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Will Machine Learning Tip the Balance in Breast Cancer Screening?
JAMA Oncology ( IF 28.4 ) Pub Date : 2017-11-01 , DOI: 10.1001/jamaoncol.2017.0473
Andrew D Trister 1, 2 , Diana S M Buist 3 , Christoph I Lee 4
Affiliation  

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.



中文翻译:

机器学习会改变乳腺癌筛查的平衡吗?

尽管多项随机临床试验已证明常规乳房 X 光检查对死亡率有益,但基于人群的乳腺癌筛查仍然是一个备受争议的问题。数字乳房 X 线照相术是最流行的乳腺癌筛查工具,但它并不完美,在筛查时检测出乳腺癌的敏感性为 84%。其他 16% 未检测到是由于多种因素的结合,最明显的是放射科医师在乳房 X 光图像上视觉识别的人为限制。1每 10 名接受乳房 X 光检查的女性中就有 1 名经历过筛查失败,包括可能导致不必要的焦虑、活检、手术切除和治疗的假阳性检查结果。最近,筛查的批评者认为,相当一部分筛查发现的乳腺癌构成了过度诊断或在女性一生中不会成为临床相关的病例。2在某些女性亚群,尤其是年轻女性中,这些集体危害被认为超过了死亡率益处。

更新日期:2017-11-10
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