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

Menstrual cycle-related fluctuations in breast density measured by using three-dimensional MR imaging

PURPOSE

To investigate the fluctuation of fibroglandular tissue volume (FV) and percentage of breast density (PD) during the menstrual cycle and compare with postmenopausal women by using three-dimensional magnetic resonance (MR)-based segmentation methods.

MATERIALS AND METHODS

This study was approved by the Institutional Review Board and was HIPAA compliant. Written informed consent was obtained. Thirty healthy female subjects, 24 premenopausal and six postmenopausal, were recruited. All subjects underwent MR imaging examination each week for 4 consecutive weeks. The breast volume (BV), FV, and PD were measured by two operators to evaluate interoperator variation. The fluctuation of each parameter measured over the course of the four examinations was evaluated on the basis of the coefficient of variation (CV).

RESULTS

The results from two operators showed a high Pearson correlation for BV (R(2) = 0.99), FV (R(2) = 0.98), and PD (R(2) = 0.96). The interoperator variation was 3% for BV and around 5%-6% for FV and PD. In the respective premenopausal and postmenopausal groups, the mean CV was 5.0% and 5.6% for BV, 7.6% and 4.2% for FV, and 7.1% and 6.0% for PD. The difference between premenopausal and postmenopausal groups was not significant (all P values > .05).

CONCLUSION

The fluctuation of breast density measured at MR imaging during a menstrual cycle was around 7%. The results may help the design and interpretation of future studies by using the change of breast density as a surrogate marker to evaluate the efficacy of hormone-modifying drugs for cancer treatment or cancer prevention.

Background parenchymal enhancement at breast MR imaging and breast cancer risk

PURPOSE

To examine the relationships between breast cancer and both amount of fibroglandular tissue (FGT) and level of background parenchymal enhancement (BPE) at magnetic resonance (MR) imaging.

MATERIALS AND METHODS

A waiver of authorization was granted by the institutional review board for this retrospective HIPAA-compliant study. Among 1275 women who underwent breast MR imaging screening between December 2002 and February 2008, 39 breast carcinoma cases were identified. Two comparisons were performed: In one comparison, two normal controls--those of the women with negative (benign) findings at breast MR imaging--were matched to each breast cancer case on the basis of age and date of MR imaging. In the second comparison, one false-positive control--that of a woman with suspicious but nonmalignant findings at MR imaging--was similarly matched to each breast cancer case. Two readers independently rated the level of MR imaging-depicted BPE and the amount of MR imaging-depicted FGT by using a categorical scale: BPE was categorized as minimal, mild, moderate, or marked, and FGT was categorized as fatty, scattered, heterogeneously dense, or dense.

RESULTS

Compared with the odds ratio (OR) for a normal control, the OR for breast cancer increased significantly with increasing BPE: The ORs for moderate or marked BPE versus minimal or mild BPE were 10.1 (95% confidence interval [CI]: 2.9, 35.3; P < .001) and 3.3 (95% CI: 1.3, 8.3; P = .006) for readers 1 and 2, respectively. Similar odds were seen when the false-positive controls were compared with the breast cancer cases: The ORs for moderate or marked BPE versus minimal or mild BPE were 5.1 (95% CI: 1.4, 19.1; P = .005) and 3.7 (95% CI: 1.2, 11.2; P = .013) for readers 1 and 2, respectively. The breast cancer odds also increased with increasing FGT, but the BPE findings remained significant after adjustment for FGT.

CONCLUSION

Increased BPE is strongly predictive of breast cancer odds.

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.

A new bias field correction method combining N3 and FCM for improved segmentation of breast density on MRI

PURPOSE

Quantitative breast density is known as a strong risk factor associated with the development of breast cancer. Measurement of breast density based on three-dimensional breast MRI may provide very useful information. One important step for quantitative analysis of breast density on MRI is the correction of field inhomogeneity to allow an accurate segmentation of the fibroglandular tissue (dense tissue). A new bias field correction method by combining the nonparametric nonuniformity normalization (N3) algorithm and fuzzy-C-means (FCM)-based inhomogeneity correction algorithm is developed in this work.

METHODS

The analysis is performed on non-fat-sat T1-weighted images acquired using a 1.5 T MRI scanner. A total of 60 breasts from 30 healthy volunteers was analyzed. N3 is known as a robust correction method, but it cannot correct a strong bias field on a large area. FCM-based algorithm can correct the bias field on a large area, but it may change the tissue contrast and affect the segmentation quality. The proposed algorithm applies N3 first, followed by FCM, and then the generated bias field is smoothed using Gaussian kernal and B-spline surface fitting to minimize the problem of mistakenly changed tissue contrast. The segmentation results based on the N3+FCM corrected images were compared to the N3 and FCM alone corrected images and another method, coherent local intensity clustering (CLIC), corrected images. The segmentation quality based on different correction methods were evaluated by a radiologist and ranked.

RESULTS

The authors demonstrated that the iterative N3+FCM correction method brightens the signal intensity of fatty tissues and that separates the histogram peaks between the fibroglandular and fatty tissues to allow an accurate segmentation between them. In the first reading session, the radiologist found (N3+FCM > N3 > FCM) ranking in 17 breasts, (N3+FCM > N3 = FCM) ranking in 7 breasts, (N3+FCM = N3 > FCM) in 32 breasts, (N3+FCM = N3 = FCM) in 2 breasts, and (N3 > N3+FCM > FCM) in 2 breasts. The results of the second reading session were similar. The performance in each pairwise Wilcoxon signed-rank test is significant, showing N3+FCM superior to both N3 and FCM, and N3 superior to FCM. The performance of the new N3+FCM algorithm was comparable to that of CLIC, showing equivalent quality in 57/60 breasts.

CONCLUSIONS

Choosing an appropriate bias field correction method is a very important preprocessing step to allow an accurate segmentation of fibroglandular tissues based on breast MRI for quantitative measurement of breast density. The proposed algorithm combining N3+FCM and CLIC both yield satisfactory results.

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.

Computational simulation of breast compression based on segmented breast and fibroglandular tissues on magnetic resonance images

This study presents a finite element-based computational model to simulate the three-dimensional deformation of a breast and fibroglandular tissues under compression. The simulation was based on 3D MR images of the breast, and craniocaudal and mediolateral oblique compression, as used in mammography, was applied. The geometry of the whole breast and the segmented fibroglandular tissues within the breast were reconstructed using triangular meshes by using the Avizo 6.0 software package. Due to the large deformation in breast compression, a finite element model was used to simulate the nonlinear elastic tissue deformation under compression, using the MSC.Marc software package. The model was tested in four cases. The results showed a higher displacement along the compression direction compared to the other two directions. The compressed breast thickness in these four cases at a compression ratio of 60% was in the range of 5-7 cm, which is a typical range of thickness in mammography. The projection of the fibroglandular tissue mesh at a compression ratio of 60% was compared to the corresponding mammograms of two women, and they demonstrated spatially matched distributions. However, since the compression was based on magnetic resonance imaging (MRI), which has much coarser spatial resolution than the in-plane resolution of mammography, this method is unlikely to generate a synthetic mammogram close to the clinical quality. Whether this model may be used to understand the technical factors that may impact the variations in breast density needs further investigation. Since this method can be applied to simulate compression of the breast at different views and different compression levels, another possible application is to provide a tool for comparing breast images acquired using different imaging modalities--such as MRI, mammography, whole breast ultrasound and molecular imaging--that are performed using different body positions and under different compression conditions.

Decrease in breast density in the contralateral normal breast of patients receiving neoadjuvant chemotherapy: MR imaging evaluation

PURPOSE

To investigate the change of breast density with quantitative magnetic resonance (MR) imaging in the contralateral normal breast of patients receiving neoadjuvant chemotherapy.

MATERIALS AND METHODS

This study was approved by the institutional review board and was HIPAA compliant. Informed consent was obtained. Fifty-four patients with breast cancer (mean age, 47 years; age range, 30-74 years) treated with NAC protocol and enrolled in a breast MR imaging research study were studied. The density in the contralateral normal breast was analyzed by using an MR imaging-based segmentation method. The effect of chemotherapy on the change of density following the doxorubicin and cyclophosphamide (AC) and the AC and taxane regimen was evaluated. The dependence on age was investigated by using a multivariate regression model.

RESULTS

In patients who underwent both AC and taxane follow-up, the mean percentage of change from the individual's baseline density was -10% (95% confidence interval: -12.8%, -7.2%) after AC and -12.7% (95% confidence interval: -16%, -9.4%) after AC and taxane. In patients who underwent both follow-up studies after one to two and four cycles of AC, the mean percentage of change was -9.4% (95% confidence interval: -13.5%, -5.3%) after one to two cycles of AC and -14.7% (95% confidence interval: -20.6%, -8.7%) after four cycles of AC. The percentage reduction of density was significantly dependent on age. Patients younger than 40 years had a greater reduction after chemotherapy than patients older than 55 years (P = .01).

CONCLUSION

By using three-dimensional MR imaging, patients receiving chemotherapy showed reduction of breast density, and the effects were significant after initial treatment with one to two cycles of the AC regimen.

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.

Quantitative analysis of breast parenchymal patterns using 3D fibroglandular tissues segmented based on MRI

PURPOSE

Mammographic density and breast parenchymal patterns (the relative distribution of fatty and fibroglandular tissue) have been shown to be associated with the risk of developing breast cancer. Percent breast density as determined by mammography is a well-established risk factor, but on the other hand, studies on parenchymal pattern have been scarce, possibly due to the lack of reliable quantitative parameters that can be used to analyze parenchymal tissue distribution. In this study the morphology of fibroglandular tissue distribution was analyzed using three-dimensional breast MRI, which is not subject to the tissue overlapping problem.

METHODS

Four parameters, circularity, convexity, irregularity, and compactness, which are sensitive to the shape and margin of segmented fibroglandular tissue, were analyzed for 230 patients. Cases were assigned to one of two distinct parenchymal breast patterns: Intermingled pattern with intermixed fatty and fibroglandular tissue (Type I, N = 141), and central pattern with confined fibroglandular tissue inside surrounded by fatty tissue outside (Type C, N = 89). For each analyzed parameter, the differentiation between these two patterns was analyzed using a two-tailed t-test based on transformed parameters to normal distribution, as well as distribution histograms and ROC analysis.

RESULTS

These two groups of patients were well matched both in age (50 +/- 11 vs 50 +/- 11) and in fibroglandular tissue volume (Type I: 104 +/- 62 cm3 vs Type C: 112 +/- 73 cm3). Between Type I and Type C breasts, all four morphological parameters showed significant differences that could be used to differentiate between the two breast types. In the ROC analysis, among all four parameters, the "compactness" could achieve the highest area under the curve of 0.84, and when all four parameters were combined, the AUC could be further increased to 0.94.

CONCLUSIONS

The results suggest that these morphological parameters analyzed from 3D MRI can be used to distinguish between intermingled and central dense tissue distribution patterns, and hence may be used to characterize breast parenchymal pattern quantitatively. The availability of these quantitative morphological parameters may facilitate the investigation of the relationship between parenchymal pattern and breast cancer risk.

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.