End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography

With an estimated 160,000 deaths in 2018, lung cancer is the most common cause of cancer death in the United States . Lung cancer screening using low-dose computed tomography has been shown to reduce mortality by 20-43% and is now included in US screening guidelines . Existing challenges include int...

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Vydáno v:	Nature medicine Ročník 25; číslo 6; s. 954 - 961
Hlavní autoři:	Ardila, Diego, Kiraly, Atilla P, Bharadwaj, Sujeeth, Choi, Bokyung, Reicher, Joshua J, Peng, Lily, Tse, Daniel, Etemadi, Mozziyar, Ye, Wenxing, Corrado, Greg, Naidich, David P, Shetty, Shravya
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Nature Publishing Group 01.06.2019
Témata:	Algorithms Automation Cancer screening Computation Computed tomography Databases, Factual Deep learning Deep Learning - statistics & numerical data Diagnosis, Computer-Assisted - methods Diagnosis, Computer-Assisted - statistics & numerical data Humans Imaging, Three-Dimensional - statistics & numerical data Lung cancer Lung Neoplasms - diagnosis Lung Neoplasms - diagnostic imaging Mass Screening - methods Mass Screening - statistics & numerical data Medical imaging Medical screening Model accuracy Neural Networks, Computer Retrospective Studies Risk Factors Tomography Tomography, X-Ray Computed - statistics & numerical data United States United States
ISSN:	1078-8956, 1546-170X, 1546-170X
On-line přístup:	Získat plný text
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Popis
Shrnutí:	With an estimated 160,000 deaths in 2018, lung cancer is the most common cause of cancer death in the United States . Lung cancer screening using low-dose computed tomography has been shown to reduce mortality by 20-43% and is now included in US screening guidelines . Existing challenges include inter-grader variability and high false-positive and false-negative rates . We propose a deep learning algorithm that uses a patient's current and prior computed tomography volumes to predict the risk of lung cancer. Our model achieves a state-of-the-art performance (94.4% area under the curve) on 6,716 National Lung Cancer Screening Trial cases, and performs similarly on an independent clinical validation set of 1,139 cases. We conducted two reader studies. When prior computed tomography imaging was not available, our model outperformed all six radiologists with absolute reductions of 11% in false positives and 5% in false negatives. Where prior computed tomography imaging was available, the model performance was on-par with the same radiologists. This creates an opportunity to optimize the screening process via computer assistance and automation. While the vast majority of patients remain unscreened, we show the potential for deep learning models to increase the accuracy, consistency and adoption of lung cancer screening worldwide.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1078-8956 1546-170X 1546-170X
DOI:	10.1038/s41591-019-0447-x