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Artificial Intelligence and Computer-Assisted Evaluation of Chest Pathology

  • Edwin J. R. van BeekEmail author
  • John T. Murchison
Chapter

Abstract

The use of computer-assisted diagnosis in chest pathologies has gradually made inroads into the clinical workflow of chest radiography and computed tomography. The introduction of novel machine learning tools and application of artificial intelligence will likely lead to more advanced applications, which will be better suited to what chest radiologists require to allow efficient and accurate reporting. The excitement around these software opportunities extends beyond the simple “diagnostic” utility but expands into quantifiable disease biomarkers, which will have impact on patients’ management, the selection of patients for clinical trials of new drugs and ultimately the impact of (new) treatments on long-term outcomes.

This chapter will offer insight into the use of computer-assisted systems in the main areas of lung nodules/lung cancer, pulmonary embolism and parenchymal and airways diseases.

Keywords

Thoracic imaging Lung cancer Chronic obstructive pulmonary diseases Pulmonary fibrosis Biomarkers 

References

  1. 1.
    Lodwick GW, Keats TE, Dorst JP. The coding of roentgen images for computer analysis as applied to lung cancer. Radiology. 1963;81:185–200.PubMedGoogle Scholar
  2. 2.
    Melendez J, Philipsen RHHM, Chanda-Kapata P, Sunkutu V, Kapata N, van Ginneken B. Automatic versus human reading of chest X-rays in the Zambia national tuberculosis prevalence study. Int J Tuberc Lung Dis. 2017;21:880–6.PubMedGoogle Scholar
  3. 3.
    Kao EF, Jaw TW, Li CW, Chou MC, Liu GC. Automated detection of endotracheal tubes in paediatric chest radiographs. Comput Methods Prog Biomed. 2015;118:1–10.Google Scholar
  4. 4.
    Zaglam N, Jouvet P, Flechelles O, Emeriaud G, Cheriet F. Computer-aided diagnosis system for the acute respiratory distress syndrome from chest radiographs. Comp Biol Med. 2014;52:41–8.Google Scholar
  5. 5.
    Zimmerman JJ, Akhtar SR, Caldwell E, Rubenfield GD. Incidence and outcomes of pediatric acute lung injury. Pediatrics. 2009;124:87–95.PubMedGoogle Scholar
  6. 6.
    Quekel LGBA, Kessels AGH, Goei R, van Engelshoven JMA. Miss rate of lung cancer on the chest radiograph in clinical practice. Chest. 1999;115:720–4.PubMedGoogle Scholar
  7. 7.
    Quekel LGBA, Goie R, Kessels AGH, van Engelshoven JMA. Detection of lung cancer on the chest radiograph: impact of previous films, clinical information, double reading, and dual reading. J Clin Epidemiol. 2001;54:1146–50.PubMedGoogle Scholar
  8. 8.
    MacMahon H, Li F, Engelmann R, Roberts R, Armato S. Dual energy subtraction and temporal subtraction chest radiography. J Thorac Imaging. 2008;23:77–85.PubMedGoogle Scholar
  9. 9.
    White CJ, Flukinger T, Jeudy J, Chen JJ. Detection system to detect missed lung cancer at chest radiography. Radiology. 2009;252:273–81.PubMedGoogle Scholar
  10. 10.
    Li F, Engelmann R, Pesce LL, Doi K, Metz CE, MacMahon H. Small lung cancers: improved detection by use of bone suppression imaging – comparison with dual-energy subtraction chest radiography. Radiology. 2011;261:937–49.PubMedGoogle Scholar
  11. 11.
    Kakeda S, Moriya J, Sato H, et al. Improved detection of lung nodules on chest radiographs using a commercial computer-aided diagnosis system. Am J Roentgenol. 2004;182:505–10.Google Scholar
  12. 12.
    Bley TA, Baumann T, Saueressig U, et al. Comparison of radiologist and CAD performance in the detection of CT-confirmed subtle pulmonary nodules on digital chest radiographs. Invest Radiol. 2008;43:343–8.PubMedGoogle Scholar
  13. 13.
    Van Beek EJR, Mullan B, Thompson B. Evaluation of a real-time interactive pulmonary nodule analysis system on chest digital radiographic images. A prospective study. Acad Radiol. 2008;15:571–5.PubMedGoogle Scholar
  14. 14.
    The National Lung Screening Trial Research Team. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011;365:395–409.PubMedCentralGoogle Scholar
  15. 15.
    Black WC, Gareen IF, Soneji SS, et al. Cost-effectiveness of CT screening in the National Lung Screening Trial. N Engl J Med. 2014;371:1793–802.PubMedPubMedCentralGoogle Scholar
  16. 16.
    Aberle DR, DeMello S, Berg CD, et al. Results of the two incidence screenings in the National Lung Screening Trial. N Engl J Med. 2013;369:920–31.PubMedPubMedCentralGoogle Scholar
  17. 17.
    Oudkerk M, Deveraj A, Vliegenthart R, et al. European position statement on lung cancer screening. Lancet Oncol. 2017;18:e754–66.PubMedGoogle Scholar
  18. 18.
    Heuvelmans MA, Oudkerk M, de Jong PA, Mali WP, Groen HJM, Vliegenthart R. The impact of radiologists’ expertise on screen result decisions in a CT lung cancer screening trial. Eur Radiol. 2015;25:792–9.PubMedGoogle Scholar
  19. 19.
    Nietert PJ, Ravenel JG, Taylor KK, Silvestri GA. Influence of nodule detection software on radiologists’ confidence in identifying pulmonary nodules with computed tomography. J Thorac Imaging. 2011;26:48–53.PubMedPubMedCentralGoogle Scholar
  20. 20.
    Rubin GD, Lyo JK, Palk DS, et al. Pulmonary nodules on multi-detector row CT scans: performance comparison of radiologists and computer-aided detection. Radiology. 2005;234:274–83.PubMedGoogle Scholar
  21. 21.
    Das M, Muhlenbruch G, Mahnken AH, et al. Small pulmonary nodules: effect of two computer-aided detection systems on radiologist performance. Radiology. 2006;241:564–71.PubMedGoogle Scholar
  22. 22.
    Liang M, Tang W, Xu DM, et al. Low-dose CT screening for lung cancer: computer-aided detection of missed lung cancers. Radiology. 2016;281:279–88.PubMedGoogle Scholar
  23. 23.
    Rubin GD. Lung nodule and cancer detection in computed tomography screening. J Thorac Imaging. 2015;30:130–8.PubMedPubMedCentralGoogle Scholar
  24. 24.
    Callister MEJ, Baldwin DR, Akram AR, et al. British Thoracic Society guidelines for the investigation and management of pulmonary nodules. Thorax. 2015;70(Suppl 2):ii1–ii54.PubMedGoogle Scholar
  25. 25.
    MacMahon H, Naidich DP, Goo JM, et al. Guidelines for management of incidental pulmonary nodules detected on CT imaging: from the Fleischner Society 2017. Radiology. 2017;284:228–43.PubMedPubMedCentralGoogle Scholar
  26. 26.
    Bankier AA, MacMahon H, Goo JM, Rubin GD, Schaefer-Prokop CM, Naidich DP. Recommendations for measuring pulmonary nodules at CT: a statement from the Fleischner Society. Radiology. 2017;285:584–600.PubMedGoogle Scholar
  27. 27.
    Deveraj A, van Ginneken B, Nair A, Baldwin D. Use of volumetry for lung nodule management: theory and practice. Radiology. 2017;284:630–44.Google Scholar
  28. 28.
    Lo SCB, Freeman MT, Gillis LB, White CS, Mun SK. Computer-aided detection of lung nodules on CT with a computerized pulmonary vessel suppressed function. Am J Roentgenol. 2018;210:1–9.Google Scholar
  29. 29.
    Van Riel SJ, Ciompi F, Winkler Wille MM, Dirksen A, et al. Malignancy risk estimation of pulmonary nodules in screening CTs: comparison between a computer model and human observers. PLoS One. 2017;12:e0185032.PubMedPubMedCentralGoogle Scholar
  30. 30.
    Wille MM, Dirksen A, Ashraf H, et al. Results of the randomized Danish lung cancer screening trial with focus on high-risk profiling. Am J Respir Crit Care Med. 2013;193:542–51.Google Scholar
  31. 31.
    McWilliams A, Tammemagi MC, Mayo JR, et al. Probability of cancer in pulmonary nodules detected on first screening CT. N Engl J Med. 2013;369:910–9.PubMedPubMedCentralGoogle Scholar
  32. 32.
    Snoeckx A, Reyntiens P, Desbuquoit D, et al. Evaluation of the solitary pulmonary nodule: size matters, but do not ignore the power of morphology. Insights Imaging. 2018;9:73–86.PubMedGoogle Scholar
  33. 33.
  34. 34.
    Lee G, Lee HY, Park H, et al. Radiomics and its emerging role in lung cancer research, imaging biomarkers and clinical management: state of the art. Eur J Radiol. 2017;86:297–307.PubMedGoogle Scholar
  35. 35.
    Aerts HJWL, Velasquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006.PubMedPubMedCentralGoogle Scholar
  36. 36.
    Ko JP, Suh J, Ibidapo O, et al. Lung adenocarcinoma: correlation of quantitative CT findings with pathologic findings. Radiology. 2016;280:931–9.PubMedGoogle Scholar
  37. 37.
    Huang P, Park S, Yan R, et al. Lung cancer diagnosis with small pulmonary nodules: a matched case-control study. Radiology. 2018;286:286–95.PubMedGoogle Scholar
  38. 38.
    Liu Y, Wang H, Li Q, et al. Radiologic features of small pulmonary nodules and lung cancer risk in the National Lung Screening Trial: a nested case-control study. Radiology. 2018;286:298–306.PubMedGoogle Scholar
  39. 39.
    Horeweg N, van Rosmalen J, Heuvelmans MA, et al. Lung cancer probability in patients with CT-detected pulmonary nodules: a prespecified analysis of data from the NELSON trial of low-dose CT screening. Lancet Oncol. 2014;15:1332–41.PubMedGoogle Scholar
  40. 40.
    Konstantinides SV, Torbicki A, Agnelli G, et al. 2014 ESC guidelines on the diagnosis and management of acute pulmonary embolism. Eur Heart J. 2014;35:3033–80.PubMedGoogle Scholar
  41. 41.
    Hochhegger B, Alves GRT, Chaves M, et al. Interobserver agreement between radiologists and radiology residents and emergency physicians in the detection of PE using CTPA. Clin Imaging. 2014;38:445–7.PubMedGoogle Scholar
  42. 42.
    Schoepf UJ, Schneider AC, Das M, Wood SA, Cheema JI, Costello P. Pulmonary embolism: computer-aided detection at multidetector row spiral computed tomography. J Thorac Imaging. 2007;22:319–23.PubMedGoogle Scholar
  43. 43.
    Maizlin ZV, Vos PM, Godoy MB, Cooperberg PL. Computer-aided detection of pulmonary embolism on CT angiography. J Thorac Imaging. 2007;22:324–9.PubMedGoogle Scholar
  44. 44.
    Buhmann S, Herzog P, Liang J, et al. Clinical evaluation of a computer-aided diagnosis (CAD) prototype for the detection of pulmonary embolism. Acad Radiol. 2007;14:851–8.Google Scholar
  45. 45.
    Engelke C, Schmidt S, Bakai A, Auer F, Marten K. Computer-assisted detection of pulmonary embolism: performance evaluation in consensus with experienced and inexperienced chest radiologists. Eur Radiol. 2008;18:298–307.PubMedGoogle Scholar
  46. 46.
    Blackmon KN, Florin C, Bogoni L, et al. Computer-aided detection of pulmonary embolism at CT pulmonary angiography: can it improve performance of inexperienced readers? Eur Radiol. 2011;21:1214–23.PubMedGoogle Scholar
  47. 47.
    Wittenberg R, Berger FH, Peters JH, et al. Acute pulmonary embolism: effect of a computer-assisted detection prototype on diagnosis – an observer study. Radiology. 2012;262:305–13.PubMedGoogle Scholar
  48. 48.
    Das M, Mühlenbruch G, Helm A, et al. Computer-aided detection of pulmonary embolism: influence on radiologists’ detection performance with respect to vessel segments. Eur Radiol. 2008;18:1350–5.PubMedGoogle Scholar
  49. 49.
    Kligerman S, Lahiji K, Galvin JR, Stokum C, White CS. Missed pulmonary embolism on CT angiography: assessment with pulmonary embolism – computer aided detection. Am J Roentegenol. 2013;202:65–73.Google Scholar
  50. 50.
    Dewailly M, Remy-Jardin M, Duhamel A, et al. Computer-aided detection of acute pulmonary embolism with 64-slice multi-detector row computed tomography: impact of the scanning conditions and overall image quality in the detection of peripheral clots. J Comput Assist Tomogr. 2010;34:23–30.PubMedGoogle Scholar
  51. 51.
    Lee CW, Seo JB, Song JW, et al. Evaluation of computer-aided detection and dual energy software in detection of peripheral pulmonary embolism on dual-energy pulmonary CT angiography. Eur Radiol. 2011;21:54–62.PubMedGoogle Scholar
  52. 52.
    Lahiji K, Kligerman S, Jeudy J, White C. Improved accuracy of pulmonary embolism computer-aided detection using iterative reconstruction compared with filtered back projection. Am J Roentgenol. 2014;203:763–71.Google Scholar
  53. 53.
    Kröger JR, Hickethier T, Pahn G, Gerhardt F, Maintz D, Bunck AC. Influence of spectral detector CT based monoenergetic images on the computer-aided detection of pulmonary artery embolism. Eur J Radiol. 2017;95:242–8.PubMedGoogle Scholar
  54. 54.
    Aviram G, Soikher E, Bendet A, et al. Prediction of mortality in pulmonary embolism based on left atrial volume measured on CT pulmonary angiography. Chest. 2016;149:667–75.PubMedGoogle Scholar
  55. 55.
    Li Y, Dai Y, Deng L, Guo Y. Computer-aided detection for the automated evaluation of pulmonary embolism. Technol Health Care. 2017;2015:S135–42.Google Scholar
  56. 56.
    Rucco M, Sousa-Rodrigues D, Merelli E, et al. Neural hypernetwork approach for pulmonary embolism diagnosis. BMC Res Notes. 2015;8:617.PubMedPubMedCentralGoogle Scholar
  57. 57.
    Uppaluri R, Mitsa T, Sonka M, Hoffman EA, McLennan G. Quantification of pulmonary emphysema from lung computed tomography images. Am J Respir Crit Care Med. 1997;156:248–54.PubMedGoogle Scholar
  58. 58.
    National Emphysema Treatment Trial Research Group. A randomized trial comparing lung volume reduction surgery with medical therapy for severe emphysema. N Engl J Med. 2003;348:2059–73.Google Scholar
  59. 59.
    Regan EA, Hokanson JE, Murphy JR, et al. Genetic epidemiology of COPD (COPDGene) study design. COPD. 2010;7:32–43.PubMedPubMedCentralGoogle Scholar
  60. 60.
    Wan ES, Hokanseon JE, Murphy JR, et al. Clinical and radiographic predictors of GOLD-unclassified smokers in the COPDGene study. Am J Respir Crit Care Med. 2011;184:57–63.PubMedPubMedCentralGoogle Scholar
  61. 61.
    Couper D, LaVange LM, Han M, et al. Design of the subpopulations and intermediate outcomes in COPD study (SPIROMICS). Thorax. 2014;69:491–4.PubMedGoogle Scholar
  62. 62.
    Hoffman EA, Ahmed FS, Baumhauer H, et al. Variation in the percent of emphysema-like lung in a healthy, non-smoking multi-ethnic sample. The MESA lung study. Ann Am Thorac Soc. 2014;11:898–907.PubMedPubMedCentralGoogle Scholar
  63. 63.
    Busacker A, Newell JD Jr, Keefe T, et al. A multivariate analysis of risk factors for the air-trapping asthmatic phenotype as measured by quantitative CT analysis. Chest. 2009;135:48–56.PubMedGoogle Scholar
  64. 64.
    Witt CA, Sheshadri A, Carlstrom L, et al. Longitudinal changes in airway remodelling and air trapping in severe asthma. Acad Radiol. 2014;21:986–93.PubMedPubMedCentralGoogle Scholar
  65. 65.
    Martin SP, Gariani J, Hachulla AL, et al. Impact of iterative reconstructions on objective and subjective emphysema assessment with computed tomography: a prospective study. Eur Radiol. 2017;27:2950–6.PubMedGoogle Scholar
  66. 66.
    Rodriguez A, Ranallo FN, Judy PF, Fain SB. The effects of iterative reconstruction and kernel selection on quantitative computed tomography measures of lung density. Med Phys. 2017;44:2267–80.PubMedPubMedCentralGoogle Scholar
  67. 67.
    Galban CJ, Han MK, Boes JL, et al. Computed tomography-based biomarker provides unique signature for diagnosis of COPD phenotypes and disease progression. Nat Med. 2012;18:1711–5.PubMedPubMedCentralGoogle Scholar
  68. 68.
    Kim HG, Tashkin DP, Clements PJ, et al. A computer-aided diagnosis system for quantitative scoring of extent of lung fibrosis in scleroderma patients. Clin Exp Rheumatol. 2010;28(Suppl 62):S26–35.PubMedPubMedCentralGoogle Scholar
  69. 69.
    Xu Y, van Beek EJ, Hwanjo Y, Guo J, McLennan G, Hoffman EA. Computer-aided classification of interstitial lung diseases via MDCT: 3D adaptive multiple feature method (3D AMFM). Acad Radiol. 2006;13:969–78.PubMedGoogle Scholar
  70. 70.
    Martinez FJ, de Andrade JA, Anstrom KJ, King TE Jr, Raghu G, Idiopathic Pulmonary Fibrosis Clinical Research Network. Randomized trial of acetylcysteine in idiopathic pulmonary fibrosis. N Engl J Med. 2014;370:2093–101.PubMedPubMedCentralGoogle Scholar
  71. 71.
    Salisbury ML, Lynch DA, van Beek EJR, et al. Idiopathic pulmonary fibrosis: the association between the adaptive multiple features method and fibrosis outcome. Am J Respir Crit Care Med. 2017;195:921–9.PubMedPubMedCentralGoogle Scholar
  72. 72.
    Humphries SM, Yagihashi K, Huckleberry J, et al. Idiopathic pulmonary fibrosis: data-driven textural analysis of extent of fibrosis at baseline and 15-month follow-up. Radiology. 2017;285:270–8.PubMedPubMedCentralGoogle Scholar
  73. 73.
    Jacob J, Bartholmai BJ, Rajagopalan S, et al. Automated quantitative computed tomography versus visual computed tomography scoring in idiopathic pulmonary fibrosis: validation against pulmonary function. J Thorac Imaging. 2016;31:304–11.PubMedGoogle Scholar
  74. 74.
    Jacob J, Bartholmai BJ, Rajagopalan S, et al. Serial automated quantitative CT analysis in idiopathic pulmonary fibrosis: functional correlations and comparison with changes in visual CT scores. Eur Radiol. 2018;28:1318–27.PubMedGoogle Scholar
  75. 75.
    Wu X, Kim GH, Barber D, et al. Computed tomographic biomarkers in idiopathic pulmonary fibrosis: the future of quantitative analysis. Am J Respir Crit Care Med. 2018;  https://doi.org/10.1164/rccm.201803-0444PP. [Epub ahead of print]PubMedGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Edinburgh ImagingUniversity of EdinburghEdinburghUK
  2. 2.Department of RadiologyRoyal Infirmary of EdinburghEdinburghUK

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