Automated percentage of breast density measurements for full-field digital mammography applications

Rationale and Objectives: Increased mammographic breast density is a significant risk factor for breast cancer. A reproducible, accurate, and automated breast density measurement is required for full-field digital mammography (FFDM) to support clinical applications. We evaluated a novel automated percentage of breast density measure (PD_a) and made comparisons with the standard operator-assisted measure (PD) using FFDM data. Methods: We used a nested breast cancer case-control study matched on age, year of mammogram and diagnosis with images acquired from a specific direct x-ray conversion FFDM technology. PD_a was applied to the raw and clinical display (or processed) representation images. We evaluated the transformation (pixel mapping) of the raw image, giving a third representation (raw-transformed), to improve the PD_a performance using differential evolution optimization. We applied PD to the raw and clinical display images as a standard for measurement comparison. Conditional logistic regression was used to estimate the odd ratios (ORs) for breast cancer with 95% confidence intervals (CI) for all measurements; analyses were adjusted for body mass index. PD_a operates by evaluating signal-dependent noise (SDN), captured as local signal variation. Therefore, we characterized the SDN relationship to understand the PD_a performance as a function of data representation and investigated a variation analysis of the transformation. Results: The associations of the quartiles of operator-assisted PD with breast cancer were similar for the raw (OR: 1.00 [ref.]; 1.59 [95% CI, 0.93-2.70]; 1.70 [95% CI, 0.95-3.04]; 2.04 [95% CI, 1.13-3.67]) and clinical display (OR: 1.00 [ref.]; 1.31 [95% CI, 0.79-2.18]; 1.14 [95% CI, 0.65-1.98]; 1.95 [95% CI, 1.09-3.47]) images. PD_a could not be assessed on the raw images without preprocessing. However, PD_a had similar associations with breast cancer when assessed on 1) raw-transformed (OR: 1.00 [ref.]; 1.27 [95% CI, 0.74-2.19]; 1.86 [95% CI, 1.05-3.28]; 3.00 [95% CI, 1.67-5.38]) and 2) clinical display (OR: 1.00 [ref.]; 1.79 [95% CI, 1.04-3.11]; 1.61 [95% CI, 0.90-2.88]; 2.94 [95% CI, 1.66-5.19]) images. The SDN analysis showed that a nonlinear relationship between the mammographic signal and its variation (ie, the biomarker for the breast density) is required for PD_a. Although variability in the transform influenced the respective PD_a distribution, it did not affect the measurement's association with breast cancer. Conclusions: PD_a assessed on either raw-transformed or clinical display images is a valid automated breast density measurement for a specific FFDM technology and compares well against PD. Further work is required for measurement generalization.