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European Radiology

, Volume 26, Issue 10, pp 3643–3652 | Cite as

Performance of ultralow-dose CT with iterative reconstruction in lung cancer screening: limiting radiation exposure to the equivalent of conventional chest X-ray imaging

  • Adrian HuberEmail author
  • Julia Landau
  • Lukas Ebner
  • Yanik Bütikofer
  • Lars Leidolt
  • Barbara Brela
  • Michelle May
  • Johannes Heverhagen
  • Andreas Christe
Chest

Abstract

Objective

To investigate the detection rate of pulmonary nodules in ultralow-dose CT acquisitions.

Materials and methods

In this lung phantom study, 232 nodules (115 solid, 117 ground-glass) of different sizes were randomly distributed in a lung phantom in 60 different arrangements. Every arrangement was acquired once with standard radiation dose (100 kVp, 100 references mAs) and once with ultralow radiation dose (80 kVp, 6 mAs). Iterative reconstruction was used with optimized kernels: I30 for ultralow-dose, I70 for standard dose and I50 for CAD. Six radiologists examined the axial 1-mm stack for solid and ground-glass nodules. During a second and third step, three radiologists used maximum intensity projection (MIPs), finally checking with computer-assisted detection (CAD), while the others first used CAD, finally checking with the MIPs.

Results

The detection rate was 95.5 % with standard dose (DLP 126 mGy*cm) and 93.3 % with ultralow-dose (DLP: 9 mGy*cm). The additional use of either MIP reconstructions or CAD software could compensate for this difference. A combination of both MIP reconstructions and CAD software resulted in a maximum detection rate of 97.5 % with ultralow-dose.

Conclusion

Lung cancer screening with ultralow-dose CT using the same radiation dose as a conventional chest X-ray is feasible.

Key points

93.3 % of all lung nodules were detected with ultralow-dose CT.

A sensitivity of 97.5 % is possible with additional image post-processing.

The radiation dose is comparable to a standard radiography in two planes.

Lung cancer screening with ultralow-dose CT is feasible.

Keywords

Computed tomography Pulmonary nodule detection Lung adenocarcinoma Ultralow-dose acquisition Diagnostic performance with low radiation dosage 

Abbreviations and acronyms

CAD

Computer-aided detection

MIP

Maximum intensity projection

Notes

Acknowledgments

The scientific guarantor of this publication is Dr. Andreas Christe. The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article. This study has received funding by the Bernese Cancer League, the Jubilee Foundation Swisslife and the Swiss Fight Against Cancer Foundation. The funders had no role in data collection, study design and analysis, preparation of the manuscript or decision to publish. One of the authors has significant statistical expertise. Institutional Review Board approval was not required because this was a phantom study. No study subjects or cohorts have been previously reported. Methodology: prospective, experimental, performed at one institution.

References

  1. 1.
    Jemal A, Bray F, Center MM et al (2011) Global cancer statistics. CA Cancer J Clin 61:69–90CrossRefPubMedGoogle Scholar
  2. 2.
    Kaneko M, Eguchi K, Ohmatsu H et al (1996) Peripheral lung cancer: screening and detection with low-dose spiral CT versus radiography. Radiology 201:798–802CrossRefPubMedGoogle Scholar
  3. 3.
    Henschke CI, Yankelevitz DF, Libby DM et al (2006) Survival of patients with stage I lung cancer detected on CT screening. N Engl J Med 355:1763–1771CrossRefPubMedGoogle Scholar
  4. 4.
    The National Lung Screening Trial Team (2011) Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med 365:395–409CrossRefGoogle Scholar
  5. 5.
    Field JK, van Klaveren R, Pedersen JH et al (2013) European randomized lung cancer screening trials: post NLST. J Surg Oncol 108:280–286CrossRefPubMedGoogle Scholar
  6. 6.
    van Iersel CA, de Koning HJ, Draisma G et al (2007) Risk-based selection from the general population in a screening trial: selection criteria, recruitment and power for the Dutch-Belgian randomised lung cancer multi-slice CT screening trial (NELSON). Int J Cancer 120:868–874CrossRefPubMedGoogle Scholar
  7. 7.
    Wender R, Fontham ETH, Barrera E et al (2013) American cancer society lung cancer screening guidelines. CA Cancer J Clin 63:106–117CrossRefGoogle Scholar
  8. 8.
    Jaklitsch MT, Jacobson FL, Austin JHM et al (2012) The American association for thoracic surgery guidelines for lung cancer screening using low-dose computed tomography scans for lung cancer survivors and other high-risk groups. J Thorac Cardiovasc Surg 144:33–38CrossRefPubMedGoogle Scholar
  9. 9.
    Nair A, Hansell DM (2011) European and North American lung cancer screening experience and implications for pulmonary nodule management. Eur Radiol 21:2445–2454CrossRefPubMedGoogle Scholar
  10. 10.
    Kauczor H-U, Bonomo L, Gaga M, et al. (2015) ESR/ERS white paper on lung cancer screening. Eur Respir J ERJ–00330–2015. doi:  10.1183/09031936.00033015
  11. 11.
    Mahadevia PJ, Fleisher LA, Frick KD et al (2003) Lung cancer screening with helical computed tomography in older adult smokers: a decision and cost-effectiveness analysis. JAMA J Am Med Assoc 289:313–322CrossRefGoogle Scholar
  12. 12.
    Swensen SJ, Jett JR, Sloan JA et al (2002) Screening for lung cancer with low-dose spiral computed tomography. Am J Respir Crit Care Med 165:508–513CrossRefPubMedGoogle Scholar
  13. 13.
    Brenner DJ (2004) Radiation risks potentially associated with low-dose CT screening of adult smokers for lung cancer. Radiology 231:440–445CrossRefPubMedGoogle Scholar
  14. 14.
    Baumueller S, Winklehner A, Karlo C et al (2012) Low-dose CT of the lung: potential value of iterative reconstructions. Eur Radiol 22:2597–2606CrossRefPubMedGoogle Scholar
  15. 15.
    Neroladaki A, Botsikas D, Boudabbous S et al (2013) Computed tomography of the chest with model-based iterative reconstruction using a radiation exposure similar to chest X-ray examination: preliminary observations. Eur Radiol 23:360–366CrossRefPubMedGoogle Scholar
  16. 16.
    Gordic S, Morsbach F, Schmidt B et al (2014) Ultralow-Dose chest computed tomography for pulmonary nodule detection: first performance evaluation of single energy scanning with spectral shaping. Invest Radiol 49:465–473CrossRefPubMedGoogle Scholar
  17. 17.
    Valencia R, Denecke T, Lehmkuhl L et al (2006) Value of axial and coronal maximum intensity projection (MIP) images in the detection of pulmonary nodules by multislice spiral CT: comparison with axial 1-mm and 5-mm slices. Eur Radiol 16:325–332CrossRefPubMedGoogle Scholar
  18. 18.
    Christe A, Leidolt L, Huber A et al (2013) 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 82:e873–e878CrossRefPubMedGoogle Scholar
  19. 19.
    Zhao Y, de Bock GH, Vliegenthart R et al (2012) Performance of computer-aided detection of pulmonary nodules in low-dose CT: comparison with double reading by nodule volume. Eur Radiol 22:2076–2084CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Ebner L, Bütikofer Y, Ott D et al (2015) Lung nodule detection by microdose CT versus chest radiography (standard and dual-energy subtracted). Am J Roentgenol. doi: 10.2214/AJR.14.12921 Google Scholar
  21. 21.
    ICRP (2007) The 2007 recommendations of the international commission on radiological protection. ICRP publication 103. Ann ICRP 37:1–332CrossRefGoogle Scholar
  22. 22.
    Zar JH (2010) Biostatistical analysis. Prentice-Hall/Pearson, Upper Saddle RiverGoogle Scholar
  23. 23.
    Wilcoxon F (1946) Individual comparisons of grouped data by ranking methods. J Econ Entomol 39:269CrossRefPubMedGoogle Scholar
  24. 24.
    Light RJ (1971) Measures of response agreement for qualitative data: some generalizations and alternatives. Psychol Bull 76:365–377CrossRefGoogle Scholar
  25. 25.
    Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33:159–174CrossRefPubMedGoogle Scholar
  26. 26.
    Schoonjans F, Zalata A, Depuydt CE, Comhaire FH (1995) MedCalc: a new computer program for medical statistics. Comput Methods Programs Biomed 48:257–262CrossRefPubMedGoogle Scholar
  27. 27.
    Veronesi G, Maisonneuve P, Spaggiari L et al (2014) Diagnostic performance of low-dose computed tomography screening for lung cancer over five years. J Thorac Oncol 9:935–939CrossRefPubMedGoogle Scholar
  28. 28.
    Doo KW, Kang E-Y, Yong HS et al (2014) Comparison of chest radiography, chest digital tomosynthesis and low dose MDCT to detect small ground-glass opacity nodules: an anthropomorphic chest phantom study. Eur Radiol 24:3269–3276CrossRefPubMedGoogle Scholar
  29. 29.
    Godoy MCB, Kim TJ, White CS et al (2013) Benefit of computer-aided detection analysis for the detection of subsolid and solid lung nodules on thin- and thick-section CT. Am J Roentgenol 200:74–83CrossRefGoogle Scholar
  30. 30.
    Li Q, Li F, Doi K (2008) Computerized detection of lung nodules in thin-section CT images by use of selective enhancement filters and an automated rule-based classifier. Acad Radiol 15:165–175CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Slattery MM, Foley C, Kenny D et al (2012) Long-term follow-up of non-calcified pulmonary nodules (<10 mm) identified during low-dose CT screening for lung cancer. Eur Radiol 22:1923–1928CrossRefPubMedGoogle Scholar
  32. 32.
    Henschke CI, Yankelevitz DF, Naidich DP et al (2004) CT screening for lung cancer: suspiciousness of nodules according to size on baseline scans. Radiology 231:164–168CrossRefPubMedGoogle Scholar
  33. 33.
    Travis WD, Brambilla E, Noguchi M et al (2011) International association for the study of lung cancer/American thoracic society/european respiratory society: international multidisciplinary classification of lung adenocarcinoma. Proc Am Thorac Soc 8:381–385CrossRefPubMedGoogle Scholar
  34. 34.
    MacMahon H, Austin JHM, Gamsu G et al (2005) Guidelines for management of small pulmonary nodules detected on CT Scans: a statement from the fleischner society. Radiology 237:395–400CrossRefPubMedGoogle Scholar
  35. 35.
    Horeweg N, van Rosmalen J, Heuvelmans MA et al (2014) 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 15:1332–1341CrossRefPubMedGoogle Scholar
  36. 36.
    Bach PB, Mirkin JN, Oliver TK et al (2012) Benefits and harms of ct screening for lung cancer: a systematic review. JAMA 307:2418–2429CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    McMahon PM, Kong CY, Bouzan C et al (2011) Cost-Effectiveness of CT screening for lung cancer in the U.S. J Thorac Oncol Off Publ Int Assoc Study Lung Cancer 6:1841–1848Google Scholar
  38. 38.
    Priola AM, Priola SM, Giaj-Levra M et al (2013) Clinical implications and added costs of incidental findings in an early detection study of lung cancer by using low-dose spiral computed tomography. Clin Lung Cancer 14:139–148CrossRefPubMedGoogle Scholar
  39. 39.
    Bergh KAM, Essink-Bot M-L, Borsboom GJJM, et al. (2010) Long-term effects of lung cancer CT screening on health-related quality of life (NELSON). Eur Respir J erj01234–2010. doi:  10.1183/09031936.00123410
  40. 40.
    Vansteenkiste J, Dooms C, Mascaux C, Nackaerts K (2012) Screening and early detection of lung cancer. Ann Oncol 23:x320–x327CrossRefPubMedGoogle Scholar

Copyright information

© European Society of Radiology 2016

Authors and Affiliations

  • Adrian Huber
    • 1
    • 2
    Email author
  • Julia Landau
    • 1
  • Lukas Ebner
    • 1
    • 3
  • Yanik Bütikofer
    • 1
  • Lars Leidolt
    • 1
  • Barbara Brela
    • 1
  • Michelle May
    • 1
  • Johannes Heverhagen
    • 1
  • Andreas Christe
    • 1
  1. 1.Department of Diagnostic, Interventional and Paediatric RadiologyUniversity Hospital Inselspital BernBernSwitzerland
  2. 2.Department of Polyvalent and Oncological RadiologyUniversity Hospital Pitié-SalpêtrièreParisFrance
  3. 3.Department of RadiologyDuke University Medical CenterDurhamUSA

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