Volumetric Breast Density Estimation From Three-Dimensional Reconstructed Digital Breast Tomosynthesis Images Using Deep Learning

Abstract

Purpose: Breast density is a widely established independent breast cancer risk factor. With the increasing utilization of digital breast tomosynthesis (DBT) in breast cancer screening, there is an opportunity to estimate volumetric breast density (VBD) routinely. However, current available methods extrapolate VBD from two-dimensional (2D) images acquired using DBT and/or depend on the existence of raw DBT data, which is rarely archived by clinical centers because of storage constraints.

Methods: We retrospectively analyzed 1,080 nonactionable three-dimensional (3D) reconstructed DBT screening examinations acquired between 2011 and 2016. Reference tissue segmentations were generated using previously validated software that uses 3D reconstructed slices and raw 2D DBT data. We developed a deep learning (DL) model that segments dense and fatty breast tissue from background. We then applied this model to estimate %VBD and absolute dense volume (ADV) in cm³ in a separate case-control sample (180 cases and 654 controls). We created two conditional logistic regression models, relating each model-derived density measurement to likelihood of contralateral breast cancer diagnosis, adjusted for age, BMI, family history, and menopausal status.

Results: The DL model achieved unweighted and weighted Dice scores of 0.88 (standard deviation [SD] = 0.08) and 0.76 (SD = 0.15), respectively, on the held-out test set, demonstrating good agreement between the model and 3D reference segmentations. There was a significant association between the odds of breast cancer diagnosis and model-derived VBD (odds ratio [OR], 1.41 [95 % CI, 1.13 to 1.77]; P = .002), with an AUC of 0.65 (95% CI, 0.60 to 0.69). ADV was also significantly associated with breast cancer diagnosis (OR, 1.45 [95% CI, 1.22 to 1.73]; P < .001) with an AUC of 0.67 (95% CI, 0.62 to 0.71).

Conclusion: DL-derived density measures derived from 3D reconstructed DBT images are associated with breast cancer diagnosis.

FIG 1.

Schematic representing the methodology for reference tissue segmentation construction, DL model training, and DL model evaluation. 2D, two-dimensional; 3D, three-dimensional; CNN, convolutional neural network; DL, deep learning; DM-DBT, digital mammography-digital breast tomosynthesis; VBD, volumetric breast density.

FIG 2.

DBT 3D reconstructed slices (top row), corresponding reference segmentation (middle row), and DL-derived segmentation predictions (bottom row) from CC and MLO views in the test set. Black = background; gray = fatty breast tissue; white = dense breast tissue. CC, craniocaudal; DBT, digital breast tomosynthesis; DL, deep learning; MLO, mediolateral oblique.

FIG 3.

Delineation of DL-derived %VBD by radiologist-determined BI-RADS density category on the case-control set. One-way ANOVA analysis comparing mean DL-derived %VBD for each BI-RADS category was significant after Bonferroni correction for multiple comparisons (P < .001). The mean DL-derived %VBD was 7.1%, 10.9%, 21.6%, and 34.6% for BI-RADS density categories A (n = 101), B (n = 475), C (n = 248), and D (n = 10), respectively. ANOVA, analysis of variance; BI-RADS, Breast Imaging-Reporting and Data System; DL, deep learning; VBD, volumetric breast density.

FIG A1.

Scatter plot illustrating the relationship between (A) DL-derived VBD and reference VBD in the test set when stratifying by image view. CC, craniocaudal; DL, deep learning; MLO, mediolateral oblique; VBD, volumetric breast density.

FIG A2.

Inclusion and exclusion criteria for case-control sample selection. BI-RADS, Breast Imaging-Reporting and Data System; DM-DBT, digital mammography-digital breast tomosynthesis.

FIG A3.

Distribution of DL-derived ADV and %VBD on the case-control data set. (A) ADV for the entire case-control cohort; (B) %VBD for the entire case-control cohort; (C) ADV for the entire case-control cohort with cases and controls separately overlaid; (D) %VBD for the entire case-control cohort with cases and controls separately overlaid. ADV, absolute dense volume; DL, deep learning; VBD, volumetric breast density.