International evaluation of an AI system for breast cancer screening
International evaluation of an AI system for breast cancer screening
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DOI：
10.1038/s41586-019-1799-6
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发表时间：
2020-01-02
期刊：
影响因子：
64.8
通讯作者：
Shetty, Shravya
中科院分区：
文献类型：
--
作者：
McKinney, Scott Mayer;Sieniek, Marcin;Shetty, Shravya
筛查乳房X光检查旨在在疾病的早期阶段识别乳腺癌，此时治疗可以更成功（1）。尽管世界各地都有筛查计划，但乳腺X线照片的解释仍受到高假阳性和假阴性率的影响（2）。在这里，我们提出了一个人工智能（AI）系统，它能够在乳腺癌预测方面超越人类专家。为了评估其在临床环境中的性能，我们策划了来自英国的大型代表性数据集和来自美国的大型丰富数据集。我们显示假阳性的绝对减少率为5.7%和1.2%（美国和英国），假阴性的绝对减少率为9.4%和2.7%。我们提供证据的能力，该系统从英国推广到美国。在一项由6名放射科医生参与的独立研究中，AI系统的表现优于所有人类阅片者：AI系统的受试者工作特征曲线下面积（AUC-ROC）比普通放射科医生的AUC-ROC大11.5%。我们运行了一个模拟，其中人工智能系统参与了英国使用的双重阅读过程，并发现人工智能系统保持了非劣效的性能，并将第二个阅读器的工作量减少了88%。这种对人工智能系统的强大评估为临床试验铺平了道路，以提高乳腺癌筛查的准确性和效率。
Screening mammography aims to identify breast cancer at earlier stages of the disease, when treatment can be more successful(1). Despite the existence of screening programmes worldwide, the interpretation of mammograms is affected by high rates of false positives and false negatives(2). Here we present an artificial intelligence (AI) system that is capable of surpassing human experts in breast cancer prediction. To assess its performance in the clinical setting, we curated a large representative dataset from the UK and a large enriched dataset from the USA. We show an absolute reduction of 5.7% and 1.2% (USA and UK) in false positives and 9.4% and 2.7% in false negatives. We provide evidence of the ability of the system to generalize from the UK to the USA. In an independent study of six radiologists, the AI system outperformed all of the human readers: the area under the receiver operating characteristic curve (AUC-ROC) for the AI system was greater than the AUC-ROC for the average radiologist by an absolute margin of 11.5%. We ran a simulation in which the AI system participated in the double-reading process that is used in the UK, and found that the AI system maintained non-inferior performance and reduced the workload of the second reader by 88%. This robust assessment of the AI system paves the way for clinical trials to improve the accuracy and efficiency of breast cancer screening.