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Updated: May 30, 2026
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
Published on: December 15, 2014
Analysis of parenchymal texture with digital breast tomosynthesis: comparison with digital mammography and
Despina Kontos1, Lynda C Ikejimba, Predrag R Bakic
1Department of Radiology, University of Pennsylvania Health System, Philadelphia PA 19104-4206, USA. Despina.Kontos@uphs.upenn.edu
Purpose:
To correlate the parenchymal texture features at digital breast tomosynthesis (DBT) and digital mammography with breast percent density (PD), an established breast cancer risk factor, in a screening population of women.
Materials And Methods:
This HIPAA-compliant study was approved by the institutional review board. Bilateral DBT images and digital mammograms from 71 women (mean age, 54 years; age range, 34-75 years) with negative or benign findings at screening mammography were retrospectively collected from a separate institutional review board-approved DBT screening trial (performed from July 2007 to March 2008) in which all women had given written informed consent. Parenchymal texture features of skewness, coarseness, contrast, energy, homogeneity, and fractal dimension were computed from the retroareolar region. Principal component analysis (PCA) was applied to obtain orthogonal texture components. Mammographic PD was estimated with software. Correlation analysis and multiple linear regression with generalized estimating equations were performed to determine the association between texture features and breast PD. Regression was adjusted for age to determine the independent association of texture to breast PD when age was also considered as a predictor variable.
Results:
Texture feature correlations to breast PD were stronger with DBT than with digital mammography. Statistically significant correlations (P < .001) were observed for contrast (r = 0.48), energy (r = -0.47), and homogeneity (r = -0.56) at DBT and for contrast (r = 0.26), energy (r = -0.26), and homogeneity (r = -0.33) at digital mammography. Multiple linear regression analysis of PCA texture components as predictors of PD also demonstrated significantly stronger associations with DBT. The association was strongest when age was also considered as a predictor of PD (R² = 0.41 for DBT and 0.28 for digital mammography; P < .001).
Conclusion:
Parenchymal texture features are more strongly correlated to breast PD in DBT than in digital mammography. The authors' long-term hypothesis is that parenchymal texture analysis with DBT will result in quantitative imaging biomarkers that can improve the estimation of breast cancer risk.
