Fully automated volumetric breast density estimation from digital breast tomosynthesis

Aimilia Gastounioti, Lauren Pantalone, Christopher G. Scott, Eric A. Cohen, Fang F. Wu, Stacey J. Winham, Matthew R. Jensen, Andrew D.A. Maidment, Celine M. Vachon, Emily F. Conant, Despina Kontos

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


Background: While digital breast tomosynthesis (DBT) is rapidly replacing digital mammography (DM) in breast cancer screening, the potential of DBT density measures for breast cancer risk assessment remains largely unexplored. Purpose: To compare associations of breast density estimates from DBT and DM with breast cancer. Materials and Methods: This retrospective case-control study used contralateral DM/DBT studies from women with unilateral breast cancer and age- and ethnicity-matched controls (September 19, 2011-January 6, 2015). Volumetric percent density (VPD%) was estimated from DBT using previously validated software. For comparison, the publicly available Laboratory for Individualized Breast Radiodensity Assessment software package, or LIBRA, was used to estimate area-based percent density (APD%) from raw and processed DM images. The commercial Quantra and Volpara software packages were applied to raw DM images to estimate VPD% with use of physics-based models. Density measures were compared by using Spearman correlation coefficients (r), and conditional logistic regression was performed to examine density associations (odds ratios [OR]) with breast cancer, adjusting for age and body mass index. Results: A total of 132 women diagnosed with breast cancer (mean age 6 standard deviation [SD], 60 years 6 11) and 528 controls (mean age, 60 years 6 11) were included. Moderate correlations between DBT and DM density measures (r = 0.32-0.75; all P , .001) were observed. Volumetric density estimates calculated from DBT (OR, 2.3 [95% CI: 1.6, 3.4] per SD for VPD%DBT) were more strongly associated with breast cancer than DM-derived density for both APD% (OR, 1.3 [95% CI: 0.9, 1.9] [P , .001] and 1.7 [95% CI: 1.2, 2.3] [P = .004] per SD for LIBRA raw and processed data, respectively) and VPD% (OR, 1.6 [95% CI: 1.1, 2.4] [P = .01] and 1.7 [95% CI: 1.2, 2.6] [P = .04] per SD for Volpara and Quantra, respectively). Conclusion: The associations between quantitative breast density estimates and breast cancer risk are stronger for digital breast tomosynthesis compared with digital mammography.

Original languageEnglish
Pages (from-to)561-568
Number of pages8
Issue number3
StatePublished - Dec 2021


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