Keynote Lecture — Breast Cancer

Interpretation of radiological (and other complex medical) images is challenging due to an often overwhelming amount of visual data, much of which is of no consequence and which often serves to confound the observer. In addition, there are well-recognized physiologic obstacles to visual search and recognition, which contribute to false-negative radiological interpretations. The interpretation of screening mammograms is particularly challenging, because a large number of cases are viewed to detect a small number of cancers (3–10 cancers per 1000 women screened), which are often manifest by subtle alterations superimposed upon the complex radiographic structure of the breast. To overcome known limitations of human observers, second (or double) readings of screening mammograms have been implemented in some practices, which yields a 4–15% increase in cancers detected. For two decades, researchers have investigated the use of computers to analyse mammograms to provide prompts to the radiologist to minimize oversight errors (observational lapses). In 1998, the FDA approved the use of a computer aided detection (CAD) system for screening mammography. This system is specifically designed to minimize the possibility of oversights leading to falsenegative interpretations by pointing out areas on the mammograms that have features suggestive of cancer. The system digitizes the mammogram at 50-micron spatial resolution and 12 bits of gray scale, with excellent linearity and low noise over the optical density range of interest. The digitized image is analysed by proprietary signal processing software, which highlight ROIs (regions of interest) with the following characteristics: