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. 2009 Mar;16(3):283-98.
doi: 10.1016/j.acra.2008.08.014.

Parenchymal texture analysis in digital breast tomosynthesis for breast cancer risk estimation: a preliminary study

Despina Kontos  1 , Predrag R Bakic, Ann-Katherine Carton, Andrea B Troxel, Emily F Conant, Andrew D A Maidment
Affiliations

Parenchymal texture analysis in digital breast tomosynthesis for breast cancer risk estimation: a preliminary study

Despina Kontos et al. Acad Radiol. 2009 Mar.

Abstract

Rationale and objectives: Studies have demonstrated a relationship between mammographic parenchymal texture and breast cancer risk. Although promising, texture analysis in mammograms is limited by tissue superposition. Digital breast tomosynthesis (DBT) is a novel tomographic x-ray breast imaging modality that alleviates the effect of tissue superposition, offering superior parenchymal texture visualization compared to mammography. The aim of this study was to investigate the potential advantages of DBT parenchymal texture analysis for breast cancer risk estimation.

Materials and methods: DBT and digital mammographic (DM) images of 39 women were analyzed. Texture features, shown in previous studies with mammograms to correlate with cancer risk, were computed from the retroareolar breast region. The relative performances of the DBT and DM texture features were compared in correlating with two measures of breast cancer risk: (1) the Gail and Claus risk estimates and (2) mammographic breast density. Linear regression was performed to model the association between texture features and increasing levels of risk.

Results: No significant correlation was detected between parenchymal texture and the Gail and Claus risk estimates. Significant correlations were observed between texture features and breast density. Overall, the DBT texture features demonstrated stronger correlations with breast percent density than DM features (P < or = .05). When dividing the study population into groups of increasing breast percent density, the DBT texture features appeared to be more discriminative, having regression lines with overall lower P values, steeper slopes, and higher R(2) estimates.

Conclusion: Although preliminary, the results of this study suggest that DBT parenchymal texture analysis could provide more accurate characterization of breast density patterns, which could ultimately improve breast cancer risk estimation.

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Figures

Figure 1
Figure 1
An illustrative example of (a) digital breast tomosynthesis acquisition geometry with (b) the reconstructed tomographic breast image.
Figure 2
Figure 2
Differences in parenchymal texture in (a) a digital mammogram (DM) and (b–c) the digital breast tomosynthesis (DBT) tomographic slices for the same breast, where (b) the superficial skin layer, in which skin pore texture is visible, is separated from (c) the deeper fibro-glandular tissue layers.
Figure 3
Figure 3
Illustration of the Cumulus (Ver. 4, 2006) software thresholding technique used for mammographic breast percent density (PD) estimation: the image background and the pectoral muscle are excluded (in red), and the dense tissue is segmented by gray-level thresholding (in green). PD is then estimated as the percent of dense tissue within the delineated breast region.
Figure 4
Figure 4
An illustrative example of (a) a 3D ROI segmented from a reconstructed digital breast tomosynthesis (DBT) image and (b) the corresponding 2D ROI from the digital mammogram (DM) of the same breast.
Figure 5
Figure 5
Examples of various mammographic texture patterns: (a) skewness, (b) coarseness, (c) fractal dimension, and (d) contrast.
Figure 6
Figure 6
Scatter-plots of the texture features versus breast percent density (PD) (left) and the Gail lifetime risk estimates (right), for digital mammography (DM) and digital breast tomosynthesis (DBT).
Figure 7
Figure 7
Box-plots with fitted regression lines and associated p-values for digital mammography (DM) and digital breast tomosynthesis (DBT) coarseness, contrast, and fractal dimension texture features versus the five groups of increasing breast percent density (PD): < 10%, 10%≤…< 25%, 25%≤…< 50%, 50%≤…<75%, and 75%≤…< 100%.
Figure 8
Figure 8
Box-plots with fitted regression lines and associated p-values for digital mammography (DM) and digital breast tomosynthesis (DBT) PCA features versus the five groups of increasing breast percent density (PD): < 10%, 10%≤…< 25%, 25%≤…< 50%, 50%≤…<75%, and 75%≤…< 100%.
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