Skip to main content

Advertisement

Log in

Diagnostic impact of digital tomosynthesis in oncologic patients with suspected pulmonary lesions on chest radiography

European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To assess the actual diagnostic impact of digital tomosynthesis (DTS) in oncologic patients with suspected pulmonary lesions on chest radiography (CXR).

Methods

A total of 237 patients (135 male, 102 female; age, 70.8 ± 10.4 years) with a known primary malignancy and suspected pulmonary lesion(s) on CXR and who underwent DTS were retrospectively identified. Two radiologists (experience, 10 and 15 years) analysed in consensus CXR and DTS images and proposed a diagnosis according to a confidence score: 1 or 2 = definitely or probably benign pulmonary or extrapulmonary lesion, or pseudolesion; 3 = indeterminate; 4 or 5 = probably or definitely pulmonary lesion. DTS findings were proven by CT (n = 114 patients), CXR during follow-up (n = 105) or histology (n = 18).

Results

Final diagnoses included 77 pulmonary opacities, 26 pulmonary scars, 12 pleural lesions and 122 pulmonary pseudolesions. DTS vs CXR presented a higher (P < 0.05) sensitivity (92 vs 15 %), specificity (91 vs 9 %), overall accuracy (92 vs 12 %), and diagnostic confidence (area under ROC, 0.997 vs 0.619). Mean effective dose of CXR vs DTS was 0.06 vs 0.107 mSv (P < 0.05).

Conclusions

DTS improved diagnostic accuracy and confidence in comparison to CXR alone in oncologic patients with suspected pulmonary lesions on CXR with only a slight, though significant, increase in radiation dose.

Key points

Digital tomosynthesis (DTS) improves accuracy of chest radiography (CXR) in oncologic patients.

DTS improves confidence of CXR in oncologic patients.

DTS allowed avoidance of CT in about 50 % of oncologic patients.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Abbreviations

CXR:

chest radiography

DTS:

digital tomosynthesis

CT:

computed tomography

References

  1. Erasmus JJ, Connolly JE, McAdams HP, Roggli VL (2000) Solitary pulmonary nodules: Part I. Morphologic evaluation for differentiation of benign and malignant lesions. Radiographics 20:43–58

    Article  CAS  PubMed  Google Scholar 

  2. Remy-Jardin M, Remy J, Giraud F, Marquette CH (1993) Pulmonary nodules: detection with thick-section spiral CT versus conventional CT. Radiology 187:513–520

    Article  CAS  PubMed  Google Scholar 

  3. McAdams HP, Samei E, Dobbins J, Tourassi GD, Ravin CE (2006) Recent advances in chest radiography 1. Radiology 241(3):663–683

    Article  PubMed  Google Scholar 

  4. Dobbins JT III, Godfrey DJ (2003) Digital x-ray tomosynthesis: current state of the art and clinical potential. Phys Med Biol 48:R65–R106

    Article  PubMed  Google Scholar 

  5. Dobbins JT, Mc Adams HP, Devon G, Li CM (2008) Digital tomosynthesis of the chest. J Thorac Imaging 23:86–92

    Article  PubMed  Google Scholar 

  6. Vikgren J, Zachrisson S, Svalkvist A et al (2008) Comparison of chest tomosynthesis and chest radiography for detection of pulmonary nodules: human observer study of clinical cases. Radiology 249:1034–1041

    Article  PubMed  Google Scholar 

  7. Quaia E, Baratella E, Cioffi V et al (2010) The value of digital tomosynthesis in the diagnosis of suspected pulmonary lesions on chest radiography: analysis of diagnostic accuracy and confidence. Acad Radiol 17:1267–1274

    Article  PubMed  Google Scholar 

  8. Quaia E, Baratella E, Cernic S et al (2012) Analysis of the impact of digital tomosynthesis on the radiological investigation of patients with suspected pulmonary lesions on chest radiography. Eur Radiol 22:1912–1922

    Article  PubMed  Google Scholar 

  9. Quaia E, Baratella E, Poillucci G, Kus S, Cioffi V, Cova MA (2013) Digital tomosynthesis as a problem-solving imaging technique to confirm or exclude potential thoracic lesions based on chest x-ray radiography. Acad Radiol 20:546–553

    Article  PubMed  Google Scholar 

  10. Quaia E, Grisi G, Baratella E et al (2014) Diagnostic imaging costs before and after digital tomosynthesis implementation in patient management after detection of suspected thoracic lesions on chest radiography. Insights Imaging 5:147–155

    Article  PubMed  PubMed Central  Google Scholar 

  11. Machida H, Yuhara T, Mori T, Ueno E, Moribe Y, Sabol JM (2010) Optimizing parameters for flat-panel detector digital tomosynthesis. Radiographics 30(2):549–562

    Article  PubMed  Google Scholar 

  12. Gomi T, Nakajima M, Fujiwara H, Umeda T (2011) Comparison of chest dual-energy subtraction digital tomosynthesis imaging and dual-energy subtraction radiography to detect simulated pulmonary nodules with and without calcifications a phantom study. Acad Radiol 18:191–196

    Article  PubMed  Google Scholar 

  13. Yamada Y, Jinzaki M, Hasegawa I et al (2011) Fast scanning tomosynthesis for the detection of pulmonary nodules: diagnostic performance compared with chest radiography using multidetector-row computed tomography as the reference. Invest Radiol 46:471–477

    Article  PubMed  Google Scholar 

  14. Hansell DM, Bankier A, Mac Mahon H et al (2008) Fleischner Society: glossary of terms for thoracic imaging. Radiology 246:697–722

    Article  PubMed  Google Scholar 

  15. Hasegawa M, Sone S, Takashima S et al (2000) Growth rate of small lung cancers detected on mass CT screening. Br J Radiol 73:1252–1259

    Article  CAS  PubMed  Google Scholar 

  16. Servomaa A, Tapiovaara M (1998) Organ dose calculation in medical X ray examinations by the program PCXMC. Radiat Prot Dosimetry 80:213–219

    Article  Google Scholar 

  17. Cristy M, Eckerman KR (1987) Specific absorbed fractions of energy at various ages from internal photon sources. I. Method. Publication No ORNL/TM-8381. Oak Ridge National Laboratory, Oak Ridge, TN

  18. Sabol JM (2009) A Monte Carlo estimation of effective dose in chest tomosynthesis. Med Phys 36:5480–5487

    Article  PubMed  Google Scholar 

  19. European guidelines on quality criteria for computed tomography. Report EUR 16262. Brussels, Belgium: European Commission, 1999. Available at http://www.drs.dk/guidelines/ct/quality/mainindex.htm. Accessed 10 Sep 2012

  20. Campbell MJ, Machin D (1999) Medical statistics, a commonsense approach. Wiley, Chichester, pp 85–89

    Google Scholar 

  21. Beck JR, Shultz EK (1986) The use of relative operating characteristic (ROC) curves in test performance evaluation. Arch Pathol Lab Med 110:13–20

    CAS  PubMed  Google Scholar 

  22. Hanley JA, McNeil BJ (1983) A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 148:839–443

    Article  CAS  PubMed  Google Scholar 

  23. Zhu X, Yu J, Huang Z (2004) Low-dose chest CT: optimizing radiation protection for patients. AJR Am J Roentgenol 183:809–816

    Article  PubMed  Google Scholar 

  24. Johnsson ÅA, Vikgren J, Bath M (2014) A retrospective study of chest tomosynthesis as a tool for optimizing the use of computed tomography resources and reducing patient radiation exposure. Acad Radiol 21:1427–1433

    Article  PubMed  Google Scholar 

  25. Christensen JD, Chiles C (2015) Low-dose computed tomographic screening for lung cancer. Clin Chest Med 36:147–160

    Article  PubMed  Google Scholar 

  26. Siegelman JW, Supanich MP, Gavrielides MA (2015) Pulmonary nodules with ground-glass opacity can be reliably measured with low-dose techniques regardless of iterative reconstruction: results of a phantom study. AJR Am J Roentgenol 204:1242–1247

    Article  PubMed  Google Scholar 

  27. Woon Do K, Kang EY, Yong HS, Ham SY, Lee KY, Choo JY (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 24:3269–3276

    Article  Google Scholar 

  28. Li B, Avinash GB (2007) Optimization of slice sensitivity profile for radiographic tomosynthesis. Med Phys 34:2907–2916

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

The scientific guarantor of this publication is Emilio Quaia, MD. 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. The authors state that this work has not received any funding. No complex statistical methods were necessary for this paper. Institutional review board approval was obtained. Written informed consent was obtained from all subjects (patients) in this study. Methodology: retrospective, observational. We thank Gerhard Brunst, Luc Katz, John Sabol and Katelyn Nye from GE Healthcare for the help they provided for technical questions about Definium x-ray system, reconstruction algorithm, and DR support.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Emilio Quaia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Quaia, E., Baratella, E., Poillucci, G. et al. Diagnostic impact of digital tomosynthesis in oncologic patients with suspected pulmonary lesions on chest radiography. Eur Radiol 26, 2837–2844 (2016). https://doi.org/10.1007/s00330-015-4104-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-015-4104-6

Keywords

Navigation