Mammography reading with computer-aided detection (CAD): performance of different readers

Computer-aided detection (CAD) systems place prompts in digital images to attract readers' attention to potential malignancies. A reader must then decide whether or not prompted regions correspond to genuine abnormalities and has the option of disregarding falsely prompted regions. In this paper we investigate different readers' performance with CAD in the context of breast screening. In a retrospective study, eight consultant radiologists each read over 1000 screening mammograms comprising normal cases, screen detected cancer cases and cases that were detected as cancers subsequently. We present their results in terms of cancer detection and recall rates, and relate this to their previous experience of film reading. Our results show that the detection of cancers did not differ significantly between readers, although more experienced film readers were less likely to recommend that normal cases should be recalled.