Abstract
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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 89-94 |
| Number of pages | 6 |
| Journal | Nature |
| Volume | 577 |
| Issue number | 7788 |
| DOIs | |
| State | Published - Jan 2 2020 |
Funding
Competing interests This study was funded by Google LLC and/or a subsidiary thereof (‘Google’). S.M.M., M. Sieniek, V.G., J.G., N.A., T.B., M.C., G.C.C., D.H., S.J., A.K., C.J.K., D.K., J.R.L., H.M., B.R.-P., L.P., M. Suleyman, D.T., J.D.F. and S.S. are employees of Google and own stock as part of the standard compensation package. J.J.R., R.S., F.J.G. and A.D. are paid consultants of Google. M.E., F.G.-V., D.M., K.C.Y. and M.H.-B received funding from Google to support the research collaboration. Acknowledgements We would like to acknowledge multiple contributors to this international project: Cancer Research UK, the OPTIMAM project team and staff at the Royal Surrey County Hospital who developed the UK mammography imaging database; S. Tymms and S. Steer for providing patient perspectives; R. Wilson for providing a clinical perspective; all members of the Etemadi Research Group for their efforts in data aggregation and de-identification; and members of the Northwestern Medicine leadership, without whom this work would not have been possible (M. Schumacher, C. Christensen, D. King and C. Hogue). We also thank everyone at NMIT for their efforts, including M. Lombardi, D. Fridi, P. Lendman, B. Slavicek, S. Xinos, B. Milfajt and others; V. Cornelius, who provided advice on statistical planning; R. West and T. Saensuksopa for assistance with data visualization; A. Eslami and O. Ronneberger for expertise in machine learning; H. Forbes and C. Zaleski for assistance with project management; J. Wong and F. Tan for coordinating labelling resources; R. Ahmed, R. Pilgrim, A. Phalen and M. Bawn for work on partnership formation; R. Eng, V. Dhir and R. Shah for data annotation and interpretation; C. Chen for critically reading the manuscript; D. Ardila for infrastructure development; C. Hughes and D. Moitinho de Almeida for early engineering work; and J. Yoshimi, X. Ji, W. Chen, T. Daly, H. Doan, E. Lindley and Q. Duong for development of the labelling infrastructure. A.D. and F.J.G. receive funding from the National Institute for Health Research (Senior Investigator award). Infrastructure support for this research was provided by the NIHR Imperial Biomedical Research Centre (BRC). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.
ASJC Scopus subject areas
- General
