Skip to main content
SpringerLink
Log in

Minimum perceivable size difference: how well can radiologists visually detect a change in lung nodule size from CT images?

  • Chest
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objective

The purpose of this study was to determine how well radiologists could visually detect a change in lung nodule size on the basis of visual image perception alone.

Subjects and methods

Under IRB approval, 109 standard chest CT image series were anonymized and exported from PACS. Nine hundred forty virtual lung nodule pairs (six baseline diameters, six relative volume differences, two nodule types—solid and ground glass—and 14 repeats) were digitally inserted into the chest CT image series (same location, different sizes between the pair). These digitally altered CT image pairs were shown to nine radiologists who were tasked to visually determine which image contained the larger nodule using a two-alternative forced-choice perception experimental design. These data were statistically analyzed using a generalized linear mixed effects model to determine how accurately the radiologists were able to correctly identify the larger nodule.

Results

Nominal baseline nodule diameter, relative volume difference, and nodule type were found to be statistically significant factors (p < 0.001) in influencing the radiologists’ accuracy. For solid (ground-glass) nodules, the baseline diameter needed to be at least 6.3 mm (13.2 mm) to be able to visually detect a 25% change in volume with 95 ± 1.4% accuracy. Accuracy was lowest for the nodules with the smallest baseline diameters and smallest relative volume differences. Additionally, accuracy was lower for ground-glass nodules compared to solid nodules.

Conclusions

Factors that impacted visual size assessment were baseline nodule diameter, relative volume difference, and solid versus non-solid nodule type, with larger and more solid lesions offering a more precise assessment of change.

Key Points

• For solid nodules, radiologists could visually detect a 25% change in volume with 95% accuracy for nodules having greater than 6.3-mm baseline diameter.

• For ground-glass nodules, radiologists could visually detect a 25% change in volume with 95% accuracy for nodules having greater than 13.2-mm baseline diameter.

• Accuracy in detecting a change in nodule size began to stabilize around 90–100% for nodules with larger baseline diameters (> 8 mm for solid nodules, > 12 mm for ground-glass nodules) and larger relative volume differences (>15% for solid nodules, > 25% for ground-glass nodules).

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

Access this article

Log in via an institution

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

Similar content being viewed by others

Abbreviations

2AFC:

Two-alternative forced-choice

References

  1. National Lung Screening Trial Research Team, Aberle DR, Adams AM et al (2011) Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med 365:395–409

    Article  Google Scholar 

  2. Siegel RL, Miller KD, Jemal A (2019) Cancer statistics, 2019. CA Cancer J Clin 69:7–34

    Article  Google Scholar 

  3. Yousaf-Khan U, van der Aalst C, de Jong PA et al (2017) Final screening round of the NELSON lung cancer screening trial: the effect of a 2.5-year screening interval. Thorax 72:48–56

    Article  Google Scholar 

  4. Bankier AA, MacMahon H, Goo JM, Rubin GD, Schaefer-Prokop CM, Naidich DP (2017) Recommendations for measuring pulmonary nodules at CT: a statement from the Fleischner Society. Radiology 285:584–600

    Article  Google Scholar 

  5. Callister ME, Baldwin DR, Akram AR et al (2015) British Thoracic Society guidelines for the investigation and management of pulmonary nodules. Thorax 70(Suppl 2):ii1–ii54

    Article  Google Scholar 

  6. Devaraj A, van Ginneken B, Nair A, Baldwin D (2017) Use of volumetry for lung nodule management: theory and practice. Radiology 284:630–644

    Article  Google Scholar 

  7. van Klaveren RJ, Oudkerk M, Prokop M et al (2009) Management of lung nodules detected by volume CT scanning. N Engl J Med 361:2221–2229

    Article  Google Scholar 

  8. Wilson DO, Ryan A, Fuhrman C, Schuchert M, Shapiro S, Siegfried JM, Weissfeld J (2012) Doubling times and CT screen–detected lung cancers in the Pittsburgh Lung Screening Study. Am J Respir Crit Care Med 185:85–89

    Article  Google Scholar 

  9. Scholten ET, de Hoop B, Jacobs C et al (2013) Semi-automatic quantification of subsolid pulmonary nodules: comparison with manual measurements. PLoS One 8:e80249

  10. Strasburger H, Huber J, Rose D (2018) Ewald Hering’s (1899) On the limits of visual acuity: a translation and commentary: with a supplement on Alfred Volkmann’s (1863) physiological investigations in the field of optics. Iperception 9:2041669518763675

    PubMed  PubMed Central  Google Scholar 

  11. Field JK, Duffy SW, Baldwin DR et al (2016) The UK Lung Cancer Screening Trial: a pilot randomised controlled trial of low-dose computed tomography screening for the early detection of lung cancer. Health Technol Assess 20:1–146

    Article  Google Scholar 

  12. Pastorino U, Rossi M, Rosato V et al (2012) Annual or biennial CT screening versus observation in heavy smokers: 5-year results of the MILD trial. Eur J Cancer Prev 21:308–315

    Article  Google Scholar 

  13. Solomon J, Samei E (2014) A generic framework to simulate realistic lung, liver and renal pathologies in CT imaging. Phys Med Biol 59:6637–6657

    Article  Google Scholar 

  14. Singh S, Pinsky P, Fineberg NS et al (2011) Evaluation of reader variability in the interpretation of follow-up CT scans at lung cancer screening. Radiology 259:263–270

  15. Revel MP, Bissery A, Bienvenu M, Aycard L, Lefort C, Frija G (2004) Are two-dimensional CT measurements of small noncalcified pulmonary nodules reliable. Radiology 231:453–458

    Article  Google Scholar 

Download references

Funding

This study did not have funding.

Author information

Authors and Affiliations

  1. Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, NC, USA

    Justin Solomon, Taylor Richards & Ehsan Samei

  2. Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland

    Lukas Ebner, Andreas Christe, Alan Peters, Jaro Munz, Laura Löbelenz & Jeremias Klaus

  3. Institute of Radiology and Nuclear Medicine, Luzerner Kantonsspital, Lucerne, Canton of Lucerne, Switzerland

    Justus E. Roos

Authors
  1. Justin Solomon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lukas Ebner
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andreas Christe
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alan Peters
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jaro Munz
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Laura Löbelenz
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jeremias Klaus
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Taylor Richards
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ehsan Samei
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Justus E. Roos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Justin Solomon.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Ehsan Samei.

Conflict of interest

The authors of this manuscript declare relationships with the following companies:

  • Justin Solomon has relationships with the following entities, unrelated to the present publication: 12 Sigma, Sun Nuclear, Metis Health Analytics.

  • Ehsan Samei has relationships with the following entities unrelated to the present publication: GE, Siemens, Bracco, Imalogix, 12 Sigma, Sun Nuclear, Metis Health Analytics, Cambridge University Press, and Wiley and Sons.

Statistics and biometry

Justin Solomon and Ehsan Samei have statistical expertise and provided the analysis.

Informed consent

Written informed consent was waived by the Institutional Review Board because this was a retrospective study on anonymized image data.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• Retrospective

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Lukas Ebner is a co-first author.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Solomon, J., Ebner, L., Christe, A. et al. Minimum perceivable size difference: how well can radiologists visually detect a change in lung nodule size from CT images?. Eur Radiol 31, 1947–1955 (2021). https://doi.org/10.1007/s00330-020-07326-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-020-07326-2

Keywords

Search

Navigation