Development and validation of deep learning-based automatic detection algorithm for malignant pulmonary nodules on chest radiographs

Radiology

Volumn 290, Issue 1, 2019, Pages 218-228

  Nam, Ju Gang ^a   Park, Sunggyun ^b   Hwang, Eui Jin ^a   Lee, Jong Hyuk ^c   Jin, Kwang Nam ^d   Lim, Kun Young ^e   Vu, Thienkai Huy ^f   Sohn, Jae Ho ^f   Hwang, Sangheum ^g   Goo, Jin Mo ^a   Park, Chang Min ^a  

^a Seoul National University Hospital   (South Korea)

^b Oncology group   (South Korea)

^c Armed Forces Seoul Hospital   (South Korea)

^d Seoul National University Boramae Hospital   (South Korea)

^e National Cancer Center   (South Korea)

^f UNIVERSITY OF CALIFORNIA   (United States)

^g Seoul National University of Science and Technology   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; AGED; ANALYTIC METHOD; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATION; CLINICAL EVALUATION; DIAGNOSTIC PROCEDURE; DIAGNOSTIC RADIOLOGIST; DISEASE CLASSIFICATION; FEMALE; GENERAL PRACTITIONER; GROUPS BY AGE; HUMAN; LEARNING ALGORITHM; LUNG NODULE; LUNG TUMOR; MAJOR CLINICAL STUDY; MALE; MEDICAL EXPERT; PRIORITY JOURNAL; RETROSPECTIVE STUDY; SOUTH KOREA; THORAX RADIOGRAPHY; UNITED STATES; WORK EXPERIENCE; ADOLESCENT; ALGORITHM; COMPUTER ASSISTED DIAGNOSIS; DIAGNOSTIC IMAGING; MIDDLE AGED; MULTIPLE PULMONARY NODULES; PROCEDURES; REPRODUCIBILITY; VERY ELDERLY; YOUNG ADULT;

ADOLESCENT; ADULT; AGED; AGED, 80 AND OVER; ALGORITHMS; DEEP LEARNING; FEMALE; HUMANS; LUNG NEOPLASMS; MALE; MIDDLE AGED; MULTIPLE PULMONARY NODULES; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; RADIOGRAPHY, THORACIC; REPRODUCIBILITY OF RESULTS; RETROSPECTIVE STUDIES; YOUNG ADULT;

EID: 85057579798     PISSN: 00338419     EISSN: 15271315     Source Type: Journal    
DOI: 10.1148/radiol.2018180237     Document Type: Article

References (28)

1. Schalekamp S, van Ginneken B, Koedam E, et al. Computer-aided detection improves detection of pulmonary nodules in chest radiographs beyond the support by bone-suppressed images. Radiology 2014;272(1):252-261
2. de Hoop B, De Boo DW, Gietema HA, et al. Computer-aided detection of lung cancer on chest radiographs: effect on observer performance. Radiology 2010;257(2): 532-540
3. Li F, Arimura H, Suzuki K, et al. Computer-aided detection of peripheral lung cancers missed at CT: ROC analyses without and with localization. Radiology 2005;237(2):684-690
4. Potchen EJ, Cooper TG, Sierra AE, et al. Measuring performance in chest radiography. Radiology 2000;217(2):456-459
5. Toyoda Y, Nakayama T, Kusunoki Y, Iso H, Suzuki T. Sensitivity and specificity of lung cancer screening using chest low-dose computed tomography. Br J Cancer 2008;98(10):1602-1607
6. Gavelli G, Giampalma E. Sensitivity and specificity of chest X-ray screening for lung cancer: review article. Cancer 2000;89(11 Suppl):2453-2456
7. Austin JH, Romney BM, Goldsmith LS. Missed bronchogenic carcinoma: radiographic findings in 27 patients with a potentially resectable lesion evident in retrospect. Radiology 1992;182(1):115-122
8. Mettler FA Jr, Huda W, Yoshizumi TT, Mahesh M. Effective doses in radiology and diagnostic nuclear medicine: a catalog. Radiology 2008;248(1):254-263
9. Bach PB, Mirkin JN, Oliver TK, et al. Benefits and harms of CT screening for lung cancer: a systematic review. JAMA 2012;307(22):2418-2429
10. Gerritsen MG, Willemink MJ, Pompe E, et al. Improving early diagnosis of pulmonary infections in patients with febrile neutropenia using low-dose chest computed tomography. PLoS One 2017;12(2):e0172256
11. den Harder AM, Willemink MJ, van Hamersvelt RW, et al. Pulmonary nodule volumetry at different low computed tomography radiation dose levels with hybrid and model-based iterative reconstruction: a within patient analysis. J Comput Assist Tomogr 2016;40(4):578-583
12. Schalekamp S, van Ginneken B, Karssemeijer N, Schaefer-Prokop CM. Chest radiography: new technological developments and their applications. Semin Respir Crit Care Med 2014;35(1):3-16
13. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521(7553):436-444
14. Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In: NIPS'12: Proceedings of the 25th International Conference on Neural Information Processing Systems-Volume 1. Red Hook, NY: Curran Associates, 2012; 1097-1105
15. Lecun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE 1998;86(11):2278-2324
16. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016; 770-778
17. Liao S, Gao Y, Oto A, Shen D. Representation learning: a unified deep learning framework for automatic prostate MR segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Berlin, Germany: Springer, 2013; 254-261
18. Kumar D, Wong A, Clausi DA. Lung nodule classification using deep features in CT images. In: 2015 12th Conference on Computer and Robot Vision. LOCATION: IEEE, 2015; 133-138
19. Chakraborty DP. Recent developments in imaging system assessment methodology, FROC analysis and the search model. Nucl Instrum Methods Phys Res A 2011;648(Supplement 1):S297-S301
20. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988;44(3):837-845
21. Fletcher JG, Yu L, Li Z, et al. Observer performance in the detection and classification of malignant hepatic nodules and masses with CT image-space denoising and iterative reconstruction. Radiology 2015;276(2):465-478
22. Bland JM, Altman DG. Multiple significance tests: the Bonferroni method. BMJ 1995;310(6973):170
23. Novak RD, Novak NJ, Gilkeson R, Mansoori B, Aandal GE. A comparison of computer-aided detection (CAD) effectiveness in pulmonary nodule identification using different methods of bone suppression in chest radiographs. J Digit Imaging 2013;26(4):651-656
24. Dellios N, Teichgraeber U, Chelaru R, Malich A, Papageorgiou IE. Computer-aided Detection Fidelity of Pulmonary Nodules in Chest Radiograph. J Clin Imaging Sci 2017;7(1):8
25. Li F, Engelmann R, Armato SG 3rd, MacMahon H. Computer-aided nodule detection system: results in an unselected series of consecutive chest radiographs. Acad Radiol 2015;22(4):475-480
26. Schalekamp S, van Ginneken B, Heggelman B, et al. New methods for using computer-aided detection information for the detection of lung nodules on chest radiographs. Br J Radiol 2014;87(1036):20140015
27. de Groot PM, Carter BW, Abbott GF, Wu CC. Pitfalls in chest radiographic interpretation: blind spots. Semin Roentgenol 2015;50(3):197-209
28. Bengio Y, Courville A, Vincent P. Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 2013;35(8):1798-1828. The score was defined as the number of thoracic radiologists who successfully detected the nodule (confidence 1).