|
乳腺影像筛查目的在于发现早期乳腺癌，此时治疗成功率较高。虽然世界各地都有筛查方案，但是都受到了乳腺影像读片假阳性率和假阴性率较高的影响。为此，美国谷歌开发了一种人工智能系统，其对乳腺癌的预测能力可以超越人类专家。 2020年1月1日，全球自然科学三大旗舰期刊之一、英国《自然》正刊发表美国谷歌、芝加哥西北大学纪念医院、斯坦福大学医疗中心、帕罗奥图退伍军人医疗中心、英国谷歌、伦敦大学帝国学院、剑桥大学、皇家萨里郡医院、皇家马斯登医院、蒂莱斯坦乳腺中心的研究报告，评估了谷歌人工智能系统筛查乳腺癌的临床实际效果。 该国际多中心研究选择2012～2015年英国2万5856例两次读片和2001～2018年美国3097例单次读片的乳腺影像筛查数据，对谷歌人工智能系统筛查乳腺癌的临床效果进行评定。 结果发现，谷歌人工智能系统与人类专家相比：
该研究提供了能够将该系统从英国推广到美国的证据。该研究对6位放射科医师进行单独分析，谷歌人工智能系统的读片能力高于所有放射科医师，受试者操作特征曲线下面积绝对值高11.5%。 根据模拟分析，谷歌人工智能系统可以将第二次读片的工作量减少88%。 因此，该强有力的研究结果，为人工智能系统的乳腺癌筛查临床研究铺平了道路，有望大大提高乳腺癌筛查的精度和速度。 对此，美国放射学会首席研究官、哈佛大学医学院教授、贝斯以色列和新英格兰女执事医疗中心埃塔·皮萨诺发表同期报道：乳腺癌人工智能筛查令人鼓舞。 Nature. 2020 Jan 1;577(7788):89-94. International evaluation of an AI system for breast cancer screening. Scott Mayer McKinney, Marcin Sieniek, Varun Godbole, Jonathan Godwin, Natasha Antropova, Hutan Ashrafian, Trevor Back, Mary Chesus, Greg C. Corrado, Ara Darzi, Mozziyar Etemadi, Florencia Garcia-Vicente, Fiona J. Gilbert, Mark Halling-Brown, Demis Hassabis, Sunny Jansen, Alan Karthikesalingam, Christopher J. Kelly, Dominic King, Joseph R. Ledsam, David Melnick, Hormuz Mostofi, Lily Peng, Joshua Jay Reicher, Bernardino Romera-Paredes, Richard Sidebottom, Mustafa Suleyman, Daniel Tse, Kenneth C. Young, Jeffrey De Fauw, Shravya Shetty. Google Health, Palo Alto, CA, USA; DeepMind, London, UK; Imperial College London, London, UK; Northwestern Medicine, Chicago, IL, USA; University of Cambridge, Cambridge, UK; Royal Surrey County Hospital, Guildford, UK; Verily Life Sciences, South San Francisco, CA, USA; Google Health, London, UK; Stanford Health Care and Palo Alto Veterans Affairs, Palo Alto, CA, USA; The Royal Marsden Hospital, London, UK; Thirlestaine Breast Centre, Cheltenham, UK. Screening mammography aims to identify breast cancer at earlier stages of the disease, when treatment can be more successful1. Despite the existence of screening programmes worldwide, the interpretation of mammograms is affected by high rates of false positives and false negatives2. 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. DOI: 10.1038/s41586-019-1799-6 Nature. 2020 Jan 1;577(7788):35-36. AI shows promise for breast cancer screening. Etta D. Pisano. American College of Radiology, Philadelphia, Pennsylvania, USA; Beth Israel Lahey Medical Center, Harvard Medical School, Boston, Massachusetts. DOI: 10.1038/d41586-019-03822-8
|
|
|
