Breast Density Imaging Research

New method to assess breast density software

Here, Professor Peter Hogg tells us about some exciting developments in the Breast Imaging Research theme.

Breast Density

Breast density is a major risk factor for developing breast cancer; the higher the density the more likely the chance of developing breast cancer. An increasing number of mammography breast screening units are assessing breast density, visually or by using software, with a view to stratifying women into different risk categories: high risk / high density would be screened more frequently.

Surprisingly no physical phantom exists to assess the reliability of mammography software to determine breast density, instead software is assessed mathematically. A physical phantom should attempt to replicate key characteristics of the female breast, being deformable and comprising of two quite different density structures (glandular and fatty tissues).

New work at the University of Salford, in collaboration with partners in New Zealand, Norway and Morecambe Bay NHS Foundation Trust have developed and tested a novel deformable phantom to assess the reliability of breast density assessment software. The results are promising though much more work is needed. Information about the article is given below:

Development of a phantom to test fully automated breast density software work in progress, G.G. Waade, S. Hofvind, J.D. Thompson, R. Highnam, P. Hogg Radiography, 2016,


Side view photo of the deformable breast phantom

Objectives: Mammographic density (MD) is an independent risk factor for breast cancer and may have a future role for stratified screening. Automated software can estimate MD but the relationship between breast thickness reduction and MD is not fully understood. Our aim is to develop a deformable breast phantom to assess automated density software and the impact of breast thickness reduction on MD. Methods: Several different configurations of poly vinyl alcohol (PVAL) phantoms were created. Three methods were used to estimate their density. Raw image data of mammographic images were processed using Volpara to estimate volumetric breast density (VBD%); Hounsfield units (HU) were measured on CT images; and physical density (g/cm3) was calculated using a formula involving mass and volume. Phantom volume versus contact area and phantom volume versus phantom thickness was compared to values of real breasts.

Results: Volpara recognized all deformable phantoms as female breasts. However, reducing the phantom thickness caused a change in phantom density and the phantoms were not able to tolerate same level of compression and thickness reduction experienced by female breasts during mammography. Conclusion: Our results are promising as all phantoms resulted in valid data for automated breast density measurement. Further work should be conducted on PVAL and other materials to produce deformable phantoms that mimic female breast structure and density with the ability of being compressed to the same level as female breasts.

Advances in knowledge: We are the first group to have produced deformable phantoms that are recognized as breasts by Volpara software.


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