Although multiple randomized clinical trials have demonstrated mortality benefit from routine mammography, population-based breast cancer screening continues to be a highly contentious issue. Digital mammography is the most prevalent breast cancer screening tool, but it is imperfect, with a sensitivity of 84% for detecting breast cancers present at the time of screening. The other 16% are not detected owing to a combination of factors, most notably the human limitation of what radiologists are visually able to identify on mammographic images.1 One in 10 women who undergo mammography screening experience screening failures including false-positive examination results that may lead to unnecessary anxiety, biopsies, surgical excision, and treatment. More recently, critics of screening have suggested that a considerable proportion of screen-detected breast cancers constitute overdiagnosis or cases that would not have become clinically relevant during women’s lifetimes.2 These collective harms are felt to outweigh mortality benefits among certain subpopulations of women, especially younger women.
Trister AD, Buist DSM, Lee CI. Will Machine Learning Tip the Balance in Breast Cancer Screening? JAMA Oncol. 2017;3(11):1463–1464. doi:10.1001/jamaoncol.2017.0473
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