DBT Algorithm Predicts Breast Cancer Risk

Volumetric calculations of breast density derived from digital breast tomosynthesis (DBT) may be more accurate than similar calculations drawn from conventional 2D digital mammography (DM) in predicting cancer risk, according to a study published in Radiology on September 14, 2021.

The team led by Dr. Aimilia Gasrounioti, Ph. D, from the University of Pennsylvania found, “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).

“The associations between quantitative breast density estimates and breast cancer risk are stronger for digital breast tomosynthesis compared with digital mammography,” concluded the authors.


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. Fully Automated Volumetric Breast Density Estimation from Digital Breast Tomosynthesis. Radiology. Published online September 14, 2021. Accessed September 27, 2021, at https://pubs.rsna.org/doi/10.1148/radiol.2021210190

Allegretto, Amerigo. Researchers tout algorithm that predicts breast cancer risk with DBT. AuntMinnie.com September 15, 2021. Accessed September 27, 2021, at https://www.auntminnie.com/index.aspx?sec=sup&sub=wom&pag=dis&ItemID=133489