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November 2017

Will Machine Learning Tip the Balance in Breast Cancer Screening?

Author Affiliations
  • 1Department of Radiation Medicine, Oregon Health and Science University, Portland
  • 2Sage Bionetworks, Seattle, Washington
  • 3Kaiser Permanente Washington Health Research Institute, Seattle, Washington
  • 4Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson Cancer Research Center, Department of Radiology, University of Washington School of Medicine, Seattle
JAMA Oncol. 2017;3(11):1463-1464. doi:10.1001/jamaoncol.2017.0473

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.

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