Early detection of breast cancer: overview of the evidence on computer-aided detection in mammography screening.
We review the evidence on computer-aided detection (CAD) as an adjunct to mammography interpretation, and discuss the complexity of its impact on decision-making and potential medico-legal aspects. CAD prompts the reader to review lesions on the mammogram and re-evaluate the decision on whether to recall CAD-prompted findings. Studies show that CAD can improve the sensitivity of a single reader, with an incremental cancer detection rate (from adding CAD to a single read) ranging between 1 and 19%. However, CAD will also substantially increase the recall rate (decrease the reader's specificity) causing additional recall in approximately 6-35% of women. Evidence indicates that CAD does not perform as well as double (human) reading in the context of organized breast screening where double reading is the standard of care. Although CAD can identify and prompt readers to missed cancers, the high number of false-positive prompts (1.5-4 false prompts per case) can have an adverse effect on clinical decision-making, and detracts from CAD's application in screening practice. Refinement in CAD algorithms, in combination with increasing implementation of digital mammography, may improve the potential use of CAD in mammography reading, but will require prospective evaluation.
Houssami
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Screening and Test Evaluation Program, School of Public Health, University of Sydney, Sydney, NSW 2006, Australia. nehmath@med.usyd.edu.au
Given-Wilson
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Ciatto
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D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D019317:Evidence-Based Medicine; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D008403:Mass Screening / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*