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In order to assess density using fully automated methods, the raw FFDM (for processing) image data from GE Senographe Essential mammography systems was obtained. Cancers (invasive and ductal carcinoma in situ) were identified through hospital records or through the North West Cancer Intelligence Service; women who moved out the area were considered ineligible. Two case-control datasets were created. In study 1, cases were women with breast cancer detected at the screen on entry to PROCAS and in study 2 cases were women who were breast cancer free at the screen on entry to PROCAS but had breast cancer detected subsequently, either between screening rounds or at a later screen. In these women we analysed the density of the screen on entry to PROCAS.
Study 2 examined the relationship between mammographic density in mammograms prior to the detection of cancer, and in matched controls that subsequently remained cancer free. This enables us to evaluate which mammographic density methods are most appropriate for stratifying women attending breast screening. Whilst visual assessment was most strongly associated with cancer, it is unlikely to be used widely for population-based stratified screening; we conclude that Volpara or Densitas percentage density provide a pragmatic solution. However, we hypothesise that methods that measure purely the quantity or relative proportion of dense tissue do not fully capture the mammographic risk in the same way as visual assessment by experts, who can see not only the quantity of dense tissue but the location and pattern. The addition of algorithms that automatically quantify mammographic pattern to automated density software could potentially provide a solution that more closely reproduces visual assessment. Recent research in this area has proved promising [33,34,35,36], although there is as yet no consensus as to the best method of encapsulating texture information within risk assessment. 2b1af7f3a8