Changes in quantitative parameters of pulmonary nonsolid nodule induced by lung inflation according to paired inspiratory and expiratory computed tomography imaging

  • Li Fan
  • QingChu Li
  • WenTing Tu
  • RuTan Chen
  • Yi Xia
  • Yu Pu
  • ZhaoBin LiEmail author
  • ShiYuan LiuEmail author



To evaluate quantitative parameters of nonsolid nodules on paired inspiratory and expiratory computed tomography (CT) and to examine whether these parameters are sensitive to lung inflation reflected by lung volume.


Thirty-three patients with 41 nonsolid nodules were included in this prospective study. Paired inspiratory and low-dose respiratory plain chest CT were performed. The volume and density of nonsolid nodule(s), both lungs, the right and left lung, and five lobes, were analyzed in inspiratory and expiratory CT scans. The ratio of expiratory to inspiratory parameters was calculated and labeled as parameter(E-I)/I. To standardize the changes in nonsolid nodule quantitative parameters, the ratio of nonsolid nodule parameter to lung parameter was also calculated. Quantitative parameters were compared between inspiratory and expiratory CT.


Nonsolid nodule volumes on expiratory CT were reduced by 19.8% ± 12.9%, while the density was increased by 11.4% ± 8.8%. The volume of nonsolid nodules was significantly greater on inspiratory compared with expiratory CT (p < 0.001). The density of nonsolid nodules was significantly greater on expiratory than inspiratory CT (p < 0.001). The volume(E-I)/I was significantly greater than density(E-I)/I both in nonsolid nodules and lung. The volume(E-I)/I and density(E-I)/I of nonsolid nodules were independent of size. The density(E-I)/I of nonsolid nodule was greater in the lower lobe than that in the upper lobe (p = 0.002).


Volume changes in nonsolid nodules were more sensitive than density changes in expiratory phase. The density of lower lobe nodules was more susceptible to respiration. Expiratory scanning is not recommended for quantification of nonsolid nodules and/or follow-up.

Key Points

• The nonsolid nodule volume on expiratory CT was reduced by 19.8% ± 12.9%.

• The nonsolid nodule density on expiratory CT was increased by 11.4% ± 8.8%.

• The volume (E-I)/I and density (E-I)/I of nonsolid nodules were independent of size.


Solitary pulmonary nodule Tomography, x-ray computed Computational biology 



Automatic exposure control


Computed tomography


Ipsilateral lung


Mean lung density





This study has received funding by the National Key R&D Program of China (grant numbers 2016YFE0103000 and 2017YFC1308703), the National Natural Science Foundation of China (grant Numbers 81871321 and 81370035), and the Youth Fund of the National Natural Science Foundation of China (grant number 81501618).

Compliance with ethical standards


The scientific guarantor of this publication is Prof. ShiYuan Liu.

Conflict of interest

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.

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.


• prospective

• experimental

• performed at one institution


  1. 1.
    Kim HY, Shim YM, Lee KS, Han J, Yi CA, Kim YK (2007) Persistent pulmonary nodular ground-glass opacity at thin-section CT: histopathologic comparisons. Radiology 245:267–275Google Scholar
  2. 2.
    Nakajima R, Yokose T, Kakinuma R, Nagai K, Nishiwaki Y, Ochiai A (2002) Localized pure ground-glass opacity on high resolution CT: histologic characteristics. J Comput Assist Tomogr 26:323–329Google Scholar
  3. 3.
    Yanagawa M, Johkoh T, Noguchi M et al (2017) Radiological prediction of tumor invasiveness of lung adenocarcinoma on thin-section CT. Medicine (Baltimore) 96:e6331CrossRefGoogle Scholar
  4. 4.
    Goo JM, Park CM, Lee HJ (2011) Ground-glass nodules on chest CT as imaging biomarkers in the management of lung adenocarcinoma. AJR Am J Roentgenol 196:533–543CrossRefGoogle Scholar
  5. 5.
    MacMahon H, Naidich DP, Goo JM et al (2017) Guidelines for management of incidental pulmonary nodules detected on CT images: from the Fleischner Society 2017. Radiology 284:228–243CrossRefGoogle Scholar
  6. 6.
    Naidich DP, Bankier AA, MacMahon H et al (2013) Recommendations for the management of nonsolid pulmonary nodules detected at CT: a statement from the Fleischner Society. Radiology 266:304–317CrossRefGoogle Scholar
  7. 7.
    Wang Y, Fan L, Liu SY, Li Q, Chen R (2016) Diagnostic value of CT target scanning combining with changing position for pulmonary nodule in special location. Journal of Practical Radiology 32:694–698Google Scholar
  8. 8.
    Lynch DA, Austin JH, Hogg JC et al (2015) CT-definable subtypes of chronic obstructive pulmonary disease: a statement of the Fleischner Society. Radiology 277:192–205CrossRefGoogle Scholar
  9. 9.
    Kim EY, Seo JB, Lee HJ et al (2015) Detailed analysis of the density change on chest CT of COPD using non-rigid registration of inspiration/expiration CT scans. Eur Radiol 25:541–549CrossRefGoogle Scholar
  10. 10.
    Fan L, Fang M, Li Z et al (2018) Radiomics signature: a biomarker for the preoperative discrimination of lung invasive adenocarcinoma manifesting as a ground-glass nodule. Eur Radiol.
  11. 11.
    Kishi K, Homma S, Kurosaki A et al (2004) Small lung tumors with the size of 1cm or less in diameter: clinical, radiological, and histopathological characteristics. Lung Cancer 44:43–51CrossRefGoogle Scholar
  12. 12.
    Rami-Porta R, Bolejack V, Crowley J et al (2015) The IASLC lung cancer staging project: proposals for the revisions of the T descriptors in the forthcoming eighth edition of the TNM classification for lung cancer. J Thorac Oncol 10:990–1003CrossRefGoogle Scholar
  13. 13.
    Yamashiro T, Matsuoka S, Bartholmai BJ et al (2010) Collapsibility of lung volume by paired inspiratory and expiratory CT scans: correlations with lung function and mean lung density. Acad Radiol 17:489–495CrossRefGoogle Scholar
  14. 14.
    Fan L, Liu SY, Xiao XS, Sun F (2010) Demonstration of pulmonary perfusion heterogeneity induced by gravity and lung inflation using arterial spin labeling. Eur J Radiol 73:249–254CrossRefGoogle Scholar
  15. 15.
    Protti A, Iapichino GE, Milesi M et al (2014) Validation of computed tomography for measuring lung weight. Intensive Care Med Exp 2:31CrossRefGoogle Scholar
  16. 16.
    West JB (2012) Respiratory physiology—the essentials, 9th edn. Lippincott Williams and Wilkins, BaltimoreGoogle Scholar
  17. 17.
    Alpert JB, Ko JP (2018) Management of incidental lung nodules: current strategy and rationale. Radiol Clin North Am 56:339–351CrossRefGoogle Scholar
  18. 18.
    Yu WS, Hong SR, Lee JG et al (2016) Three-dimensional ground glass opacity ratio in C T images can predict tumor invasiveness of stage Ia lung cancer. Yonsei Med J 57:1131–1138CrossRefGoogle Scholar
  19. 19.
    Sakakura N, Inaba Y, Yatabe Y et al (2016) Estimation of the pathological invasive size of pulmonary adenocarcinoma using high-resolution computed tomography of the chest: a consideration based on lung and mediastinal window settings. Lung Cancer 95:51–56CrossRefGoogle Scholar
  20. 20.
    Son JY, Lee HY, Kim JH et al (2016) Quantitative CT analysis of pulmonary ground-glass opacity nodules for distinguishing invasive adenocarcinoma from non–invasive or minimally invasive adenocarcinoma: the added value of using iodine mapping. Eur Radiol 26:43–54CrossRefGoogle Scholar
  21. 21.
    Kitami A, Sano F, Hayashi S et al (2016) Correlation between histological invasiveness and the computed tomography value in pure ground-glass nodules. Surg Today 46:593–598CrossRefGoogle Scholar
  22. 22.
    Hersh CP, Washko GR, Estépar RS et al (2013) Paired inspiratory-expiratory chest CT scans to assess for small airways disease in COPD. Respir Res 14:42CrossRefGoogle Scholar
  23. 23.
    Lo P, Young S, Kim HJ, Brown MS, McNitt-Gray MF (2016) Variability in CT lung-nodule quantification: effects of dose reduction and reconstruction methods on density and texture-based features. Med Phys 43:4854–4865Google Scholar
  24. 24.
    Doo KW, Kang EY, Yong HS, Woo OH, Lee KY, Oh YW (2014) Accuracy of lung nodule volumetry in low-dose CT with iterative reconstruction: an anthropomorphic thoracic phantom study. Br J Radiol 87:20130644Google Scholar
  25. 25.
    Maruyama S, Fukushima Y, Miyamae Y, Koizumi K (2018) Usefulness of model-based iterative reconstruction in semi-automatic volumetry for ground-glass nodules at ultra-low-dose CT: a phantom study. Radiol Phys Technol 11:235–241CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  1. 1.Department of Radiology, Changzheng HospitalSecond Military Medical UniversityShanghaiChina
  2. 2.Department of Radiation OncologyShanghai Jiao Tong University Affiliated Sixth People’s HospitalShanghaiChina

Personalised recommendations