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Lung Nodule: Imaging Features and Evaluation in the Age of Machine Learning

  • Pulmonary Radiology (M Stephens and S Kapur, Section Editors)
  • Published:
Current Pulmonology Reports Aims and scope Submit manuscript

Abstract

Purpose of Review

With the unprecedented increase in chest CT studies, especially due to implementation of lung cancer screening, evaluation of lung nodules by radiologists can be exhausting and time-consuming. Machine learning promises to be a useful tool for detection and characterization of nodules. The purpose of this review is to evaluate the recent literature pertaining to machine learning in lung nodule detection and evaluation.

Recent Findings

There has been a recent surge of publications pertaining to machine learning and its applications in chest imaging. Many studies have shown promising results for automatic detection and characterization of lung nodules. Other studies have shown combined performance of a radiologist and computer-assisted detection (CAD) out performed a single radiologist, CAD alone, and double readers. Although these recent advances heighten expectations, it is important for developers and users to be mindful of challenges such as training, validation, independent testing, and proper user training.

Summary

Computer-aided technology can help radiologists in evaluating lung nodules especially with the large number of scans performed. Recent advances in machine learning are replacing traditional methods and could significantly change the way radiology is practiced.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance

  1. World Cancer Report, World Health Organization (WHO) 2014. http://publications.iarc.fr/Non-Series-Publications/World-Cancer-Reports/World-Cancer-Report-2014. Access 17 July 2019.

  2. National Lung Screening Trial Research, T, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011;365(5):395–409.

    Article  Google Scholar 

  3. Naidich DP, Bankier AA, MacMahon H, Schaefer-Prokop CM, Pistolesi M, Goo JM, et al. Recommendations for the management of subsolid pulmonary nodules detected at CT: a statement from the Fleischner Society. Radiology. 2013;266(1):304–17.

    Article  PubMed  Google Scholar 

  4. Balekian AA, Silvestri GA, Simkovich SM, Mestaz PJ, Sanders GD, Daniel J, et al. Accuracy of clinicians and models for estimating the probability that a pulmonary nodule is malignant. Ann Am Thorac Soc. 2013;10(6):629–35.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Awai K, Murao K, Ozawa A, Nakayama Y, Nakaura T, Liu D, et al. Pulmonary nodules: estimation of malignancy at thin-section helical CT--effect of computer-aided diagnosis on performance of radiologists. Radiology. 2006;239(1):276–84.

    Article  PubMed  Google Scholar 

  6. Sahiner B, et al. Deep learning in medical imaging and radiation therapy. Med Phys. 2019;46(1):e1–e36. This paper provide up to date information about applications of deep learning in medical imaging in general including lungs.

    Article  PubMed  Google Scholar 

  7. Bastanlar Y, Ozuysal M. Introduction to machine learning. Methods Mol Biol. 2014;1107:105–28.

    Article  PubMed  Google Scholar 

  8. Wang S, Summers RM. Machine learning and radiology. Med Image Anal. 2012;16(5):933–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. McCarville MB, Lederman HM, Santana VM, Daw NC, Shochat SJ, Li CS, et al. Distinguishing benign from malignant pulmonary nodules with helical chest CT in children with malignant solid tumors. Radiology. 2006;239(2):514–20.

    Article  PubMed  Google Scholar 

  10. Cheng JZ, Ni D, Chou YH, Qin J, Tiu CM, Chang YC, et al. Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Sci Rep. 2016;6:24454.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Cascio D, Magro R, Fauci F, Iacomi M, Raso G. Automatic detection of lung nodules in CT datasets based on stable 3D mass-spring models. Comput Biol Med. 2012;42(11):1098–109.

    Article  CAS  PubMed  Google Scholar 

  12. Ge Z, Sahiner B, Chan HP, Hadjiiski LM, Cascade PN, Bogot N, et al. Computer-aided detection of lung nodules: false positive reduction using a 3D gradient field method and 3D ellipsoid fitting. Med Phys. 2005;32(8):2443–54.

    Article  PubMed  Google Scholar 

  13. Guo W, Li Q. High performance lung nodule detection schemes in CT using local and global information. Med Phys. 2012;39(8):5157–68.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Gurcan MN, Sahiner B, Petrick N, Chan HP, Kazerooni EA, Cascade PN, et al. Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system. Med Phys. 2002;29(11):2552–8.

    Article  PubMed  Google Scholar 

  15. Chan HP, Hadjiiski L, Zhou C, Sahiner B. Computer-aided diagnosis of lung cancer and pulmonary embolism in computed tomography-a review. Acad Radiol. 2008;15(5):535–55.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Gruetzemacher R, Gupta A. Using deep learning for pulmonary nodule detection & diagnosis. Conference Proceedings of the 22nd Americas Conference on Information Systems (AMCIS). San Diego, California, United States; 2016.

  17. Messay T, Hardie RC, Rogers SK. A new computationally efficient CAD system for pulmonary nodule detection in CT imagery. Med Image Anal. 2010;14(3):390–406.

    Article  PubMed  Google Scholar 

  18. Armato SG 3rd, et al. Computerized detection of pulmonary nodules on CT scans. Radiographics. 1999;19(5):1303–11.

    Article  PubMed  Google Scholar 

  19. Firmino M, Angelo G, Morais H, Dantas MR, Valentim R. Computer-aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy. Biomed Eng Online. 2016;15:2.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Fukushima K, Miyake S. Neocognitron - a new algorithm for pattern-recognition tolerant of deformations and shifts in position. Pattern Recogn. 1982;15(6):455–69.

    Article  Google Scholar 

  21. LeCun, Y., et al. Handwritten digit recognistion with a back-projection network. Conference Proceedings of the 2nd international conference on Neural Information Processing Systems (NIPS). 1989; 396-404.

  22. Lo SCB, et al. Computer-assisted diagnosis of lung nodule detection using artificial convolution neural-network. Medical Imaging 1993: Image Processing, 1993. 1898, p. 859–869.

  23. Hinton GE, Osindero S, Teh YW. A fast learning algorithm for deep belief nets. Neural Comput. 2006;18(7):1527–54.

    Article  PubMed  Google Scholar 

  24. Krizhevsky, A., I. Sutskever, and G. Hinton. ImageNet classification with deep convolutional neural networks. in 25th International Conference on Neural Information Processing Systems. 2012. Lake Tahoe, Nevada. This paper was a major paper that showed the value of deep learning and how it can outperform traditional methods of machine learning.

  25. Zech JR, Badgeley MA, Liu M, Costa AB, Titano JJ, Oermann EK. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study. PLoS Med. 2018;15(11):e1002683.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Goo JM. A computer-aided diagnosis for evaluating lung nodules on chest CT: the current status and perspective. Korean J Radiol. 2011;12(2):145–55.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Armato SG 3rd, et al. LUNGx challenge for computerized lung nodule classification. J Med Imaging (Bellingham). 2016;3(4):044506.

    Article  Google Scholar 

  28. Setio AAA, Traverso A, de Bel T, Berens MSN, Bogaard C, Cerello P, et al. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge. Med Image Anal. 2017;42:1–13.

    Article  PubMed  Google Scholar 

  29. Data Science Bowl https://datasciencebowl.com/competitions/turning-machine-intelligence-against-lung-cancer/. 2016–2017. Access 1 April 2019.

  30. Meyers PH, Nice CM Jr, Becker HC, Nettleton WJ Jr, Sweeney JW, Meckstroth GR. Automated computer analysis of radiographic images. Radiology. 1964;83:1029–34.

    Article  CAS  PubMed  Google Scholar 

  31. Giger ML, Doi K, MacMahon H. Image feature analysis and computer-aided diagnosis in digital radiography. 3. Automated detection of nodules in peripheral lung fields. Med Phys. 1988;15(2):158–66.

    Article  CAS  PubMed  Google Scholar 

  32. Kanazawa K, Kawata Y, Niki N, Satoh H, Ohmatsu H, Kakinuma R, et al. Computer-aided diagnosis for pulmonary nodules based on helical CT images. Comput Med Imaging Graph. 1998;22(2):157–67.

    Article  CAS  PubMed  Google Scholar 

  33. Lin, J.S., et al., Application of artificial neural networks for reduction of false-positive detections in digital chest radiographs. Conference Proceedings of the Annual Symposium on Computer Application in Medical Care. 1993;434–8.

  34. Lo SB, et al. Artificial convolution neural network techniques and applications for lung nodule detection. IEEE Trans Med Imaging. 1995;14(4):711–8.

    Article  CAS  PubMed  Google Scholar 

  35. Lo SC, Freedman MT, Lin JS, Mun SK. Automatic lung nodule detection using profile matching and back-propagation neural network techniques. J Digit Imaging. 1993;6(1):48–54.

    Article  CAS  PubMed  Google Scholar 

  36. McCulloch CC, Kaucic RA, Mendonça PRS, Walter DJ, Avila RS. Model-based detection of lung nodules in computed tomography exams. Thoracic computer-aided diagnosis. Acad Radiol. 2004;11(3):258–66.

    Article  PubMed  Google Scholar 

  37. Way TW, Hadjiiski LM, Sahiner B, Chan HP, Cascade PN, Kazerooni EA, et al. Computer-aided diagnosis of pulmonary nodules on CT scans: segmentation and classification using 3D active contours. Med Phys. 2006;33(7):2323–37.

    Article  PubMed  Google Scholar 

  38. Chan HP, Sahiner B, Wagner RF, Petrick N. Classifier design for computer-aided diagnosis: effects of finite sample size on the mean performance of classical and neural network classifiers. Med Phys. 1999;26(12):2654–68.

    Article  CAS  PubMed  Google Scholar 

  39. Marten K, Engelke C, Seyfarth T, Grillhösl A, Obenauer S, Rummeny EJ. Computer-aided detection of pulmonary nodules: influence of nodule characteristics on detection performance. Clin Radiol. 2005;60(2):196–206.

    Article  CAS  PubMed  Google Scholar 

  40. Lee JW, Goo JM, Lee HJ, Kim JH, Kim S, Kim YT. The potential contribution of a computer-aided detection system for lung nodule detection in multidetector row computed tomography. Investig Radiol. 2004;39(11):649–55.

    Article  Google Scholar 

  41. Brown MS, Goldin JG, Suh RD, McNitt-Gray MF, Sayre JW, Aberle DR. Lung micronodules: automated method for detection at thin-section CT--initial experience. Radiology. 2003;226(1):256–62.

    Article  PubMed  Google Scholar 

  42. van Ginneken B, Armato SG III, de Hoop B, van Amelsvoort-van de Vorst S, Duindam T, Niemeijer M, et al. Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: the ANODE09 study. Med Image Anal. 2010;14(6):707–22.

    Article  PubMed  Google Scholar 

  43. Bae KT, Kim JS, Na YH, Kim KG, Kim JH. Pulmonary nodules: automated detection on CT images with morphologic matching algorithm--preliminary results. Radiology. 2005;236(1):286–93.

    Article  PubMed  Google Scholar 

  44. Sahiner B, Chan HP, Hadjiiski LM, Cascade PN, Kazerooni EA, Chughtai AR, et al. Effect of CAD on radiologists’ detection of lung nodules on thoracic CT scans: analysis of an observer performance study by nodule size. Acad Radiol. 2009;16(12):1518–30.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Godoy MC, et al. Benefit of computer-aided detection analysis for the detection of subsolid and solid lung nodules on thin- and thick-section CT. AJR Am J Roentgenol. 2013;200(1):74–83.

    Article  PubMed  Google Scholar 

  46. Henschke CI, Yankelevitz DF, Mirtcheva R, McGuinness G, McCauley D, Miettinen OS. CT screening for lung cancer: frequency and significance of part-solid and nonsolid nodules. AJR Am J Roentgenol. 2002;178(5):1053–7.

    Article  PubMed  Google Scholar 

  47. Yuan R, Vos PM, Cooperberg PL. Computer-aided detection in screening CT for pulmonary nodules. AJR Am J Roentgenol. 2006;186(5):1280–7.

    Article  PubMed  Google Scholar 

  48. Benzakoun J, Bommart S, Coste J, Chassagnon G, Lederlin M, Boussouar S, et al. Computer-aided diagnosis (CAD) of subsolid nodules: evaluation of a commercial CAD system. Eur J Radiol. 2016;85(10):1728–34.

    Article  PubMed  Google Scholar 

  49. Yanagawa M, Honda O, Yoshida S, Ono Y, Inoue A, Daimon T, et al. Commercially available computer-aided detection system for pulmonary nodules on thin-section images using 64 detectors-row CT: preliminary study of 48 cases. Acad Radiol. 2009;16(8):924–33.

    Article  PubMed  Google Scholar 

  50. Kim JS, Kim JH, Cho G, Bae KT. Automated detection of pulmonary nodules on CT images: effect of section thickness and reconstruction interval--initial results. Radiology. 2005;236(1):295–9.

    Article  PubMed  Google Scholar 

  51. White CS, Pugatch R, Koonce T, Rust SW, Dharaiya E. Lung nodule CAD software as a second reader: a multicenter study. Acad Radiol. 2008;15(3):326–33.

    Article  PubMed  Google Scholar 

  52. Narayanan BN, Hardie RC, Kebede TM. Performance analysis of a computer-aided detection system for lung nodules in CT at different slice thicknesses. J Med Imaging (Bellingham). 2018;5(1):014504.

    Google Scholar 

  53. Hein PA, et al. Computer-aided pulmonary nodule detection - performance of two CAD systems at different CT dose levels. Rofo. 2009;181(11):1056–64.

    Article  CAS  PubMed  Google Scholar 

  54. Lee JY, Chung MJ, Yi CA, Lee KS. Ultra-low-dose MDCT of the chest: influence on automated lung nodule detection. Korean J Radiol. 2008;9(2):95–101.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Riccardi A, Petkov TS, Ferri G, Masotti M, Campanini R. Computer-aided detection of lung nodules via 3D fast radial transform, scale space representation, and Zernike MIP classification. Med Phys. 2011;38(4):1962–71.

    Article  PubMed  Google Scholar 

  56. Setio AA, et al. Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans Med Imaging. 2016;35(5):1160–9.

    Article  PubMed  Google Scholar 

  57. Dou Q, Chen H, Yu L, Qin J, Heng PA. Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection. IEEE Trans Biomed Eng. 2017;64(7):1558–67.

    Article  PubMed  Google Scholar 

  58. Ferreira JR Jr, Oliveira MC, de Azevedo-Marques PM. Characterization of pulmonary nodules based on features of margin sharpness and texture. J Digit Imaging. 2018;31(4):451–63.

    Article  PubMed  Google Scholar 

  59. Song Q, et al. Using deep learning for classification of lung nodules on computed tomography images. J Healthc Eng. 2017;2017:8314740.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Tu X, Xie M, Gao J, Ma Z, Chen D, Wang Q, et al. Automatic categorization and scoring of solid, part-solid and non-solid pulmonary nodules in CT images with convolutional neural network. Sci Rep. 2017;7(1):8533.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Ciompi F, Chung K, van Riel SJ, Setio AAA, Gerke PK, Jacobs C, et al. Towards automatic pulmonary nodule management in lung cancer screening with deep learning. Sci Rep. 2017;7:46479.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Nishio M, Sugiyama O, Yakami M, Ueno S, Kubo T, Kuroda T, et al. Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning. PLoS One. 2018;13(7):e0200721.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Way TW, Sahiner B, Chan HP, Hadjiiski L, Cascade PN, Chughtai A, et al. Computer-aided diagnosis of pulmonary nodules on CT scans: improvement of classification performance with nodule surface features. Med Phys. 2009;36(7):3086–98.

    Article  PubMed  PubMed Central  Google Scholar 

  64. El-Baz A, et al. 3D shape analysis for early diagnosis of malignant lung nodules. Med Image Comput Comput Assist Interv. 2011;14(Pt 3):175–82.

    PubMed  Google Scholar 

  65. Kumar, D., A. Wong, and D.A. Clausi, Lung Nodule Classification Using Deep Features in CT Images. 2015 12th Conference on Computer and Robot Vision Crv 2015, 2015: p. 133–138.

  66. Wei G, et al. Content-based image retrieval for lung nodule classification using texture features and learned distance metric. J Med Syst. 2017;42(1):13.

    Article  PubMed  Google Scholar 

  67. Zhao X, Liu L, Qi S, Teng Y, Li J, Qian W. Agile convolutional neural network for pulmonary nodule classification using CT images. Int J Comput Assist Radiol Surg. 2018;13(4):585–95.

    Article  PubMed  Google Scholar 

  68. Xie Y, Xia Y, Zhang J, Song Y, Feng D, Fulham M, et al. Knowledge-based collaborative deep learning for benign-malignant lung nodule classification on chest CT. IEEE Trans Med Imaging. 2019;38(4):991–1004.

    Article  PubMed  Google Scholar 

  69. van Klaveren RJ, Oudkerk M, Prokop M, Scholten ET, Nackaerts K, Vernhout R, et al. Management of lung nodules detected by volume CT scanning. N Engl J Med. 2009;361(23):2221–9.

    Article  PubMed  Google Scholar 

  70. Zhao B, Schwartz LH, Moskowitz CS, Ginsberg MS, Rizvi NA, Kris MG. Lung cancer: computerized quantification of tumor response--initial results. Radiology. 2006;241(3):892–8.

    Article  PubMed  Google Scholar 

  71. Goo JM, Tongdee T, Tongdee R, Yeo K, Hildebolt CF, Bae KT. Volumetric measurement of synthetic lung nodules with multi-detector row CT: effect of various image reconstruction parameters and segmentation thresholds on measurement accuracy. Radiology. 2005;235(3):850–6.

    Article  PubMed  Google Scholar 

  72. Goo JM, Kim KG, Gierada DS, Castro M, Bae KT. Volumetric measurements of lung nodules with multi-detector row CT: effect of changes in lung volume. Korean J Radiol. 2006;7(4):243–8.

    Article  PubMed  PubMed Central  Google Scholar 

  73. Zhao B, Schwartz LH, Moskowitz CS, Wang L, Ginsberg MS, Cooper CA, et al. Pulmonary metastases: effect of CT section thickness on measurement--initial experience. Radiology. 2005;234(3):934–9.

    Article  PubMed  Google Scholar 

  74. Das M, Ley-Zaporozhan J, Gietema HA, Czech A, Mühlenbruch G, Mahnken AH, et al. Accuracy of automated volumetry of pulmonary nodules across different multislice CT scanners. Eur Radiol. 2007;17(8):1979–84.

    Article  PubMed  Google Scholar 

  75. Petrou M, Quint LE, Nan B, Baker LH. Pulmonary nodule volumetric measurement variability as a function of CT slice thickness and nodule morphology. AJR Am J Roentgenol. 2007;188(2):306–12.

    Article  PubMed  Google Scholar 

  76. Kuhnigk JM, Dicken V, Bornemann L, Bakai A, Wormanns D, Krass S, et al. Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans. IEEE Trans Med Imaging. 2006;25(4):417–34.

    Article  PubMed  Google Scholar 

  77. Ravenel JG, Leue WM, Nietert PJ, Miller JV, Taylor KK, Silvestri GA. Pulmonary nodule volume: effects of reconstruction parameters on automated measurements--a phantom study. Radiology. 2008;247(2):400–8.

    Article  PubMed  Google Scholar 

  78. Ko JP, Rusinek H, Jacobs EL, Babb JS, Betke M, McGuinness G, et al. Small pulmonary nodules: volume measurement at chest CT--phantom study. Radiology. 2003;228(3):864–70.

    Article  PubMed  Google Scholar 

  79. Aerts HJ, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006.

    Article  CAS  PubMed  Google Scholar 

  80. Kadir T, Gleeson F. Lung cancer prediction using machine learning and advanced imaging techniques. Transl Lung Cancer Res. 2018;7(3):304–12.

    Article  PubMed  PubMed Central  Google Scholar 

  81. Khorrami, M., et al. A combination of intra- and peritumoral features on baseline CT scans is associated with overall survival in non-small cell lung cancer patients treated with immune check point inhibitors: a multi-agent multi-site study. Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109500R. 2019 https://doi.org/10.1117/12.2513001.

  82. Lee KW, Kim M, Gierada DS, Bae KT. Performance of a computer-aided program for automated matching of metastatic pulmonary nodules detected on follow-up chest CT. AJR Am J Roentgenol. 2007;189(5):1077–81.

    Article  PubMed  Google Scholar 

  83. Beigelman-Aubry C, Raffy P, Yang W, Castellino RA, Grenier PA. Computer-aided detection of solid lung nodules on follow-up MDCT screening: evaluation of detection, tracking, and reading time. AJR Am J Roentgenol. 2007;189(4):948–55.

    Article  PubMed  Google Scholar 

  84. Tao C, Gierada DS, Zhu F, Pilgram TK, Wang JH, Bae KT. Automated matching of pulmonary nodules: evaluation in serial screening chest CT. AJR Am J Roentgenol. 2009;192(3):624–8.

    Article  PubMed  Google Scholar 

  85. Sumathipala Y, Shafiq M, Bongen E, Brinton C, Paik D. Machine learning to predict lung nodule biopsy method using CT image features: a pilot study. Comput Med Imaging Graph. 2019;71:1–8.

    Article  PubMed  Google Scholar 

  86. McKee BJ, Regis SM, McKee AB, Flacke S, Wald C. Performance of ACR lung-RADS in a clinical CT lung screening program. J Am Coll Radiol. 2015;12(3):273–6.

    Article  PubMed  Google Scholar 

  87. Christe A, Torrente JC, Lin M, Yen A, Hallett R, Roychoudhury K, et al. CT screening and follow-up of lung nodules: effects of tube current-time setting and nodule size and density on detectability and of tube current-time setting on apparent size. AJR Am J Roentgenol. 2011;197(3):623–30.

    Article  PubMed  Google Scholar 

  88. Christe A, Charimo-Torrente J, Roychoudhury K, Vock P, Roos JE. Accuracy of low-dose computed tomography (CT) for detecting and characterizing the most common CT-patterns of pulmonary disease. Eur J Radiol. 2013;82(3):e142–50.

    Article  PubMed  Google Scholar 

  89. Rusinek H, Naidich DP, McGuinness G, Leitman BS, McCauley DI, Krinsky GA, et al. Pulmonary nodule detection: low-dose versus conventional CT. Radiology. 1998;209(1):243–9.

    Article  CAS  PubMed  Google Scholar 

  90. Wormanns D, Ludwig K, Beyer F, Heindel W, Diederich S. Detection of pulmonary nodules at multirow-detector CT: effectiveness of double reading to improve sensitivity at standard-dose and low-dose chest CT. Eur Radiol. 2005;15(1):14–22.

    Article  PubMed  Google Scholar 

  91. Quekel LG, et al. Detection of lung cancer on the chest radiograph: impact of previous films, clinical information, double reading, and dual reading. J Clin Epidemiol. 2001;54(11):1146–50.

    Article  CAS  PubMed  Google Scholar 

  92. Zhao Y, de Bock GH, Vliegenthart R, van Klaveren RJ, Wang Y, Bogoni L, et al. Performance of computer-aided detection of pulmonary nodules in low-dose CT: comparison with double reading by nodule volume. Eur Radiol. 2012;22(10):2076–84.

    Article  PubMed  PubMed Central  Google Scholar 

  93. Fraioli F, Bertoletti L, Napoli A, Pediconi F, Calabrese FA, Masciangelo R, et al. Computer-aided detection (CAD) in lung cancer screening at chest MDCT: ROC analysis of CAD versus radiologist performance. J Thorac Imaging. 2007;22(3):241–6.

    Article  PubMed  Google Scholar 

  94. Beyer F, Zierott L, Fallenberg EM, Juergens KU, Stoeckel J, Heindel W, et al. Comparison of sensitivity and reading time for the use of computer-aided detection (CAD) of pulmonary nodules at MDCT as concurrent or second reader. Eur Radiol. 2007;17(11):2941–7.

    Article  CAS  PubMed  Google Scholar 

  95. Christe A, Leidolt L, Huber A, Steiger P, Szucs-Farkas Z, Roos JE, et al. Lung cancer screening with CT: evaluation of radiologists and different computer assisted detection software (CAD) as first and second readers for lung nodule detection at different dose levels. Eur J Radiol. 2013;82(12):e873–8.

    Article  CAS  PubMed  Google Scholar 

  96. Chan HP, Hadjiiski L, Samala RK. Computer-aided diagnosis in the era of deep learning. Med Phys (accepted), 2019.

  97. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44.

    Article  CAS  PubMed  Google Scholar 

  98. Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, et al. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging. 2016;35(5):1285–98.

    Article  PubMed  Google Scholar 

  99. Samala RK, Chan HP, Hadjiiski L, Helvie MA, Richter CD, Cha KH. Breast cancer diagnosis in digital breast tomosynthesis: effects of training sample size on multi-stage transfer learning using deep neural nets. IEEE Trans Med Imaging. 2019;38(3):686–96.

    Article  PubMed  PubMed Central  Google Scholar 

  100. Gao C, et al. Augmenting LIDC dataset using 3D generative adversarial networks to improve lung nodule detection. Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109501K.2019https://doi.org/10.1117/12.2513011.

  101. Petrick N, Sahiner B, Armato SG III, Bert A, Correale L, Delsanto S, et al. Evaluation of computer-aided detection and diagnosis systems. Med Phys. 2013;40(8):087001.

    Article  PubMed  PubMed Central  Google Scholar 

  102. Huo Z, Summers RM, Paquerault S, Lo J, Hoffmeister J, Armato SG III, et al. Quality assurance and training procedures for computer-aided detection and diagnosis systems in clinical use. Med Phys. 2013;40(7):077001.

    Article  PubMed  PubMed Central  Google Scholar 

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  1. Department of Radiology, Michigan Medicine, University of Michigan, 1500 E. Medical Center Drive, Ann Arbor, MI, 48109, USA

    M. Sayyouh, L. M. Hadjiiyski, H-P. Chan & P. Agarwal

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  1. M. Sayyouh
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Sayyouh, M., Hadjiiyski, L.M., Chan, HP. et al. Lung Nodule: Imaging Features and Evaluation in the Age of Machine Learning. Curr Pulmonol Rep 8, 86–95 (2019). https://doi.org/10.1007/s13665-019-00229-8

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