Development of a phantom to test fully automated breast density software – a work in progress

Wåde, G; Hogg, P; Thompson, JD; Hofvind, S

Abstract

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.

Citation

Wåde, G., Hogg, P., Thompson, J., & Hofvind, S. (2017). Development of a phantom to test fully automated breast density software – a work in progress. Radiography, 23(1), e14-e19. https://doi.org/10.1016/j.radi.2016.09.003

• Journal Article Type: Article
• Acceptance Date: Sep 15, 2016
• Online Publication Date: Oct 3, 2016
• Publication Date: Feb 1, 2017
• Deposit Date: Sep 21, 2016
• Publicly Available Date: Oct 3, 2017
• Journal: Radiography
• Print ISSN: 1078-8174
• Publisher: Elsevier
• Volume: 23
• Issue: 1
• Pages: e14-e19
• DOI: https://doi.org/10.1016/j.radi.2016.09.003
• Publisher URL: http://dx.doi.org/10.1016/j.radi.2016.09.003
• Related Public URLs: http://www.journals.elsevier.com/radiography/