Women with high breast density (BD) have a 4- to 6-fold greater risk for breast cancer than women with low BD. We found that BD can be easily computed from a mathematical algorithm using routine mammographic imaging data or by a curve-fitting algorithm using fat and nonfat suppression magnetic resonance imaging (MRI) data. These BD measures in a strictly defined group of premenopausal women providing both mammographic and breast MRI images were predicted as well by the same set of strong predictor variables as were measures from a published laborious histogram segmentation method and a full field digital mammographic unit in multivariate regression models. We also found that the number of completed pregnancies, C-reactive protein, aspartate aminotransferase, and progesterone were more strongly associated with amounts of glandular tissue than adipose tissue, while fat body mass, alanine aminotransferase, and insulin like growth factor-II appear to be more associated with the amount of breast adipose tissue. Our results show that methods of breast imaging and modalities for estimating the amount of glandular tissue have no effects on the strength of these predictors of BD. Thus, the more convenient mathematical algorithm and the safer MRI protocols may facilitate prospective measurements of BD.
Breast density (BD) reflects the proportion of fibroglandular tissue in the breast and is one of the strongest independent predictors of breast cancer risk [
Mammography is designed to detect early breast cancer rather than to measure BD, and the radiation dose required for detecting cancer is greater for women with dense breasts. The multiple possible variations in instrument settings can confound the use of mammograms for BD estimates, and for this reason phantoms or step-wedge standards are included for calibration of mammography when measuring volumetric density [
Mammography projects a 3-dimensional (3D) tissue into a 2-dimensional (2D) image. Thus, area measured from a 2D image can be expected to deviate from 3D volumes. Shepherd et al. [
The common use of mammography for breast cancer screening is due in part to its low cost. Limitations include a 2D projection of the compressed breast. Due to radiation exposure, mammography is not commonly applied to women less than 45 years old, unless medically indicated. Lack of mammographic imaging data in younger women makes it difficult to assess the role of BD in women of younger age in predicting later-in-life breast cancer risk. Thus, there is increased interest in the use of magnetic resonance imaging (MRI) for acquiring breast images, because it avoids radiation exposure and provides 3D images.
Several feasible MRI protocols for measuring fibroglandular tissue are available and the imaging protocols are typically a variation of clinically used T1 relaxation-rate MRI protocols, with or without fat suppression [
We previously showed that breast glandularity measured as percent glandular tissue (%-G) (commonly referred to in the literature as percent breast density), glandular tissue volume (GV), fat volume (FV), and total volume (TV) from mammographic and MRI images were highly correlated with one another by ordinary least square regression (
The main purpose of this study was to investigate the effects of methods of imaging the breast and measuring BD on biological features that may be associated with BD. BD measures by three new methods (MATH and two MRI methods) and by a FFDM unit were compared to that by a widely used HSM. The two MRI methods were a gradient-echo pulse sequence (3DGRE) and a fat suppressing, fast inversion spin echo pulse sequence (STIR). Data for dependent and independent study variables included only those that could be measured objectively. The study was compliant with HIPAA regulations and was approved by the Institutional Review Board of the University of Texas Medical Branch and the Human Research Protection Office of the US Army Medical Research and Materiel Command. Written informed consent was obtained from all subjects.
Healthy premenopausal women of all major races/ethnicities, living within 80 km of Galveston, Texas, were recruited, using webmail, posted advertisements, and postal mail. Women were 30 to 40 years old with regular monthly menstrual cycles. Subjects who were breast feeding, pregnant, expecting to become pregnant, or had used any type of contraceptive medication (oral, injection, or patch) within the prior 6 months were excluded. Multiple fasting blood samples from two separate menstrual cycles, one screening digital mammogram and two breast MR images, were all obtained during the same or separate luteal phase not more than 3 menstrual cycles apart. Only images of the left breast were analyzed in this study. Anthropometric and reproductive variables were also obtained.
There were four BD outcomes of interest, %-G, GV, FV, and TV, for multivariate regression model analyses. These were obtained in a sample of 320 women by five methods, three from 2D mammography (HSM, MATH, and FFDM) and two from 3D MRI (3DGRE and STIR). The total breast is readily isolated from surrounding background and tissue on both mammographic and MR images. Mammography generated one image and one total breast area/volume for analysis by HSM, MATH, and FFDM, and MRI generated two images and two total breast volume estimates using 3DGRE and STIR.
We developed software in-house for BD analyses using digital mammograms [
Briefly, the breast tissue region of interest (ROI) was isolated from the chest wall and muscle to obtain the total breast area for each mammogram and for generating a signal-intensity histogram of the breast ROI. With the aid of graphical user-interactive software, an analyst subjectively selected suitable signal intensity from the histogram as a threshold that best segmented glandular area (
GV, FV, and TV were the products of the respective tissue mammogram areas, the compression thickness, and a unit correction factor. For the viewing geometry of our imager, the unit correction factor for converting pixel area to mL (or cc) was 9.96, as described previously [
For the MATH method, %-G was computed using the following multivariate regression model equation that included image data from postmenopausal and other premenopausal women not involved in this study [
All variables in (
The FFDM unit itself gives an estimate of percent breast density for each mammogram, which is also available from the mammogram DICOM header as “Raddose” and “precompo.” Values for Raddose are almost the same as for precompo. Raddose values were used to represent %-G from the FFDM unit for calculating GV and FV, according to (
The 3DGRE and STIR breast MRIs were performed as described previously [
Details for the analysis of breast tissue volume in mL or cm3 have been described [
The final segmented 3D volume-rendered breast model was also subjected to volume analysis for the resampled/reconstructed 3D model using GE 3D Advantage Windows Workstation software version 4.1 (GE Healthcare Institute, Waukesha, WI), as follows. The reconstructed voxel size is the size of voxel in mm in both
Body weight (kg), height (m), body mass index (BMI = kg/m2), waist circumference (in cm at the umbilicus), and hip circumference (in cm at the widest point around the buttocks) were obtained. Additionally, total body mass, lean body mass, and fat body mass were measured in duplicate (before and after repositioning), with the subject in a supine position, using dual energy X-ray absorptiometry (DEXA) (Model Discovery A, Model QDR4500A, Hologic, Waltham, MA). Average values of duplicate measurements were used for statistical analyses. Demographic and reproductive information (race, ethnicity, ages of menarche, first pregnancy, last pregnancy, and the number of completed pregnancies) were obtained using a self-administered questionnaire.
Multiple fasting venous blood samples, drawn between 8:00 and 10:00 a.m., and between 20 and 24 days after menstrual spotting, were analyzed for 17
Numerous fasting serum analytes, including glucose, total cholesterol, high-density lipoprotein cholesterol (HDL), triglycerides, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and alkaline phosphatase (ALP), were measured by a certified hospital clinical laboratory using VITROS 5.1 FS (Ortho-Clinical Diagnostics, Rochester, NY).
Data are presented as means and 95% confidence intervals (95% CI) of the mean for continuous variables and as frequencies for the categorical variables (ethnicity and parity). Main outcomes-of-interest are presented as box plots (SigmaPlot 12, Systat Software, Inc., San Jose, CA).
In a sample of 137 subjects from whom blood chemistries and hormone data were available at the time of statistical analyses, univariate associations between dependent variables (%-G, GV, FV, and TV) and predictor variables were computed. Exploratory multivariate analyses between the dependent variables and predictor variables were performed by the GLMSELECT procedure in SAS (with stepwise, forward LAR and LASSO options) to select the best models with information criterion such as AIC, BIC, and Cp options. Good models will have small values of this criterion to select candidate predictors. GLMSELECT models were run with %-G, GV, FV, and TV as dependent variables together with a block of anthropometric measures (body weight, height, BMI, waist and hip circumference, and fat and lean body mass) or a block of blood chemistry variables (a lipid panel of cholesterol, HDL, LDL, VLDL, and triglycerides, liver enzymes of ALP, ALT, and AST, and hormones). Predictor variables, selected consistently in GLMSELECT models for all outcome variables of interest, were included in the final models. We are not aware of any prior studies examining the relationship between routinely measured blood chemistries and BD. Such relationships were explored in this study in a preliminary fashion because the liver metabolizes ovarian steroids, whole body adiposity affects liver function and breast cancer risk, and predictors of GV are few (for more details, see Section
All models were adjusted for age and reproductive variables known to influence BD, such as age of menarche and number of completed pregnancies. IGF-I, IGF-II, 17
The final multivariate model also included methods of measurement of BD as predictor variables and interaction terms between measurement methods and respective predictor variables. We performed similarity test procedures of
The racial/ethnic composition of the study population was 54% non-Hispanic White, 30% Hispanic, and 16% African American. Table
General characteristics of the study subjects (
|
|
---|---|
Race/ethnicity | |
White | 74 (54%) |
Hispanic | 41 (30%) |
Black | 22 (16%) |
|
|
Mean (95% CI) | |
|
|
Demographics and anthropometrics | |
Age, y | 35.9 (35.4, 36.4) |
Weight, kg | 74.8 (72.3, 77.4) |
Height, cm | 161.6 (160.4, 162.7) |
BMI, kg/m2 | 28.7 (27.8, 29.7) |
Fat body mass, kg | 28.2 (26.5, 29.9) |
Lean body mass, kg | 46.9 (45.8, 48.0) |
Waist circumference, cm | 87.3 (85.3, 89.4) |
Hip circumference, cm | 109.7 (107.7, 111.8) |
Reproductive history | |
Age at menarche, y | 12.5 (12.2, 12.8) |
Age at first birth, y | 23.3 (22.5, 24.2) |
Years since last pregnancy | 7.3 (6.4, 8.1) |
Pregnancy, completed | |
Zero | 18 (13.1%) |
One | 17 (12.4%) |
Two | 44 (32.1%) |
Three and more | 58 (42.3%) |
Blood chemistry and hormones | |
Triglycerides, mg/dL | 110.2 (97.4, 123) |
Cholesterol, mg/dL | 178.6 (173.7, 183.6) |
HDL, mg/dL | 53.1 (51, 55.2) |
Alkaline phosphatase (ALP), U/L | 70.6 (67.6, 73.7) |
Alanine aminotransferase (ALT), U/L | 26.9 (25.2, 28.6) |
Aspartate aminotransferase (AST), U/L | 21.1 (19.9, 22.3) |
Sex hormone binding globulin (SHBG), nmol/L | 101.9 (94.9, 108.9) |
C-reactive protein (CRP), mg/L | 6.8 (5.5, 8.1) |
Insulin, |
12.6 (11, 14.2) |
Insulin-like growth factor I (IGF-I), ng/mL | 291.6 (272.4, 310.7) |
Insulin-like growth factor II (IGF-II), ng/mL | 865.1 (824.7, 905.5) |
17 |
132.2 (125.6, 138.9) |
Progesterone, ng/mL | 10.1 (9.2, 10.9) |
Interquartile box plots of breast density (a), total breast tissue volume (b), fibroglandular breast tissue volume (c), and adipose breast tissue (d) in 137 premenopausal women as measured by a histogram segmentation method (HSM), a full field digital mammography unit (FFDM) unit, a mathematical algorithm (MATH), a 3D gradient-echo (3DGRE) pulse sequence MRI, and a short tau inversion recovery pulse sequence (STIR) MRI. For the spread and distribution, consult histograms in Figure
Breast density (%-gland in breast)
Total breast tissue
Fibroglandular breast
Adipose breast
(a)–(d) Scatter plot matrix including Pearson
Correlation matrix scatter plots, Pearson
Correlation matrix scatter plots, Pearson
Correlation matrix scatter plots, Pearson
Correlation matrix scatter plots, Pearson
The 2D mammography provides breast an area measure. Because fatty breast is more easily compressed than dense breast, this differential compression may bias %-breast density when estimated from mammograms. We correlated the area breast measure from mammograms with the volume measures from 3D MR images. Correlation coefficients of measures using areas with corresponding MRI volumes (from 3DGRE and STIR) were 0.83 for glandular area (
Table
Mean differences and 95% confidence interval in percent glandular tissue (%-G), gland volume (GV), fat volume (FV), and total breast volume (TV) by Tukey’s test.
Methods compared | %-G | GV (mL) | FV (mL) | TV (mL) |
---|---|---|---|---|
MATH versus HSMa | 1.1 (−2.85, 4.94)b | 11.4 (−32.52, 55.26) | 10.7 (−55.57, 77.06) | 0 (−75.98, 75.98) |
STIR versus 3DGRE | 2.9 (−1.00, 6.81) | 22.3 (−21.65, 66.29) | 24.3 (−42.17, 90.71) | 1.9 (−74.32, 78.21) |
3DGRE versus HSM | 4.5 (0.56, 8.37)* | 94.1 (50.09, 138.04)* | 51.9 (−14.54, 118.34) | 146.0 (69.7, 222.23)* |
3DGRE versus MATH | 5.5 (1.60, 9.42)* | 105.4 (61.38, 149.49)* | 41.2 (−25.40, 107.71) | 146.0 (69.7, 222.23)* |
3DGRE versus FFDM | 11.5 (7.57, 15.38)* | 138.0 (94.05, 181.99)* | 7.9 (−58.49, 74.38) | 146.0 (69.7, 222.23)* |
STIR versus HSM | 1.6 (−2.22, 5.34) | 71.8 (27.94, 115.56)* | 76.2 (9.97, 142.36)* | 147.9 (71.93, 223.90)* |
STIR versus MATH | 2.6 (−1.29, 6.50) | 83.1 (39.23, 127.01)* | 65.4 (−0.89, 131.74)* | 147.9 (71.93, 223.90)* |
STIR versus FFDM | 8.6 (4.68, 12.46)* | 115.7 (71.89, 159.51)* | 33.2 (−98.40, 33.98) | 147.9 (71.93, 223.90)* |
FFDM versus HSM | 7.0 (3.12, 10.90)* | 44.0 (0.14, 87.76)* | 44 (−22.24, 110.15) | 0 (−75.98, 75.98) |
FFDM versus MATH | 6.0 (2.07, 9.86)* | 32.6 (−11.31, 76.47) | 33.2 (−33.20, 99.53) | 0 (−75.98, 75.98) |
bMean (95% confidence interval).
*Difference between means, significance at
There is no gold standard for calibrating BD, and the physics behind mammography and MRI differs. Therefore, it is important to know whether correlations with biological factors known to predict breast %-G, GV, FV, and TV are affected by measurement methods.
The univariate analysis results between dependent and independent variables are shown in Table
Pearson’s correlation coefficients between dependent and selected independent variables of study population (
Variables | Pearson’s correlation coefficients | ||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Prob > |
|||||||||||||||||||||
%-G | Total breast tissue | Glandular tissue | Fatty breast tissue | ||||||||||||||||||
HSM | FFDM | MATH | 3DGRE | STIR | HSMArea | HSM | 3DGRE | STIR | HSMArea | HSM | FFDM | MATH | 3DGRE | STIR | HSMArea | HSM | FFDM | MATH | 3DGRE | STIR | |
Age | −0.06 |
−0.10 |
−0.09 |
−0.11 |
−0.07 |
−0.13 |
−0.12 |
−0.06 |
−0.08 |
−0.14 |
−0.13 |
−0.13 |
−0.14 |
−0.16 |
−0.13 |
−0.08 |
−0.09 |
−0.04 |
−0.09 |
−0.03 |
−0.06 |
Age at menarche | 0.12 |
0.09 |
0.11 |
0.08 |
0.11 |
−0.10 |
−0.07 |
−0.07 |
−0.07 |
0.07 |
0.05 |
0.00 |
−0.01 |
0.01 |
0.05 |
−0.13 |
−0.10 |
−0.06 |
−0.08 |
−0.08 |
−0.09 |
|
|||||||||||||||||||||
Weight | −0.59 |
−0.57 |
−0.55 |
−0.62 |
−0.61 |
0.64 |
0.65 |
0.75 |
0.74 |
−0.08 |
0.04 |
−0.20 |
0.16 |
−0.02 |
0.09 |
0.72 |
0.75 |
0.74 |
0.73 |
0.78 |
0.76 |
Height | 0.06 |
0.10 |
0.04 |
0.11 |
0.13 |
−0.16 |
−0.14 |
−0.13 |
−0.12 |
−0.09 |
−0.10 |
−0.03 |
−0.12 |
−0.04 |
−0.04 |
−0.14 |
−0.12 |
−0.12 |
−0.12 |
−0.13 |
−0.12 |
BMI | −0.61 |
−0.61 |
−0.57 |
−0.66 |
−0.66 |
0.70 |
0.70 |
0.80 |
0.78 |
−0.04 |
0.08 |
−0.18 |
0.20 |
0.00 |
0.11 |
0.77 |
0.79 |
0.78 |
0.78 |
0.83 |
0.80 |
Fat body mass | −0.61 |
−0.62 |
−0.60 |
−0.66 |
−0.67 |
0.69 |
0.72 |
0.82 |
0.81 |
−0.04 |
0.11 |
−0.21 |
0.23 |
0.03 |
0.12 |
0.76 |
0.80 |
0.80 |
0.79 |
0.85 |
0.83 |
Lean body mass | −0.44 |
−0.36 |
−0.35 |
−0.41 |
−0.38 |
0.42 |
0.44 |
0.48 |
0.47 |
−0.09 |
0.00 |
−0.10 |
0.13 |
−0.02 |
0.08 |
0.48 |
0.51 |
0.47 |
0.48 |
0.50 |
0.48 |
Waist circumference | −0.60 |
−0.60 |
−0.59 |
−0.67 |
−0.66 |
0.69 |
0.69 |
0.78 |
0.76 |
−0.04 |
0.08 |
−0.19 |
0.17 |
−0.02 |
0.09 |
0.76 |
0.77 |
0.76 |
0.77 |
0.82 |
0.79 |
Hip circumference | −0.55 |
−0.51 |
−0.50 |
−0.58 |
−0.56 |
0.59 |
0.62 |
0.71 |
0.70 |
−0.07 |
0.06 |
−0.14 |
0.18 |
0.00 |
0.10 |
0.66 |
0.70 |
0.67 |
0.68 |
0.74 |
0.72 |
|
|||||||||||||||||||||
Triglycerides | −0.13 |
−0.15 |
−0.18 |
−0.24 |
−0.19 |
0.16 |
0.21 |
0.20 |
0.19 |
0.07 |
0.13 |
0.04 |
0.11 |
0.04 |
0.10 |
0.15 |
0.19 |
0.18 |
0.21 |
0.20 |
0.17 |
Cholesterol | −0.06 |
−0.06 |
−0.06 |
−0.08 |
−0.11 |
0.12 |
0.14 |
0.11 |
0.10 |
0.07 |
0.10 |
0.08 |
0.13 |
0.08 |
0.05 |
0.11 |
0.12 |
0.09 |
0.12 |
0.10 |
0.10 |
HDL | 0.36 |
0.41 |
0.40 |
0.43 |
0.39 |
−0.28 |
−0.29 |
−0.33 |
−0.32 |
0.11 |
0.02 |
0.20 |
0.01 |
0.11 |
0.02 |
−0.34 |
−0.35 |
−0.39 |
−0.36 |
−0.37 |
−0.35 |
Alkaline phosphatase | −0.22 |
−0.29 |
−0.26 |
−0.34 |
−0.35 |
0.37 |
0.42 |
0.46 |
0.44 |
0.10 |
0.18 |
−0.05 |
0.18 |
0.09 |
0.11 |
0.36 |
0.41 |
0.43 |
0.43 |
0.45 |
0.44 |
Alanine aminotransferase | −0.09 |
−0.11 |
−0.08 |
−0.11 |
−0.11 |
0.15 |
0.13 |
0.13 |
0.13 |
0.05 |
0.06 |
−0.02 |
0.06 |
0.05 |
0.06 |
0.14 |
0.13 |
0.13 |
0.13 |
0.13 |
0.12 |
Aspartate aminotransferase | 0.12 |
0.09 |
0.14 |
0.08 |
0.10 |
−0.04 |
0.01 |
−0.04 |
−0.04 |
0.12 |
0.15 |
0.14 |
0.14 |
0.14 |
0.12 |
−0.08 |
−0.05 |
−0.07 |
−0.05 |
−0.07 |
−0.07 |
|
|||||||||||||||||||||
Sex hormone binding globulin | 0.28 |
0.33 |
0.32 |
0.35 |
0.36 |
−0.41 |
−0.40 |
−0.43 |
−0.43 |
−0.10 |
−0.15 |
0.02 |
−0.16 |
−0.07 |
−0.17 |
−0.41 |
−0.40 |
−0.39 |
−0.42 |
−0.43 |
−0.42 |
C-reactive protein | −0.22 |
−0.31 |
−0.23 |
−0.36 |
−0.35 |
0.50 |
0.56 |
0.60 |
0.59 |
0.20 |
0.30 |
0.03 |
0.35 |
0.21 |
0.26 |
0.46 |
0.53 |
0.52 |
0.53 |
0.57 |
0.57 |
Insulin | −0.20 |
−0.26 |
−0.18 |
−0.28 |
−0.28 |
0.39 |
0.43 |
0.46 |
0.45 |
0.15 |
0.23 |
0.04 |
0.26 |
0.15 |
0.22 |
0.37 |
0.41 |
0.39 |
0.40 |
0.44 |
0.43 |
IGF-I | 0.15 |
0.21 |
0.21 |
0.22 |
0.28 |
−0.22 |
−0.18 |
−0.24 |
−0.24 |
−0.05 |
−0.06 |
0.06 |
−0.05 |
0.00 |
0.02 |
−0.22 |
−0.19 |
−0.21 |
−0.21 |
−0.24 |
−0.26 |
IGF-II | −0.12 |
−0.11 |
−0.16 |
−0.20 |
−0.18 |
0.24 |
0.25 |
0.26 |
0.25 |
0.07 |
0.10 |
0.02 |
0.09 |
0.03 |
0.02 |
0.23 |
0.25 |
0.23 |
0.27 |
0.26 |
0.26 |
17 |
0.06 |
0.19 |
0.12 |
0.13 |
0.12 |
−0.08 |
−0.04 |
−0.09 |
−0.10 |
0.05 |
0.08 |
0.23 |
0.12 |
0.16 |
0.14 |
−0.11 |
−0.08 |
−0.17 |
−0.09 |
−0.13 |
−0.14 |
Progesterone | 0.12 |
0.14 |
0.15 |
0.11 |
0.16 |
−0.12 |
−0.06 |
−0.09 |
−0.09 |
0.03 |
0.06 |
0.13 |
0.07 |
0.08 |
0.10 |
−0.14 |
−0.10 |
−0.14 |
−0.11 |
−0.11 |
−0.12 |
The primary objective of our study was to investigate the effects of the five BD measurement methods (HSM, FFDM, MATH, 3DGRE, and STIR) on profiles of biological predictors of %-G, GV, FV, and TV.
Exploratory models were run to select strong predictors for inclusion in final multivariate models. Fat body mass, BMI, and waist-to-hip ratio were most frequently selected as predictor anthropometric variables by PROC GLMSELECT in the exploratory models. Due to strong collinearity, BMI, fat body mass, and waist-to-hip ratio were tested one at a time in the multivariate models. BMI was included in the final models, but it can be replaced by fat body mass with minimal change in the profiles and strength of significant independent predictors, that is, in terms of
Predictor variables for BD, included in the final multivariate models, were BMI, age, age of menarche, and number of completed pregnancies (
Multivariate analysis model estimates for percent breast density (%-G), fibroglandular tissue volume (GV), fat tissue volume (FV), and total breast volume (TV) measured by five different methods (
Explanatory variable | Standardized |
|||
---|---|---|---|---|
%-G | GV | FV | TV | |
BMI | −0.59 (0.09)*** | −0.10 (0.11) | 0.65 (0.07)*** | 0.52 (0.08)*** |
Age | −0.09 (0.07) | −0.13 (0.08) | −0.09 (0.05) | −0.12 (0.06)* |
Age at menarche | 0.13 (0.07) | 0.13 (0.08) | −0.05 (0.05) | 0.01 (0.06) |
Pregnancy, completed | ||||
Zero | Reference | |||
One | 0.22 (0.26) | −0.29 (0.32) | −0.28 (0.21) | −0.34 (0.23) |
Two | −0.52 (0.21)* | −0.81 (0.27)** | −0.08 (0.17) | −0.34 (0.19) |
Three and more | −0.52 (0.22)* | −0.99 (0.28)*** | −0.18 (0.18) | −0.49 (0.20)** |
Alkaline phosphatase (ALP) | −0.06 (0.08) | 0.06 (0.10) | 0.12 (0.06)* | 0.12 (0.07) |
Alanine aminotransferase (ALT) | −0.22 (0.10)* | −0.16 (0.12) | 0.07 (0.08) | 0.01 (0.09) |
Aspartate aminotransferase (AST) | 0.32 (0.10)*** | 0.24 (0.12)* | −0.15 (0.08) | −0.04 (0.09) |
Insulin-like growth factor I (IGF-I) | −0.03 (0.07) | −0.10 (0.09) | −0.04 (0.06) | −0.07 (0.06) |
Insulin-like growth factor II (IGF-II) | −0.10 (0.07) | 0.03 (0.09) | 0.16 (0.06)** | 0.15 (0.06)* |
Sex hormone binding globulin (SHBG) | 0.07 (0.07) | −0.09 (0.09) | −0.05 (0.06) | −0.07 (0.06) |
C-reactive protein (CRP) | 0.13 (0.09) | 0.23 (0.11)* | 0.05 (0.07) | 0.13 (0.08) |
17 |
−0.09 (0.07) | 0.01 (0.09) | 0.02 (0.06) | 0.02 (0.06) |
Progesterone | 0.13 (0.07)* | 0.20 (0.08)* | −0.02 (0.05) | 0.05 (0.06) |
Measurement methods# | ||||
Histogram segmentation method (HSM) | Reference | |||
Full field digital mammography (FFDM) | −0.02 (0.27) | −0.12 (0.33) | −0.10 (0.21) | 0 (0.23) |
Mathematical algorithm (MATH) | 0.10 (0.27) | 0.01 (0.33) | −0.01 (0.21) | 0 (0.23) |
3D gradient-echo MRI (3DGRE) | −0.04 (0.27) | −0.13 (0.34) | −0.01 (0.21) | −0.10 (0.24) |
Short tau inversion recovery MRI (STIR) | −0.10 (0.27) | −0.14 (0.33) | −0.003 (0.21) | −0.09 (0.23) |
Race and ethnicity | ||||
Non-Hispanic White | Reference | |||
Hispanic | 0.30 (0.16) | 0.32 (0.20) | −0.11 (0.13) | 0.02 (0.14) |
African-American | 0.40 (0.20)* | 0.34 (0.25) | −0.07 (0.16) | 0.06 (0.18) |
Model |
0.54 | 0.29 | 0.71 | 0.65 |
#All
Multivariate analysis model estimates for percent breast density (%-G), fibroglandular tissue volume (GV), fat tissue volume (FV), and total breast volume (TV) measured by five different methods (
Explanatory variable | Standardized |
|||
---|---|---|---|---|
%-G | GV | FV | TV | |
BMI | −0.62 (0.02)*** | 0.03 (0.03) | 0.79 (0.02)*** | 0.71 (0.02)*** |
Age | −0.03 (0.2) | −0.01 (0.02) | −0.02 (0.02) | −0.02 (0.02) |
Age at menarche | 0.03 (0.2) | 0.03 (0.02) | 0.01 (0.02) | 0.02 (0.02) |
Pregnancy, completed | ||||
Zero | Reference | |||
One | −0.08 (0.08) | −0.002 (0.1) | 0.18 (0.07)* | 0.16 (0.07)* |
Two | −0.24 (0.07)** | −0.37 (0.08)*** | 0.005 (0.06) | −0.14 (0.06)* |
Three and more | −0.41 (0.07)*** | −0.69 (0.08)*** | −0.12 (0.06)* | −0.35 (0.06)*** |
Measurement methoda,b | ||||
HSM | Reference | |||
GE | −0.003 (0.06) | −0.01 (0.07) | 0.002 (0.05) | 0 (0.05) |
MATH | 0.01 (0.06) | 0.01 (0.07) | 0.001 (0.05) | 0 (0.05) |
3DGRE | 0.002 (0.06) | −0.003 (0.07) | 0.003 (0.05) | 0 (0.06) |
STIR | 0.03 (0.06) | 0.01 (0.08) | −0.02 (0.05) | −0.02 (0.06) |
Race and ethnicity | ||||
Non-Hispanic White race | Reference | |||
Hispanic race | 0.16 (0.05)** | 0.33 (0.06)*** | −0.12 (0.04)** | 0.01 (0.04) |
African-American race | 0.47 (0.06)*** | 0.39 (0.08)*** | −0.22 (0.05)*** | −0.05 (0.06) |
Model |
|
|
|
|
bInteraction terms between predictors and measurement methods were all not significant (results not shown).
Within each multivariate analysis, an interaction term for each predictor variable with measurement methods was also included. All of the interaction terms between measurement methods and biological predictor variables by deviance or likelihood ratio tests were not significant (e.g., all
The first nested model within the multivariate model for %-G (Table
We recently demonstrated that two mammography (HSM and MATH) and two MRI-based modalities (3DGRE and STIR) could reliably measure breast tissue composition (i.e., %-G, GV, FV, and TV), in that all intraclass correlation and regression coefficient values were >0.75 [
The strong predictors of %-G, FV, and TV found in our sample of younger women (30 to 40 years old) were, in general, in line with those reported in older women. Briefly, whole body adiposity is predictive for breast tissue adiposity, and it explained the major portion of the variances found in %-G, FV, and TV [
Parity has been consistently reported to be negatively associated with GV [
SHBG and CRP correlated strongly with %-G, TV, GV, and FV in correlation analyses (Table
Obesity and the metabolic syndrome have been implicated in breast cancer risk [
CRP, AST, progesterone, and the number of completed pregnancies are more strongly associated with amounts of glandular tissue than with breast adipose tissue. Fat body mass, ALT, and IGF-II appear to be more associated with the amount of breast adipose rather than with glandular tissue. Associations between CRP, ALT, and AST and breast tissue composition have not been reported previously, to our knowledge, and further studies will be necessary to illuminate the mechanisms involved.
The strengths of this study were the inclusion of a population of multiethnic, premenopausal subjects with strictly defined characteristics who were not using exogenous hormones. All study samples were obtained during luteal phases within a short interval. Mean levels of hormones and blood chemistries from multiple blood samples were used for statistical analyses. To our knowledge, no other studies have validated biological features predicting BD as measured by both mammography and MRI in the same study subjects. Weaknesses of the study include a relatively small number of subjects with available measures of blood chemistries and hormones, a narrow age range for inclusion, and the exclusion of postmenopausal women and breast cancer patients, thereby limiting inferences. The parameter fit coefficients for the MATH equation, while applicable for postmenopausal women as previously described [
In summary, we found similarities among determinants of breast %-G, GV, FV, and TV measured by five different methods. Our results suggest that the two MRI protocols and the mathematical algorithm that we developed should be further tested in studies of risk factors related to BD and breast cancer. Importantly, the MATH method was able to adjust for the inherent manipulation of imaging parameters by the mammography unit. Whether MATH algorithm improves risk prediction studies of breast density or breast cancer risk deserves further study as it can automatically compute BD upon mammogram acquisition. The two MRI protocols are complimentary in image acquisition for adipose and gland tissue. The sensitivity and specificity of these methods in measuring the effects of interventions that may reduce breast density and breast cancer risk require further study.
Breast density
Histogram segmentation method
Mathematical algorithm
Full field digital mammography
Magnetic resonance imaging
%-Glandular tissue or %-breast density
Glandular/fibroglandular breast volume
Fat/adipose breast volume
Total breast volume
a 3D gradient-echo MRI pulse sequence
A fat suppressing short tau inversion recovery MRI pulse sequence
Echo time
Repetition time
Inversion time
Field of view
Number of excitations
Body mass index
Alanine aminotransferase
Aspartate aminotransferase
Alkaline phosphatase
Sex hormone binding globulin
C-reactive protein
Insulin-like growth factor I
Insulin-like growth factor-II.
The authors declare that there is no conflict of interests regarding the publication of this paper.
Lee-Jane W. Lu had overall responsibility for the conception, design, and management of the study and acquisition, analyses, and interpretation of the data for presentation and publications and has given final approval of the version to be published. Fatima Nayeem contributed to study management, including sample and data acquisition, quality-control, laboratory analyses, statistical analyses, interpretation of the data, preparation of the paper, and approval of the version to be published. Hyunsu Ju had overall responsibility for design and conduct of statistical model analyses and interpretation of the data, contributed to manuscript preparation, and approved the version to be published. Donald G. Brunder had overall responsibility for the conception of bioinformatics infrastructure for retrieving and archiving radiologic imaging data, designed and developed software for BD analyses, and was involved in data interpretation and approval of the version to be published. Karl E. Anderson had overall responsibility for conception and design of the clinical aspect of the study, interpreted clinical data and outcomes of interest, and has contributed to and approved the version to be published. Manubai Nagamani had overall responsibility for conception, design, acquisition, and interpretation of the reproductive endocrine aspect of clinical and research data and has participated in and approved the version to be published. Tuenchit Khamapirad had overall responsibility for conception, design, acquisition, and interpretation of mammography and magnetic resonance images of the breast, and design of BD analyses and has approved the version to be published. Dr. Raleigh F. Johnson, Dr. Jr., Dr. and Thomas Nishino developed the MRI image analysis methods. Rett Hutto performed the density analyses and was instrumental in finalizing the BD analysis protocol. Katrina Jencks performed many of the histogram segmentation analyses. Additionally, Mouyong Liu developed the research database. Xin Ma performed serum hormone assays. Their contributions to the project are greatly appreciated.
This research was supported by the US Army Medical Research and Materiel Command under DADM17-01-1-0417. (The content of the information does not necessarily reflect the position or the policy of the Government and no official endorsement should be inferred. The US Army Medical Research Acquisition Activity, 820 Chandler Street, Fort Detrick, MD, is the awarding and administering acquisition office.) The research was also supported, in part, by Grants from the National Institute of Health (NIH) R01 CA95545 and CA65628. This study was conducted with the support of the Institute for Translational Sciences at the University of Texas Medical Branch, supported in part by a Clinical and Translational Science Award (UL1TR000071) from the National Center for Advancing Translational Sciences, NIH, and by NIEHS 2 P30 ES06676. The study is registered at