Breast density analysis of digital breast tomosynthesis

Abstract

Mammography shifted to digital breast tomosynthesis (DBT) in the US. An automated percentage of breast density (PD) technique designed for two-dimensional (2D) applications was evaluated with DBT using several breast cancer risk prediction measures: normalized-volumetric; dense volume; applied to the volume slices and averaged (slice-mean); and applied to synthetic 2D images. Volumetric measures were derived theoretically. PD was modeled as a function of compressed breast thickness (CBT). The mean and standard deviation of the pixel values were investigated. A matched case-control (CC) study (n = 426 pairs) was evaluated. Odd ratios (ORs) were estimated with 95% confidence intervals. ORs were significant for PD: identical for volumetric and slice-mean measures [OR = 1.43 (1.18, 1.72)] and [OR = 1.44 (1.18, 1.75)] for synthetic images. A 2nd degree polynomial (concave-down) was used to model PD as a function of CBT: location of the maximum PD value was similar across CCs, occurring at 0.41 × CBT, and PD was significant [OR = 1.47 (1.21, 1.78)]. The means from the volume and synthetic images were also significant [ORs ~ 1.31 (1.09, 1.57)]. An alternative standardized 2D synthetic image was constructed, where each pixel value represents the percentage of breast density above its location. Several measures were significant and an alternative method for constructing a standardized 2D synthetic image was produced.

Figure 1

Image Illustrations: in the top row (a) C-View image for illustration-1; (b) respective central slice image; (c) C-View image for illustration-2; and (d) respective central slice image. Outlines in the top-row images are the largest rectangles that fit within the breast areas. These regions are shown in the second row with the same ordering for illustration purposes. Illustration-1 has 89 μm pixel spacing and illustration-2 has 107 μm pixel spacing.

Figure 2

Breast Density Detection: this shows the density detection for the illustrations: (a) C-view, illustration-1; (b) respective central slice image; (c) C-view, illustration-2; and (d) respective central slice.

Figure 3

Projected Standardized Synthetic Breast Density Images: these show the standardized, s(x,y), images for illustration-1 (left) and illustration-2 (right) resulting from Eq. (8). Pixel values represent the percentage of dense tissue in the breast volume above their locations.

Figure 4

Regression analysis with PD_vol as a function of PD_m This shows the scatter plot between the two measures (points) and regression line (solid red), The analysis gave: slope ≈ 1.002, intercept ≈ − 0.0201, and linear correlation ≈ 1.0. The [mean, standard deviations] for the distributions were [21.60, 1.98] for PD_vol and [21.58, 1.98] for PD_m.

Figure 5

PD slice Profiles: this shows PD (y-axis) by slice number using the normalized distance from the breast support surface (P on the x-axis) for illustration-1 (left) and illustration-2 (right). PD values per slice number (points) were fitted with a second-degree polynomial (solid). The slice distance increases as the distance increases from the breast support surface (left side of each plot).

Figure 6

DBT Volume Slice 2nd Degree Polynomial Coefficient Histograms: these show the normalized histograms for the fit-coefficients, (a,b,c), separated by case (top-row) and control status (bottom-row).

Figure 7

Regression analysis with PD_vol as a function of PD_syn: this shows the scatter plot between the two measures (points) and regression line (red-solid): slope ≈ 0.92 and standard error ≈ 0.01, intercept ≈ − 1.4, and R ≈ 0.93. The [mean, standard deviations] for the distributions were [21.60, 1.98] for PD_vol and [25.25, 2.01] for PD_syn.