End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography
| العنوان: | End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography |
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| المؤلفون: | Shravya Shetty, Joshua J. Reicher, Lily Peng, Daniel Tse, David P. Naidich, Mozziyar Etemadi, Atilla Peter Kiraly, Diego Ardila, Bokyung Choi, Greg S. Corrado, Sujeeth Bharadwaj, Wenxing Ye |
| المصدر: | Nature Medicine. 25:954-961 |
| بيانات النشر: | Springer Science and Business Media LLC, 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | 0301 basic medicine, medicine.medical_specialty, medicine.diagnostic_test, business.industry, Deep learning, Computed tomography, Retrospective cohort study, General Medicine, medicine.disease, Malignancy, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, 030104 developmental biology, 0302 clinical medicine, 030220 oncology & carcinogenesis, medicine, False positive paradox, Tomography, Artificial intelligence, Radiology, business, Lung cancer, Lung cancer screening |
| الوصف: | With an estimated 160,000 deaths in 2018, lung cancer is the most common cause of cancer death in the United States1. Lung cancer screening using low-dose computed tomography has been shown to reduce mortality by 20–43% and is now included in US screening guidelines1–6. Existing challenges include inter-grader variability and high false-positive and false-negative rates7–10. 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. A convolutional neural network performs automated prediction of malignancy risk of pulmonary nodules in chest CT scan volumes and improves accuracy of lung cancer screening. |
| تدمد: | 1546-170X 1078-8956 |
| DOI: | 10.1038/s41591-019-0447-x |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::933e6ef163f02e37f0227e7f5a5f969b https://doi.org/10.1038/s41591-019-0447-x |
| Rights: | CLOSED |
| رقم الانضمام: | edsair.doi...........933e6ef163f02e37f0227e7f5a5f969b |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 1546170X 10788956 |
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| DOI: | 10.1038/s41591-019-0447-x |