Meta-analysis of the radiological and clinical features of Usual Interstitial Pneumonia (UIP) and Nonspecific Interstitial Pneumonia (NSIP)

Autoři: Lukas Ebner aff001;  Stergios Christodoulidis aff002;  Thomai Stathopoulou aff002;  Thomas Geiser aff003;  Odile Stalder aff004;  Andreas Limacher aff004;  Johannes T. Heverhagen aff001;  Stavroula G. Mougiakakou aff001;  Andreas Christe aff001
Působiště autorů: Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Switzerland aff001;  ARTORG Center for Biomedical Engineering Research, University of Bern, Switzerland aff002;  Department for Pulmonary Medicine, Inselspital, Bern University Hospital, University of Bern, Switzerland aff003;  CTU Bern and Institute of Social and Preventive Medicine (ISPM), University of Bern, Switzerland aff004
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article



To conduct a meta-analysis to determine specific computed tomography (CT) patterns and clinical features that discriminate between nonspecific interstitial pneumonia (NSIP) and usual interstitial pneumonia (UIP).

Materials and methods

The PubMed/Medline and Embase databases were searched for studies describing the radiological patterns of UIP and NSIP in chest CT images. Only studies involving histologically confirmed diagnoses and a consensus diagnosis by an interstitial lung disease (ILD) board were included in this analysis. The radiological patterns and patient demographics were extracted from suitable articles. We used random-effects meta-analysis by DerSimonian & Laird and calculated pooled odds ratios for binary data and pooled mean differences for continuous data.


Of the 794 search results, 33 articles describing 2,318 patients met the inclusion criteria. Twelve of these studies included both NSIP (338 patients) and UIP (447 patients). NSIP-patients were significantly younger (NSIP: median age 54.8 years, UIP: 59.7 years; mean difference (MD) -4.4; p = 0.001; 95% CI: -6.97 to -1.77), less often male (NSIP: median 52.8%, UIP: 73.6%; pooled odds ratio (OR) 0.32; p<0.001; 95% CI: 0.17 to 0.60), and less often smokers (NSIP: median 55.1%, UIP: 73.9%; OR 0.42; p = 0.005; 95% CI: 0.23 to 0.77) than patients with UIP. The CT findings from patients with NSIP revealed significantly lower levels of the honeycombing pattern (NSIP: median 28.9%, UIP: 73.4%; OR 0.07; p<0.001; 95% CI: 0.02 to 0.30) with less peripheral predominance (NSIP: median 41.8%, UIP: 83.3%; OR 0.21; p<0.001; 95% CI: 0.11 to 0.38) and more subpleural sparing (NSIP: median 40.7%, UIP: 4.3%; OR 16.3; p = 0.005; 95% CI: 2.28 to 117).


Honeycombing with a peripheral predominance was significantly associated with a diagnosis of UIP. The NSIP pattern showed more subpleural sparing. The UIP pattern was predominantly observed in elderly males with a history of smoking, whereas NSIP occurred in a younger patient population.

Klíčová slova:

Computed axial tomography – Diagnostic medicine – Diagnostic radiology – Metaanalysis – Pneumonia – Pulmonary fibrosis – Radiologists – Social systems


1. Wells AU. Managing diagnostic procedures in idiopathic pulmonary fibrosis. Eur Respir Rev 2013; 22: 158–162. doi: 10.1183/09059180.00001213 23728870

2. American Thoracic Society, European Respiratory Society. ATS/ERS international multidisciplinary consensus classification of the idiopathic interstitial pneumonias. Am J Respir Crit Care Med 2002; 165: 277–304. doi: 10.1164/ajrccm.165.2.ats01 11790668

3. British Thoracic Society and Standards of Care Committee. The diagnosis, assessment and treatment of diffuse parenchymal lung disease in adults. Thorax 1999; 54: S24–S30. doi: 10.1136/thx.54.2008.s24

4. Nalysnyk L, Cid-Ruzafa J, Rotella P, Esser D. Incidence and prevalence of idiopathic pulmonary fibrosis: review of the literature. Eur Respir Rev 2012; 21: 355–361. doi: 10.1183/09059180.00002512 23204124

5. Meltzer EB, Noble PW. Idiopathic pulmonary fibrosis. Orphanet J Rare Dis 2008; 3: 8. doi: 10.1186/1750-1172-3-8 18366757

6. Travis WD, Hunninghake G, King TE Jr, Lynch DA, Colby TV, Galvin JR. Idiopathic nonspecific interstitial pneumonia: report of an American Thoracic Society project. Am J Respir Crit Care Med 2008; 177: 1338–1347. doi: 10.1164/rccm.200611-1685OC 18388353

7. Sluimer I, Schilham A, Prokop M, van Ginneken B. Computer analysis of computed tomography scans of the lung: a survey. IEEE Trans Med Imaging 2006; 25: 385–405. doi: 10.1109/TMI.2005.862753 16608056

8. Cocconcelli E, Balestro E, Biondini D, Barbiero G, Polverosi R, Calabrese F. High-Resolution Computed Tomography (HRCT) Reflects Disease Progression in Patients with Idiopathic Pulmonary Fibrosis (IPF): Relationship with Lung Pathology.J Clin Med. 2019 Mar 22;8(3).

9. Nakagawa H, Ogawa E, Fukunaga K, Kinose D, Yamaguchi M, Nagao Tet al. Quantitative CT analysis of honeycombing area predicts mortality in idiopathic pulmonary fibrosis with definite usual interstitial pneumonia pattern: A retrospective cohort study. PLoS One. 2019 Mar 21;14(3).

10. Humphries SM, Swigris JJ, Brown KK, Strand M, Gong Q, Sundy JS, et al. Quantitative high-resolution computed tomography fibrosis score: performance characteristics in idiopathic pulmonary fibrosis. Eur Respir J. 2018 Sep 17;52(3).

11. Raghu G, Collard HR, Egan JJ, Martinez FJ, Behr J, Brown KK, et al. An official ATS/ERS/JRS/ALAT statement: idiopathic pulmonary fibrosis: evidence-based guidelines for diagnosis and management. Am J Respir Crit Care Med 2011; 183: 788–824. doi: 10.1164/rccm.2009-040GL 21471066

12. Mink SN, Maycher B. Comparative manifestations and diagnostic accuracy of high-resolution computed tomography in usual interstitial pneumonia and nonspecific interstitial pneumonia. Curr Opin Pulm Med 2012; 18: 530–534. doi: 10.1097/MCP.0b013e3283568026 22759772

13. Ravaglia C, Wells AU, Tomassetti S, Gurioli C, Gurioli C, Dubini A et al.Diagnostic yield and risk/benefit analysis of trans-bronchial lung cryobiopsy in diffuse parenchymal lung diseases: a large cohort of 699 patients. BMC Pulm Med. 2019 Jan 16;19(1):16. doi: 10.1186/s12890-019-0780-3 30651103

14. Galli JA, Panetta NL, Gaeckle N, Martinez FJ, Moore B, Moore T et al. COMET investigators. Pneumothorax After Transbronchial Biopsy in Pulmonary Fibrosis: Lessons from the Multicenter COMET Trial. Lung. 2017 Oct;195(5):537–543. doi: 10.1007/s00408-017-0028-z 28623539

15. Lieberman S, Gleason J1, Ilyas MIM, Martinez F, Mehta JP, Savage EB. Assessing the Safety and Clinical Impact of Thoracoscopic Lung Biopsy in Patients with Interstitial Lung Disease. J Clin Diagn Res. 2017 Mar;11(3):OC57–OC59. doi: 10.7860/JCDR/2017/20281.9626 28511438

16. Biancosino C, Welker L, Krüger M, Bölükbas S, Bittmann I, Kirsten D. Diagnostic Gain from Surgical Biopsy for Interstitial Lung Disease—When is it Worth the Risk? Pneumologie. 2016 Mar;70(3):205–10. doi: 10.1055/s-0042-100551 26977754

17. Moher D, Liberati A, Tetzlaff J, Altman DG; PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med 2009; 151: 264–269. doi: 10.7326/0003-4819-151-4-200908180-00135 19622511

18. Raghu G, Rochwerg B, Zhang Y, Garcia CA, Azuma A, Behr J, et al. An official ATS/ERS/JRS/ALAT clinical practice guideline: treatment of idiopathic pulmonary fibrosis. An update of the 2011 clinical practice guideline. Am J Respir Crit Care Med 2015; 192: e3–e19. doi: 10.1164/rccm.201506-1063ST 26177183

19. Lynch DA, Sverzellati N, Travis WD, Brown KK, Colby TV, Galvin JR, et al. Diagnostic criteria for idiopathic pulmonary fibrosis: a Fleischner society white paper. Lancet Respir Med 2018; 6: 138–153. doi: 10.1016/S2213-2600(17)30433-2 29154106

20. Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 2011; 155: 529–536. doi: 10.7326/0003-4819-155-8-201110180-00009 22007046

21. Higgins J, Thompson S, Deeks J, Altman D. Statistical heterogeneity in systematic reviews of clinical trials: a critical appraisal of guidelines and practice. J Health Serv Res Policy 2002; 7: 51–61. doi: 10.1258/1355819021927674 11822262

22. Higgins JPT, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ 2003; 327:557–560. doi: 10.1136/bmj.327.7414.557 12958120

23. Kondoh Y, Taniguchi H, Yokoi T, Nishiyama O, Ohishi T, Kato T, et al. Cyclophosphamide and low-dose prednisolone in idiopathic pulmonary fibrosis and fibrosing nonspecific interstitial pneumonia. Eur Respir J 2005; 25: 528–533. doi: 10.1183/09031936.05.00071004 15738299

24. Sumikawa H, Johkoh T, Fujimoto K, Arakawa H, Colby TV, Fukuoka J, et al. Pathologically proved nonspecific interstitial pneumonia: CT pattern analysis as compared with usual interstitial pneumonia CT pattern. Radiology 2014; 272: 549–556. doi: 10.1148/radiol.14130853 24661246

25. Aalokken TM, Naalsund A, Mynarek G, Berstad AE, Solberg S, Strom EH, et al. Diagnostic accuracy of computed tomography and histopathology in the diagnosis of usual interstitial pneumonia. Acta Radiol 2012; 53: 296–302. doi: 10.1258/ar.2011.110482 22334869

26. Sumikawa H, Johkoh T, Fujimoto K, Ichikado K, Colby TV, Fukuoka J, et al. Usual interstitial pneumonia and nonspecific interstitial pneumonia: correlation between CT findings at the site of biopsy with pathological diagnoses. Eur J Radiol 2012; 81: 2919–2924. doi: 10.1016/j.ejrad.2011.11.018 22169358

27. Akira M, Inoue Y, Kitaichi M, Yamamoto S, Arai T, Toyokawa K. Usual interstitial pneumonia and nonspecific interstitial pneumonia with and without concurrent emphysema: thin-section CT findings. Radiology 2009; 251: 271–279. doi: 10.1148/radiol.2511080917 19221055

28. Silva CI, Muller NL, Hansell DM, Lee KS, Nicholson AG, Wells AU. Nonspecific interstitial pneumonia and idiopathic pulmonary fibrosis: changes in pattern and distribution of disease over time. Radiology 2008; 247: 251–259. doi: 10.1148/radiol.2471070369 18270375

29. Sumikawa H, Johkoh T, Ichikado K, Taniguchi H, Kondoh Y, Fujimoto K, et al. Usual interstitial pneumonia and chronic idiopathic interstitial pneumonia: analysis of CT appearance in 92 patients. Radiology 2006; 241: 258–266. doi: 10.1148/radiol.2411050928 16908678

30. Tsubamoto M, Muller NL, Johkoh T, Ichikado K, Taniguchi H, Kondoh Y, et al. Pathologic subgroups of nonspecific interstitial pneumonia: differential diagnosis from other idiopathic interstitial pneumonias on high-resolution computed tomography. J Comput Assist Tomogr 2005; 29: 793–800. doi: 10.1097/01.rct.0000182853.90520.84 16272854

31. Jeong YJ, Lee KS, Muller NL, Chung MP, Chung MJ, Han J, et al. Usual interstitial pneumonia and non-specific interstitial pneumonia: serial thin-section CT findings correlated with pulmonary function. Korean J Radiol 2005; 6: 143–152. doi: 10.3348/kjr.2005.6.3.143 16145289

32. Elliot TL, Lynch DA, Newell JD Jr., Cool C, Tuder R, Markopoulou K, et al. High-resolution computed tomography features of nonspecific interstitial pneumonia and usual interstitial pneumonia. J Comput Assist Tomogr 2005; 29: 339–345. doi: 10.1097/01.rct.0000162153.55253.d3 15891504

33. MacDonald SL, Rubens MB, Hansell DM, Copley SJ, Desai SR, du Bois RM, et al. Nonspecific interstitial pneumonia and usual interstitial pneumonia: comparative appearances at and diagnostic accuracy of thin-section CT. Radiology 2001; 221: 600–605. doi: 10.1148/radiol.2213010158 11719652

34. Nagai S, Kitaichi M, Itoh H, Nishimura K, Izumi T, Colby TV. Idiopathic nonspecific interstitial pneumonia/fibrosis: comparison with idiopathic pulmonary fibrosis and BOOP. Eur Respir J 1998; 12: 1010–1019. doi: 10.1183/09031936.98.12051010 9863989

35. Raghu G, Remy-Jardin M, Myers JL, Richeldi L, Ryerson CJ, Lederer DJ,et al. Diagnosis of Idiopathic Pulmonary Fibrosis: An Official ATS/ERS/JRS/ALAT Clinical Practice Guideline Am J Respir Crit Care Med Vol 198, Iss 5, pp e44–e68, Sep 1, 2018. doi: 10.1164/rccm.201807-1255ST 30168753

36. Uchiyama Y, Katsuragawa S, Abe H, Shiraishi J, Li F, Li Q, et al. Quantitative computerized analysis of diffuse lung disease in high-resolution computed tomography. Med Phys 2003; 30: 2440–2454. doi: 10.1118/1.1597431 14528966

37. Kim KG, Goo JM, Kim JH, Lee HJ, Min BG, Bae KT, et al. Computer-aided diagnosis of localized ground-glass opacity in the lung at CT: initial experience. Radiology 2005; 237: 657–661. doi: 10.1148/radiol.2372041461 16192320

38. Sluimer IC, Prokop M, Hartmann I, van Ginneken B. Automated classification of hyperlucency, fibrosis, ground glass, solid, and focal lesions in high-resolution CT of the lung. Med Phys 2006; 33: 2610–2620. doi: 10.1118/1.2207131 16898465

39. Zavaletta VA, Bartholmai BJ, Robb RA. High resolution multidetector CT-aided tissue analysis and quantification of lung fibrosis. Acad Radiol 2007; 14: 772–787. doi: 10.1016/j.acra.2007.03.009 17574128

40. Korfiatis P, Kalogeropoulou C, Karahaliou A, Kazantzi A, Skiadopoulos S, Costaridou L. Texture classification-based segmentation of lung affected by interstitial pneumonia in high-resolution CT. Med Phys 2008; 35: 5290–5302. doi: 10.1118/1.3003066 19175088

41. Vo KT, Sowmya A. Multiple kernel learning for classification of diffuse lung disease using HRCT lung images. Conf Proc IEEE Eng Med Biol Soc 2010; 2010: 3085–3088. doi: 10.1109/IEMBS.2010.5626113 21095740

42. Depeursinge A, van de Ville D, Platon A, Geissbuhler A, Poletti PA, Muller H. Near-affine-invariant texture learning for lung tissue analysis using isotropic wavelet frames. IEEE Trans Inf Technol Biomed 2012; 16: 665–675. doi: 10.1109/TITB.2012.2198829 22588617

43. Song Y, Cai W, Zhou Y, Feng DD. Feature-based image patch approximation for lung tissue classification. IEEE Trans Med Imaging 2013; 32: 797–808. doi: 10.1109/TMI.2013.2241448 23340591

44. Heitmann KR, Kauczor H, Mildenberger P, Uthmann T, Perl J, Thelen M. Automatic detection of ground glass opacities on lung HRCT using multiple neural networks. Eur Radiol 1997; 7: 1463–1472. doi: 10.1007/s003300050318 9369516

45. Delorme S, Keller-Reichenbecher MA, Zuna I, Schlegel W, van Kaick G. Usual interstitial pneumonia. Quantitative assessment of high-resolution computed tomography findings by computer-assisted texture-based image analysis. Invest Radiol 1997; 32: 566–574. doi: 10.1097/00004424-199709000-00009 9291045

46. Anthimopoulos MM, Christodoulidis S, Ebner L, Geiser T, Christe A, Mougiakakou SG. Semantic segmentation of pathological lung tissue with dilated fully convolutional networks. IEEE J Biomed Health Inform 2018.

47. Christodoulidis S, Anthimopoulos M, Ebner L, Christe A, Mougiakakou S. Multisource transfer learning with convolutional neural networks for lung pattern analysis. IEEE J Biomed Health Inform 2017; 21: 76–84. doi: 10.1109/JBHI.2016.2636929 28114048

48. Anthimopoulos M, Christodoulidis S, Ebner L, Christe A, Mougiakakou S. Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans Med Imaging 2016; 35: 1207–1216. doi: 10.1109/TMI.2016.2535865 26955021

Článek vyšel v časopise


2020 Číslo 1
Nejčtenější tento týden