Fully Automated Quantitative Estimation of Volumetric Breast Density from Digital Breast Tomosynthesis Images: Preliminary Results and Comparison with Digital Mammography and MR Imaging

Abstract

Purpose: 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.

Materials and 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).

Conclusion: 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.

Figure 1a:

Algorithm outline for VBD estimation from DBT images. The process is performed in four steps: (a) Dense versus nondense tissue segmentation is conducted on tomosynthesis source projection images (the green boundaries represent the dense tissue). (b) A tridimensional likelihood map is constructed from the projection images (an intermediate section is shown). The brightness indicates the probability of being dense tissue. (c) Features are extracted from each tomosynthesis reconstructed section (color shows intensity), and (d) the 3D fibroglandular tissue is segmented on the basis of multifeature support vector machine classification.

Figure 1b:

Algorithm outline for VBD estimation from DBT images. The process is performed in four steps: (a) Dense versus nondense tissue segmentation is conducted on tomosynthesis source projection images (the green boundaries represent the dense tissue). (b) A tridimensional likelihood map is constructed from the projection images (an intermediate section is shown). The brightness indicates the probability of being dense tissue. (c) Features are extracted from each tomosynthesis reconstructed section (color shows intensity), and (d) the 3D fibroglandular tissue is segmented on the basis of multifeature support vector machine classification.

Figure 1c:

Algorithm outline for VBD estimation from DBT images. The process is performed in four steps: (a) Dense versus nondense tissue segmentation is conducted on tomosynthesis source projection images (the green boundaries represent the dense tissue). (b) A tridimensional likelihood map is constructed from the projection images (an intermediate section is shown). The brightness indicates the probability of being dense tissue. (c) Features are extracted from each tomosynthesis reconstructed section (color shows intensity), and (d) the 3D fibroglandular tissue is segmented on the basis of multifeature support vector machine classification.

Figure 1d:

Algorithm outline for VBD estimation from DBT images. The process is performed in four steps: (a) Dense versus nondense tissue segmentation is conducted on tomosynthesis source projection images (the green boundaries represent the dense tissue). (b) A tridimensional likelihood map is constructed from the projection images (an intermediate section is shown). The brightness indicates the probability of being dense tissue. (c) Features are extracted from each tomosynthesis reconstructed section (color shows intensity), and (d) the 3D fibroglandular tissue is segmented on the basis of multifeature support vector machine classification.

Figure 2a:

Representative DBT reconstructed sections for three different women undergoing routine screening show manual segmentation conducted by a human investigator (red) and automated segmentation (green). The women were (a) 69 years of age, (b) 68 years of age, and (c) 36 years of age. An almost perfect match between manual and automated segmentation is shown (yellow).

Figure 2b:

Representative DBT reconstructed sections for three different women undergoing routine screening show manual segmentation conducted by a human investigator (red) and automated segmentation (green). The women were (a) 69 years of age, (b) 68 years of age, and (c) 36 years of age. An almost perfect match between manual and automated segmentation is shown (yellow).

Figure 2c:

Representative DBT reconstructed sections for three different women undergoing routine screening show manual segmentation conducted by a human investigator (red) and automated segmentation (green). The women were (a) 69 years of age, (b) 68 years of age, and (c) 36 years of age. An almost perfect match between manual and automated segmentation is shown (yellow).

Figure 3a:

Scatterplots for VBD estimates (a) per breast and (b) per side. Linear regression lines, along with related r values, show high agreement. CC = craniocaudal, MLO = mediolateral oblique.

Figure 3b:

Scatterplots for VBD estimates (a) per breast and (b) per side. Linear regression lines, along with related r values, show high agreement. CC = craniocaudal, MLO = mediolateral oblique.

Figure 4a:

Box plots used to compare VBD estimates from FFDM, MR imaging, and DBT images. (a) VBD estimates from FFDM are significantly different than those from DBT and MR imaging. Estimates from MR imaging and DBT are not significantly different. (b) Total breast volume estimates are not significantly different. (c) Absolute fibroglandular tissue volume estimates from FFDM are significantly different than those from DBT and MR imaging. Estimates from MR imaging and DBT are not significantly different. FGT = fibroglandular tissue volume.

Figure 4b:

Box plots used to compare VBD estimates from FFDM, MR imaging, and DBT images. (a) VBD estimates from FFDM are significantly different than those from DBT and MR imaging. Estimates from MR imaging and DBT are not significantly different. (b) Total breast volume estimates are not significantly different. (c) Absolute fibroglandular tissue volume estimates from FFDM are significantly different than those from DBT and MR imaging. Estimates from MR imaging and DBT are not significantly different. FGT = fibroglandular tissue volume.

Figure 4c:

Box plots used to compare VBD estimates from FFDM, MR imaging, and DBT images. (a) VBD estimates from FFDM are significantly different than those from DBT and MR imaging. Estimates from MR imaging and DBT are not significantly different. (b) Total breast volume estimates are not significantly different. (c) Absolute fibroglandular tissue volume estimates from FFDM are significantly different than those from DBT and MR imaging. Estimates from MR imaging and DBT are not significantly different. FGT = fibroglandular tissue volume.

Figure 5a:

Scatterplots show strong associations between the VBD estimates obtained from FFDM, MR imaging, and DBT images. (a) FFDM versus MR imaging, (b) FFDM versus DBT, (c) and MR imaging versus DBT results are shown. Linear regression lines, along with the related r values, are also demonstrated.

Figure 5b:

Scatterplots show strong associations between the VBD estimates obtained from FFDM, MR imaging, and DBT images. (a) FFDM versus MR imaging, (b) FFDM versus DBT, (c) and MR imaging versus DBT results are shown. Linear regression lines, along with the related r values, are also demonstrated.

Figure 5c:

Scatterplots show strong associations between the VBD estimates obtained from FFDM, MR imaging, and DBT images. (a) FFDM versus MR imaging, (b) FFDM versus DBT, (c) and MR imaging versus DBT results are shown. Linear regression lines, along with the related r values, are also demonstrated.