Fully Automated Quantitative Estimation of Volumetric Breast Density from Digital Breast Tomosynthesis Images: Preliminary Results and Comparison with Digital Mammography and MR Imaging.
OBJECTIVE
To assess a fully automated method for volumetric breast density (VBD) estimation in digital breast tomosynthesis (DBT) and to compare the findings with those of full-field digital mammography (FFDM) and magnetic resonance (MR) imaging.


METHODS
Bilateral DBT images, FFDM images, and sagittal breast MR images were retrospectively collected from 68 women who underwent breast cancer screening from October 2011 to September 2012 with institutional review board-approved, HIPAA-compliant protocols. A fully automated computer algorithm was developed for quantitative estimation of VBD from DBT images. FFDM images were processed with U.S. Food and Drug Administration-cleared software, and the MR images were processed with a previously validated automated algorithm to obtain corresponding VBD estimates. Pearson correlation and analysis of variance with Tukey-Kramer post hoc correction were used to compare the multimodality VBD estimates.


RESULTS
Estimates of VBD from DBT were significantly correlated with FFDM-based and MR imaging-based estimates with r = 0.83 (95% confidence interval [CI]: 0.74, 0.90) and r = 0.88 (95% CI: 0.82, 0.93), respectively (P < .001). The corresponding correlation between FFDM and MR imaging was r = 0.84 (95% CI: 0.76, 0.90). However, statistically significant differences after post hoc correction (α = 0.05) were found among VBD estimates from FFDM (mean ± standard deviation, 11.1% ± 7.0) relative to MR imaging (16.6% ± 11.2) and DBT (19.8% ± 16.2). Differences between VDB estimates from DBT and MR imaging were not significant (P = .26).


CONCLUSIONS
Fully automated VBD estimates from DBT, FFDM, and MR imaging are strongly correlated but show statistically significant differences. Therefore, absolute differences in VBD between FFDM, DBT, and MR imaging should be considered in breast cancer risk assessment.
From the Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3600 Market St, Suite 360, Philadelphia, PA 19104.
Said
S
Pertuz
From the Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3600 Market St, Suite 360, Philadelphia, PA 19104.
Elizabeth S
ES
McDonald
From the Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3600 Market St, Suite 360, Philadelphia, PA 19104.
Susan P
SP
Weinstein
From the Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3600 Market St, Suite 360, Philadelphia, PA 19104.
Emily F
EF
Conant
From the Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3600 Market St, Suite 360, Philadelphia, PA 19104.
Despina
D
Kontos
D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008327:Mammography / Q000379:methods*; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D012189:Retrospective Studies; D012984:Software