Comparison of breast tissue measurements using magnetic resonance imaging, digital mammography and a mathematical algorithm

Abstract

Women with mostly mammographically dense fibroglandular tissue (breast density, BD) have a four- to six-fold increased risk for breast cancer compared to women with little BD. BD is most frequently estimated from two-dimensional (2D) views of mammograms by a histogram segmentation approach (HSM) and more recently by a mathematical algorithm consisting of mammographic imaging parameters (MATH). Two non-invasive clinical magnetic resonance imaging (MRI) protocols: 3D gradient-echo (3DGRE) and short tau inversion recovery (STIR) were modified for 3D volumetric reconstruction of the breast for measuring fatty and fibroglandular tissue volumes by a Gaussian-distribution curve-fitting algorithm. Replicate breast exams (N = 2 to 7 replicates in six women) by 3DGRE and STIR were highly reproducible for all tissue-volume estimates (coefficients of variation <5%). Reliability studies compared measurements from four methods, 3DGRE, STIR, HSM, and MATH (N = 95 women) by linear regression and intra-class correlation (ICC) analyses. Rsqr, regression slopes, and ICC, respectively, were (1) 0.76-0.86, 0.8-1.1, and 0.87-0.92 for %-gland tissue, (2) 0.72-0.82, 0.64-0.96, and 0.77-0.91, for glandular volume, (3) 0.87-0.98, 0.94-1.07, and 0.89-0.99, for fat volume, and (4) 0.89-0.98, 0.94-1.00, and 0.89-0.98, for total breast volume. For all values estimated, the correlation was stronger for comparisons between the two MRI than between each MRI versus mammography, and between each MRI versus MATH data than between each MRI versus HSM data. All ICC values were >0.75 indicating that all four methods were reliable for measuring BD and that the mathematical algorithm and the two complimentary non-invasive MRI protocols could objectively and reliably estimate different types of breast tissues.

Trial registration: ClinicalTrials.gov NCT00204477 NCT00204490.

Figures

Figure 1

Central axial slice of a volunteer’s left breast using A) 3DGRE and B) STIR MR pulse sequences. For pulse sequences, consult Table 2.

Figure 2

Steps taken to generate a 3-D volume-rendered breast model. 2A, Entire scan field of view consisting of both breasts and torso anatomy; 2B, A coarse segmentation to isolate the breast region of interest; 2C, More precise trimming to remove the chest wall and other non-breast tissue from each individual slice; 2D, Surrounding air image removed from the previous image; 2E, View of the breast slice image after subtracting air image in 2D from image in 2C. A final trimming is performed to complete the breast segmentation from the rest of the patient’s anatomy. 2F, the final 3-D view of the breast model completed for volume measurement analysis.

Figure 3

Mammogram (first column), 3DGRE pulse sequence (second column), and STIR pulse sequence (third column) breast images and, beneath each image, the corresponding signal intensity histograms from 3 women with ~20% G (first two rows for subject 192), ~40% G (third-fourth rows for subject 396), and 60% G (fifth-sixth rows for subject 275). Histograms for 3DGRE and STIR have 2 curves, the upper curve representing unfitted (dots) and fitted (line) curves and the bottom curve representing segmented peaks.

Figure 4

Gaussian curve-fitting analysis of the MRI signal intensity histogram to estimate fibroglandular and fatty breast tissues. (A) histogram from a representative 3DGRE breast model; (B) Gaussian curve fit for fibroglandular breast tissue type (shaded); (C) Gaussian curve fit for fatty tissue type (shaded); (D) sum of the Gaussian curve fit for B and C (shaded) with an Rsqr of 0.92 and an unfitted area (*) representing a region of MRI signal intensities between fibroglandular and fatty tissue types; (E) Gaussian curve fit corresponding to the unfitted area (*) in (D); (F) the sum of the Gaussian curve fit (B+C+E) achieving curve fitting Rsqr of 0.999.

Figure 5

Tissue segmentation based on MRI signal intensity extracted from Gaussian curve fitting analysis. (A) Central slice of a representative breast model; (B) segmented fibroglandular tissue type (green) determined by the Gaussian curve fit (Fig. 4B); (C) segmented tissues (green) representing the Gaussian curve fit to the unfitted area (*) (Fig. 4E); (D) segmented fatty tissue type (green) determined by the Gaussian curve fit (Fig. 4C).

Figure 6

Reliability and reproducibility relationship between any two of the four different methods of measuring %-glandular tissue (panels A1–A6), glandular volume (panels B1–B6), fat volume (panels C1–C6), and total breast volume (panels D1–D3) in 95 women. Linear regression (Rsqr, regression equation and slope with 95% confidence interval and prediction lines) and intra-class correlation (ICC) analyses are shown. Four methods of measurements for (x, y) paired comparison are 3DGRE, STIR, mammogram by histogram segmentation (HSM), and mammogram from a multivariate regression equation (‘mathematical model’, MATH). Volume from each mammogram is taken from the product of mammogram area and breast compression thickness.