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Radiology. 2019 Jan;290(1):218-228. doi: 10.1148/radiol.2018180237. Epub 2018 Sep 25.

Development and Validation of Deep Learning-based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs.

Author information

1
From the Department of Radiology and Institute of Radiation Medicine, Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.G.N., E.J.H., J.M.G., C.M.P.); Lunit Incorporated, Seoul, Republic of Korea (S.P.); Department of Radiology, Armed Forces Seoul Hospital, Seoul, Republic of Korea (J.H.L.); Department of Radiology, Seoul National University Boramae Medical Center, Seoul, Republic of Korea (K.N.J.); Department of Radiology, National Cancer Center, Goyang, Republic of Korea (K.Y.L.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (T.H.V., J.H.S.); and Department of Industrial & Information Systems Engineering, Seoul National University of Science and Technology, Seoul, Republic of Korea (S.H.).

Abstract

Purpose To develop and validate a deep learning-based automatic detection algorithm (DLAD) for malignant pulmonary nodules on chest radiographs and to compare its performance with physicians including thoracic radiologists. Materials and Methods For this retrospective study, DLAD was developed by using 43 292 chest radiographs (normal radiograph-to-nodule radiograph ratio, 34 067:9225) in 34 676 patients (healthy-to-nodule ratio, 30 784:3892; 19 230 men [mean age, 52.8 years; age range, 18-99 years]; 15 446 women [mean age, 52.3 years; age range, 18-98 years]) obtained between 2010 and 2015, which were labeled and partially annotated by 13 board-certified radiologists, in a convolutional neural network. Radiograph classification and nodule detection performances of DLAD were validated by using one internal and four external data sets from three South Korean hospitals and one U.S. hospital. For internal and external validation, radiograph classification and nodule detection performances of DLAD were evaluated by using the area under the receiver operating characteristic curve (AUROC) and jackknife alternative free-response receiver-operating characteristic (JAFROC) figure of merit (FOM), respectively. An observer performance test involving 18 physicians, including nine board-certified radiologists, was conducted by using one of the four external validation data sets. Performances of DLAD, physicians, and physicians assisted with DLAD were evaluated and compared. Results According to one internal and four external validation data sets, radiograph classification and nodule detection performances of DLAD were a range of 0.92-0.99 (AUROC) and 0.831-0.924 (JAFROC FOM), respectively. DLAD showed a higher AUROC and JAFROC FOM at the observer performance test than 17 of 18 and 15 of 18 physicians, respectively (P < .05), and all physicians showed improved nodule detection performances with DLAD (mean JAFROC FOM improvement, 0.043; range, 0.006-0.190; P < .05). Conclusion This deep learning-based automatic detection algorithm outperformed physicians in radiograph classification and nodule detection performance for malignant pulmonary nodules on chest radiographs, and it enhanced physicians' performances when used as a second reader.

PMID:
30251934
DOI:
10.1148/radiol.2018180237
[Indexed for MEDLINE]

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