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

References

• 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.
  Google Scholar
• 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.
  Google Scholar
• 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.
  Google Scholar
• 4. Potchen EJ, Cooper TG, Sierra AE, et al. Measuring performance in chest radiography. Radiology 2000;217(2):456–459.
  Google Scholar
• 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.
  MedlineGoogle Scholar
• 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.
  MedlineGoogle Scholar
• 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.
  Google Scholar
• 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.
  Google Scholar
• 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.
  MedlineGoogle Scholar
• 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.
  MedlineGoogle Scholar
• 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.
  MedlineGoogle Scholar
• 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.
  MedlineGoogle Scholar
• 13. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521(7553):436–444.
  MedlineGoogle Scholar
• 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.
  Google Scholar
• 15. Lecun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE 1998;86(11):2278–2324.
  Google Scholar
• 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.
  Google Scholar
• 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.
  Google Scholar
• 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.
  Google Scholar
• 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.
  MedlineGoogle Scholar
• 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.
  MedlineGoogle Scholar
• 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.
  Google Scholar
• 22. Bland JM, Altman DG. Multiple significance tests: the Bonferroni method. BMJ 1995;310(6973):170.
  MedlineGoogle Scholar
• 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.
  MedlineGoogle Scholar
• 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.
  MedlineGoogle Scholar
• 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.
  MedlineGoogle Scholar
• 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.
  MedlineGoogle Scholar
• 27. de Groot PM, Carter BW, Abbott GF, Wu CC. Pitfalls in chest radiographic interpretation: blind spots. Semin Roentgenol 2015;50(3):197–209.
  MedlineGoogle Scholar
• 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).
  MedlineGoogle Scholar