50 breast

PURPOSE

For dosimetry and for work in optimization of x-ray imaging of the breast, it is commonly assumed that the breast is composed of 50% fibroglandular tissue and 50% fat. The purpose of this study was to assess whether this assumption was realistic.

METHODS

First, data obtained from an experimental breast CT scanner were used to validate an algorithm that measures breast density from digitized film mammograms. Density results obtained from a total of 2831 women, including 191 women receiving CT and from mammograms of 2640 women from three other groups, were then used to estimate breast compositions.

RESULTS

Mean compositions, expressed as percent fibroglandular tissue (including the skin), varied from 13.7% to 25.6% among the groups with an overall mean of 19.3%. The mean compressed breast thickness for the mammograms was 5.9 cm (sigma = 1.6 cm). 80% of the women in our study had volumetric breast density less than 27% and 95% were below 45%.

CONCLUSIONS

Based on the results obtained from the four groups of women in our study, the "50-50" breast is not a representative model of the breast composition.

Single x-ray absorptiometry method for the quantitative mammographic measure of fibroglandular tissue volume

PURPOSE

This study describes the design and characteristics of a highly accurate, precise, and automated single-energy method to quantify percent fibroglandular tissue volume (%FGV) and fibroglandular tissue volume (FGV) using digital screening mammography.

METHODS

The method uses a breast tissue-equivalent phantom in the unused portion of the mammogram as a reference to estimate breast composition. The phantom is used to calculate breast thickness and composition for each image regardless of x-ray technique or the presence of paddle tilt. The phantom adheres to the top of the mammographic compression paddle and stays in place for both craniocaudal and mediolateral oblique screening views. We describe the automated method to identify the phantom and paddle orientation with a three-dimensional reconstruction least-squares technique. A series of test phantoms, with a breast thickness range of 0.5-8 cm and a %FGV of 0%-100%, were made to test the accuracy and precision of the technique.

RESULTS

Using test phantoms, the estimated repeatability standard deviation equaled 2%, with a +/-2% accuracy for the entire thickness and density ranges. Without correction, paddle tilt was found to create large errors in the measured density values of up to 7%/mm difference from actual breast thickness. This new density measurement is stable over time, with no significant drifts in calibration noted during a four-month period. Comparisons of %FGV to mammographic percent density and left to right breast %FGV were highly correlated (r=0.83 and 0.94, respectively).

CONCLUSIONS

An automated method for quantifying fibroglandular tissue volume has been developed. It exhibited good accuracy and precision for a broad range of breast thicknesses, paddle tilt angles, and %FGV values. Clinical testing showed high correlation to mammographic density and between left and right breasts.

Breast density analysis for whole breast ultrasound images

Breast density has been established as an independent risk factor associated with the development of breast cancer. The terms mammographic density and breast density are often used interchangeably, since most breast density studies are performed with projection mammography. It is known that increase in mammographic density is associated with an increased cancer risk. A sensitive method that allows for the measurement of small changes in breast density may provide useful information for risk management. Despite the efforts to develop quantitative breast density measurements from projection mammograms, the measurements show large variability as a result of projection imaging, differing body position, differing levels of compression, and variation of the x-ray beam characteristics. This study used two separate computer-aided methods, threshold-based and proportion-based evaluations, to analyze breast density on whole breast ultrasound (US) imaging and to compare with the grading results of three radiologists using projection mammography. Thirty-two female subjects with 252 images per case were included in this study. Whole breast US images were obtained from an Aloka SSD-5500 ultrasound machine with an ASU-1004 transducer (Aloka, Japan). Before analyzing breast density, an adaptive speckle reduction filter was used for removing speckle noise, and a robust thresholding algorithm was used to divide breast tissue into fatty or fibroglandular classifications. Then, the proposed approaches were applied for analysis. In the threshold-based method, a statistical model was employed to determine whether each pixel in the breast region belonged to fibroglandular or fatty tissue. The proportion-based method was based on three-dimensional information to calculate the volumetric proportion of fibroglandular tissue to the total breast tissue. The experimental cases were graded by the proposed analysis methods and compared with the ground standard density classification assigned by a majority voting of three experienced breast radiologists. For the threshold-based method, 28 of 32 US test cases and for the proportion-based density classifier, 27 of 32 US test cases were found to be in agreement with the radiologist "ground standard" mammographic interpretations, resulting in overall accuracies of 87.5% and 84.4%, respectively. Moreover, the concordance values of the proposed methods were between 0.0938 and 0.1563, which were less than the average interobserver concordance of 0.3958. The experiment result showed that the proposed methods could be a reference opinion and offer concordant and reliable quantification of breast density for the radiologist.

Magnetic resonance imaging for secondary assessment of breast density in a high-risk cohort

A quantitative measure of three-dimensional breast density derived from noncontrast magnetic resonance imaging (MRI) was investigated in 35 women at high-risk for breast cancer. A semiautomatic segmentation tool was used to quantify the total volume of the breast and to separate volumes of fibroglandular and adipose tissue in noncontrast MRI data. The MRI density measure was defined as the ratio of breast fibroglandular volume over total volume of the breast. The overall correlation between MRI and mammographic density measures was R(2)=.67. However the MRI/mammography density correlation was higher in patients with lower breast density (R(2)=.73) than in patients with higher breast density (R(2)=.26). Women with mammographic density higher than 25% exhibited very different magnetic resonance density measures spread over a broad range of values. These results suggest that MRI may provide a volumetric measure more representative of breast composition than mammography, particularly in groups of women with dense breasts. Magnetic resonance imaging density could potentially be quantified and used for a better assessment of breast cancer risk in these populations.

Development of a quantitative method for analysis of breast density based on three-dimensional breast MRI

Breast density has been established as an independent risk factor associated with the development of breast cancer. It is known that an increase of mammographic density is associated with an increased cancer risk. Since a mammogram is a projection image, different body position, level of compression, and the x-ray intensity may lead to a large variability in the density measurement. Breast MRI provides strong soft tissue contrast between fibroglandular and fatty tissues, and three-dimensional coverage of the entire breast, thus making it suitable for density analysis. To develop the MRI-based method, the first task is to achieve consistency in segmentation of the breast region from the body. The method included an initial segmentation based on body landmarks of each individual woman, followed by fuzzy C-mean (FCM) classification to exclude air and lung tissue, B-spline curve fitting to exclude chest wall muscle, and dynamic searching to exclude skin. Then, within the segmented breast, the adaptive FCM was used for simultaneous bias field correction and fibroglandular tissue segmentation. The intraoperator and interoperator reproducibility was evaluated using 11 selected cases covering a broad spectrum of breast densities with different parenchymal patterns. The average standard deviation for breast volume and percent density measurements was in the range of 3%-4% among three trials of one operator or among three different operators. The body position dependence was also investigated by performing scans of two healthy volunteers, each at five different positions, and found the variation in the range of 3%-4%. These initial results suggest that the technique based on three-dimensional MRI can achieve reasonable consistency to be applied in longitudinal follow-up studies to detect small changes. It may also provide a reliable method for evaluating the change of breast density for risk management of women, or for evaluating the benefits/risks when considering hormonal replacement therapy or chemoprevention.

Breast density assessment in adolescent girls using dual-energy X-ray absorptiometry: a feasibility study

Breast density, the radiographically opaque fraction of the breast in a mammogram, is one of the strongest biomarkers of breast cancer risk. However, younger populations do not typically have mammograms due to radiation concerns. This study explored a commercially available dual-energy X-ray absorptiometer (DXA) system as a low-dose method to measure breast fibroglandular density in adolescent girls. Eighteen girls (13-14 years old) indicated their breast development according to Tanner and underwent three dedicated DXA scans, two of their left and one of their right breasts. Total projected breast area was manually delineated on each image and percent fibroglandular volume density (%FGV), absolute fibroglandular volume (FGV), total breast area, and volume were computed. It was possible to image breasts representing all five Tanner stages; %FGV ranged from 31.9% to 92.2% with a mean of 71.1 +/- 14.8%, whereas FGV ranged from 80 to 270 cm(3) with a mean of 168 +/- 54 cm(3). Left and right breast %FGV were highly correlated (r(p) = 0.97, P < 0.0001) and of the same magnitude (P = 0.18). However, left total volume and FGV were larger than the right by 38 cm(3) (P = 0.04) and 19 cm(3) (P = 0.02), respectively. Total volume and FGV increased by Tanner stage, whereas %FGV did not. Our method had excellent precision for %FGV and moderate precision for FGV (root mean square SDs of 2.4% and 16.6 cm(3)). These pilot data indicate that dedicated DXA breast scans may be useful in studies exploring breast density in girls.

Volumetric breast density evaluation from ultrasound tomography images

Previous ultrasound tomography work conducted by our group showed a direct correlation between measured sound speed and physical density in vitro, and increased in vivo sound speed with increasing mammographic density, a known risk factor for breast cancer. Building on these empirical results, the purpose of this work was to explore a metric to quantify breast density using our ultrasound tomography sound speed images in a manner analogous to computer-assisted mammogram segmentation for breast density analysis. Therefore, volumetric ultrasound percent density (USPD) is determined by segmenting high sound speed areas from each tomogram using a k-means clustering routine, integrating these results over the entire volume of the breast, and dividing by whole-breast volume. First, a breast phantom comprised of fat inclusions embedded in fibroglandular tissue was scanned four times with both our ultrasound tomography clinical prototype (with 4 mm spatial resolution) and CT. The coronal transmission tomograms and CT images were analyzed using semiautomatic segmentation routines, and the integrated areas of the phantom's fat inclusions were compared between the four repeated scans. The average variability for inclusion segmentation was approximately 7% and approximately2%, respectively, and a close correlation was observed in the integrated areas between the two modalities. Next, a cohort of 93 patients was imaged, yielding volumetric coverage of the breast (45-75 sound speed tomograms/patient). The association of USPD with mammographic percent density (MPD) was evaluated using two measures: (1) qualitative, as determined by a radiologist's visual assessment using BI-RADS Criteria and (2) quantitative, via digitization and semiautomatic segmentation of craniocaudal and mediolateral oblique mammograms. A strong positive association between BI-RADS category and USPD was demonstrated [Spearman rho = 0.69 (p < 0.001)], with significant differences between all BI-RADS categories as assessed by one-way ANOVA and Scheffé posthoc analysis. Furthermore, comparing USPD to calculated mammographic density yielded moderate to strong positive associations for CC and MLO views (r2 = 0.75 and 0.59, respectively). These results support the hypothesis that utilizing USPD as an analogue to mammographic breast density is feasible, providing a nonionizing, whole-breast analysis.

Mammographic density. Measurement of mammographic density

Mammographic density has been strongly associated with increased risk of breast cancer. Furthermore, density is inversely correlated with the accuracy of mammography and, therefore, a measurement of density conveys information about the difficulty of detecting cancer in a mammogram. Initial methods for assessing mammographic density were entirely subjective and qualitative; however, in the past few years methods have been developed to provide more objective and quantitative density measurements. Research is now underway to create and validate techniques for volumetric measurement of density. It is also possible to measure breast density with other imaging modalities, such as ultrasound and MRI, which do not require the use of ionizing radiation and may, therefore, be more suitable for use in young women or where it is desirable to perform measurements more frequently. In this article, the techniques for measurement of density are reviewed and some consideration is given to their strengths and limitations.

A novel breast tissue density classification methodology

It has been shown that the accuracy of mammographic abnormality detection methods is strongly dependent on the breast tissue characteristics, where a dense breast drastically reduces detection sensitivity. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. Here, we describe the development of an automatic breast tissue classification methodology, which can be summarized in a number of distinct steps: 1) the segmentation of the breast area into fatty versus dense mammographic tissue; 2) the extraction of morphological and texture features from the segmented breast areas; and 3) the use of a Bayesian combination of a number of classifiers. The evaluation, based on a large kappa = 0.81 and 0.67 for the two data sets) between automatic and expert-based Breast Imaging Reporting and Data System mammographic density assessment.

Mammographic density, breast cancer risk and risk prediction

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

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.

Comparison of a new and existing method of mammographic density measurement: intramethod reliability and associations with known risk factors

BACKGROUND

Mammographic density is one of the strongest risk factors for breast cancer. It is commonly measured by an interactive threshold method that does not fully use information contained in a mammogram. An alternative fully automated standard mammogram form (SMF) method measures density using a volumetric approach.

METHODS

We examined between-breast and between-view agreement, reliability, and associations of breast cancer risk factors with the threshold and SMF measures of breast density on the same set of 1,000 digitized films from 250 women who attended routine breast cancer screening by two-view mammography in 2004 at a London population-based screening center. Data were analyzed using random-effects models on transformed percent density.

RESULTS

Median (interquartile range) percent densities were 12.8% (5.0-22.3) and 21.8% (18.4-26.6) in the threshold and SMF methods, respectively. There was no evidence of systematic differences between left-right breasts or between views in either method. Reliability of a single measurement was lower in the SMF than in the threshold method (0.77 versus 0.92 for craniocaudal and 0.68 versus 0.89 for mediolateral oblique views). Increasing body mass index and parity were associated with reduced density in both methods; however, an increase in density with hormone replacement therapy use was found only with the threshold method.

CONCLUSION

Established properties of mammographic density were observed for SMF percent density; however, this method had poorer left-right reliability than the threshold method and has yet to be shown to be a predictor of breast cancer risk.

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.

A new validation method for X-ray mammogram registration algorithms using a projection model of breast X-ray compression

Establishing spatial correspondence between features visible in X-ray mammograms obtained at different times has great potential to aid assessment and quantitation of change in the breast indicative of malignancy. The literature contains numerous nonrigid registration algorithms developed for this purpose, but existing approaches are flawed by the assumption of inappropriate 2-D transformation models and quantitative estimation of registration accuracy is limited. In this paper, we describe a novel validation method which simulates plausible mammographic compressions of the breast using a magnetic resonance imaging (MRI) derived finite element model. By projecting the resulting known 3-D displacements into 2-D and generating pseudo-mammograms from these same compressed magnetic resonance (MR) volumes, we can generate convincing images with known 2-D displacements with which to validate a registration algorithm. We illustrate this approach by computing the accuracy for two conventional nonrigid 2-D registration algorithms applied to mammographic test images generated from three patient MR datasets. We show that the accuracy of these algorithms is close to the best achievable using a 2-D one-to-one correspondence model but that new algorithms incorporating more representative transformation models are required to achieve sufficiently accurate registrations for this application.