A practical work around for breast density distribution discrepancies between mammographic images from different vendors

Abstract

Objectives: Investigate the impact of mammography device grouped by vendor on volumetric breast density and propose a method that mitigates biases when determining the proportion of high-density women.

Materials and methods: Density grade class and volumetric breast density distributions were obtained from mammographic images from three different vendor devices in different centers using breast density evaluation software in a retrospective study. Density distributions were compared across devices with a Mann-Whitney U test and breast density thresholds corresponding to distribution percentiles calculated. A method of matching density percentiles is proposed to determine women at potentially high risk while mitigating possible bias due to the device used for screening.

Results: 2083 (mean age 59 ± 5.4), 531 (mean age 58.8 ± 5.7) and 244 (mean age 60.7 ± 6.0) screened women were evaluated on three vendor devices, respectively. Both the density grade distribution and the volumetric breast density were different between Vendor 1 and Vendor 2 data (p < 0.001) and between Vendor 1 and Vendor 3 data (p < 0.001). Between Vendor 2 and Vendor 3, no significant difference was observed (p = 0.67 for density grade, p = 0.29 for volumetric density). To recruit the top 10% of women with extremely dense breasts required respective density thresholds of 16.1%, 13.6% and 13.8% for the three vendor devices.

Conclusion: Density grade class and volumetric breast density distributions differ between devices grouped by vendor and can result in statistically different breast density distributions. Percentile-dependent density thresholds can ensure unbiased selection of high-risk women.

Key points: Question Does the use of x-ray systems from different vendors influence breast density evaluation and the resulting selection of high-risk women during breast cancer screening? Findings Statistically significant differences were observed between breast density distributions of different vendors; a method of matching via percentiles is proposed to prevent biased density evaluations. Clinical relevance Measured breast density distributions differed between X-ray devices. A workaround is proposed that determines density thresholds corresponding to a specified population, allowing the same proportion of women to be selected with a density algorithm.

Keywords: Breast density; Breast neoplasms; Early detection of cancer; Mammography.

Fig. 1

Flowchart of the data exclusion process. Diagnostic exams as well as exams with missing image data were excluded from the study

Fig. 2

Breast density distributions for the different vendors. The Volpara Density Grade^TM (VDG) class distribution can be seen on the left, while the volumetric breast density distribution is depicted on the right

Fig. 3

Normalized volumetric breast density distribution for each Volpara Density Grade^TM class for the different vendors. The values are normalized for each VDG class. The horizontal bars depict the range of volumetric densities that 95% of values fall into for each class

Fig. 4

Quantile-quantile plots for all vendor pairs. Distributions are matched by percentiles and plotted against each other. The equality line (dotted) represents equal percentiles. For low volumetric breast density values, a linear fit (dashed) can be used to describe the data