Bruce F. Schroeder MD, Presenter: Research Consultant, General Electric Company Research Consultant, Koning Corporation

Ralph Philip Highnam PhD, Abstract Co-Author: CEO, Matakina Technology, Ltd CEO, Matakina International, Ltd

Andrew Cave BEng, Abstract Co-Author: Nothing to Disclose

Jenny Walker, Abstract Co-Author: Nothing to Disclose

Nico Karssemeijer PhD, Abstract Co-Author: Shareholder, Matakina Technology Scientific Board, Matakina Technology Shareholder, QView Medical Research grant, MeVis Medical Solutions AG Research grant, Riverain Medical

Martin J. Yaffe PhD, Abstract Co-Author: Research collaboration, General Electric Company Scientific Advisory Board, ART Advanced Research Technologies Inc Founder, Matakina Technology Ltd Shareholder, Matakina Technology Ltd

Roberta A. Jong MD, Abstract Co-Author: Research Consultant, General Electric Company

Olivier Alonzo-Proulx, Abstract Co-Author: Nothing to Disclose

Ages for breast screening are set so as to optimize early breast cancer detection while minimizing costs and risk. In this study, we test the correlation of age to breast density to ascertain if there is a certain age when density change is greatest and thus where x-ray sensitivity improves markedly.

Volumetric breast density was calculated on digital mammograms of 69,000 women using Volpara, an FDA cleared, and fully automated breast density assessment tool for use on digital mammograms. The digital mammograms consisted of: • 15,000 women (aged 27-99) imaged on GE equipment in Toronto (screening and diagnosis) • 50,000 women (aged 50-70) imaged on Hologic equipment in Utrecht (screening) • 1,500 women (aged 45-70) imaged on Siemens equipment in Auckland (screening) • 400 women (aged 40-76) imaged on GE equipment in Greenville, North Carolina (screening) From the individual results for each woman, we then computed average breast density and inter-quartile range at each age and for each geographical location.  

The overall level of breast density differed between sites, most likely related to population. Greenville, for example, had 20% higher breast volumes, although it is known that the algorithm can over-estimate density when a tilted paddle is used such as in Utrecht. Previous results have shown Volpara producing similar results for the same breast imaged on Hologic and GE systems with standard paddle. Breast density decreased from 18% to 8% at Toronto (age 27 to 75), 14% to 7% at Utrecht and Auckland (50 to 75), and 10% to 5% in Greenville (40 to 75). At all sites density dimishes and then plateaus.  Interestingly, the rates of breast density decrease appear similar across sites but there is no apparent abrupt change in density at any age. For all sites, at any given age there was considerable variation in breast density. For example, the inter-quartile ranges for the Toronto data were 13-23% at age 30, 8-18% at age 40, 7-17% at age 50, 4-12% at age 60 and 4-9% at age 70.    

Screening based purely on age may deny access to women who have less dense breasts and in whom x-ray sensitivity is high. Personalized risk/benefit analyses, including breast density assessment, may provide more suitable guidance for appropriate utilization of medical resources.

Breast screening should be offered on the basis of breast density and risk factors, not just age.

Schroeder, B, Highnam, R, Cave, A, Walker, J, Karssemeijer, N, Yaffe, M, Jong, R, Alonzo-Proulx, O, At What Age Should Breast Screening Begin?.  Radiological Society of North America 2011 Scientific Assembly and Annual Meeting, November 26 - December 2, 2011 ,Chicago IL. http://archive.rsna.org/2011/11004540.html