Multicenter study of quantitative computed tomography analysis using a computer-aided three-dimensional system in patients with idiopathic pulmonary fibrosis

Japanese Journal of Radiology

Volumn 34, Issue 1, 2016, Pages 16-27

  Iwasawa, Tae ^a   Kanauchi, Tetsu ^b   Hoshi, Toshiko ^b,c   Ogura, Takashi ^a   Baba, Tomohisa ^a   Gotoh, Toshiyuki ^d   Oba, Mari S ^e  

^a Kanagawa Prefectural Cardiovascular and Respiratory Diseases Center Hospital   (Japan)

^b Saitama Cardiovascular and Respiratory Center ^*  (Japan)

^c Saitama Jikei Hospital   (Japan)

^d YOKOHAMA NATIONAL UNIVERSITY   (Japan)

^e YOKOHAMA CITY UNIVERSITY   (Japan)

Author keywords

Computer aided design; Lung; Lung disease; Multidetector computed tomography; Pulmonary fibrosis

Indexed keywords

ADULT; AGED; ARTICLE; BLAND ALTMAN PLOT; COMPARATIVE STUDY; COMPUTED TOMOGRAPHY SCANNER; COMPUTER AIDED THREE DIMENSIONAL SYSTEM; COMPUTER ASSISTED TOMOGRAPHY; COMPUTER SYSTEM; CORRELATION ANALYSIS; CORRELATION COEFFICIENT; FEMALE; FIBROSING ALVEOLITIS; FORCED VITAL CAPACITY; GAUSSIAN HISTOGRAM NORMALIZED CORRELATION; HUMAN; KAPPA ANALYSIS; MAJOR CLINICAL STUDY; MALE; MULTICENTER STUDY; MULTIDETECTOR COMPUTED TOMOGRAPHY; MULTIVARIATE ANALYSIS; PRIORITY JOURNAL; PROSPECTIVE STUDY; SCORING SYSTEM; STATISTICAL ANALYSIS; CLINICAL TRIAL; DIAGNOSTIC IMAGING; FEASIBILITY STUDY; LUNG; MIDDLE AGED; PROCEDURES; THREE DIMENSIONAL IMAGING; VERY ELDERLY; X-RAY COMPUTED TOMOGRAPHY;

AGED; AGED, 80 AND OVER; FEASIBILITY STUDIES; FEMALE; HUMANS; IDIOPATHIC PULMONARY FIBROSIS; IMAGING, THREE-DIMENSIONAL; LUNG; MALE; MIDDLE AGED; PROSPECTIVE STUDIES; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84955644218     PISSN: 18671071     EISSN: 1867108X     Source Type: Journal    
DOI: 10.1007/s11604-015-0496-0     Document Type: Article

References (35)

1. Raghu G, Collard HR, Egan JJ, Martinez FJ, Behr J, Brown KK, ATS/ERS/JRS/ALAT Committee on Idiopathic Pulmonary Fibrosis, 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.
2. Azuma A, Nukiwa T, Tsuboi E, Suga M, Abe S, Nakata K, et al. Double-blind, placebo-controlled trial of pirfenidone in patients with idiopathic pulmonary fibrosis. Am J Respir Crit Care Med. 2005;171:1040–7.
3. Taniguchi H, Ebina M, Kondoh Y, Ogura T, Azuma A, Suga M, Pirfenidone Clinical Study Group in Japan, et al. Pirfenidone in idiopathic pulmonary fibrosis. Eur Respir J. 2010;35:821–9.
4. Richeldi L, du Bois RM, Raghu G, Azuma A, Brown KK, Costabe U, et al. INPULSIS Trial Investigators. Efficacy and safety of nintedanib in idiopathic pulmonary fibrosis. N Engl J Med. 2014;370:2071–82.
5. King TE Jr, Bradford WZ, Castro-Bernardini S, Fagan EA, Glaspole I, Glassberg MK, ASCEND Study Group, et al. A phase 3 trial of pirfenidone in patients with idiopathic pulmonary fibrosis. N Engl J Med. 2014;370:2083–92.
6. Noble PW, Albera C, Bradford WZ, Costabel U, Glassberg MK, Kardatzke D, CAPACITY Study Group, et al. Pirfenidone in patients with idiopathic pulmonary fibrosis (CAPACITY): two randomised trials. Lancet. 2011;377:1760–9.
7. Lynch DA, Travis WD, Müller NL, Galvin JR, Hansell DM, Grenier PA, et al. Idiopathic interstitial pneumonias: CT features. Radiology. 2005;236:10–21.
8. Fell CD, Martinez FJ, Liu LX, Murray S, Han MK, Kazerooni EA, et al. Clinical predictors of a diagnosis of idiopathic pulmonary fibrosis. Am J Respir Crit Care Med. 2010;181:832–7.
9. Sumikawa H, Johkoh T, Colby TV, Ichikado K, Suga M, Taniguchi H, et al. Computed tomography findings in pathological usual interstitial pneumonia: relationship to survival. Am J Respir Crit Care Med. 2008;177:433–9.
10. Maldonado F, Moua T, Rajagopalan S, Karwoski RA, Raghunath S, Decker PA, et al. Automated quantification of radiological patterns predicts survival in idiopathic pulmonary fibrosis. Eur Respir J. 2014;43:204–12.
11. Ley B, Elicker BM, Hartman TE, Ryerson CJ, Vittinghoff E, Ryu JH, et al. Idiopathic pulmonary fibrosis: CT and risk of death. Radiology. 2014;273:570–9.
12. Barr RG, Berkowitz EA, Bigazzi F, Bode F, Bon J, Bowler RP, COPDGene CT Workshop Group, et al. A combined pulmonary–radiology workshop for visual evaluation of COPD: study design, chest CT findings and concordance with quantitative evaluation. COPD. 2012;9:151–9.
13. Watadani T, Sakai F, Johkoh T, Noma S, Akira M, Fujimoto K, et al. Interobserver variability in the CT assessment of honeycombing in the lungs. Radiology. 2013;266:936–44.
14. Park SO, Seo JB, Kim N, Park SH, Lee YK, Park BW, et al. Feasibility of automated quantification of regional disease patterns depicted on high-resolution computed tomography in patients with various diffuse lung diseases. Korean J Radiol. 2009;10:455–63.
15. Hoffman EA, Reinhardt JM, Sonka M, Simon BA, Guo J, Saba O, et al. Characterization of the interstitial lung diseases via density-based and texture-based analysis of computed tomography images of lung structure and function. Acad Radiol. 2003;10:1104–18.
16. Korfiatis PD, Karahaliou AN, Kazantzi AD, Kalogeropoulou C, Costaridou LI. Texture-based identification and characterization of interstitial pneumonia patterns in lung multidetector CT. IEEE Trans Inf Technol Biomed. 2010;14:675–80.
17. Uppaluri R, Hoffman EA, Sonka M, Hunninghake GW, McLennan G. Interstitial lung disease: a quantitative study using the adaptive multiple feature method. Am J Respir Crit Care Med. 1999;159:519–25.
18. Zavaletta VA, Bartholmai BJ, Robb RA. High resolution multidetector CT-aided tissue analysis and quantification of lung fibrosis. Acad Radiol. 2007;14:772–87.
19. Rosas IO, Yao J, Avila NA, Chow CK, Gahl WA, Gochuico BR. Automated quantification of high-resolution CT scan findings in individuals at risk for pulmonary fibrosis. Chest. 2011;140:1590–7.
20. Wang J, Li F, Li Q. Automated segmentation of lungs with severe interstitial lung disease in CT. Med Phys. 2009;36:4592–9.
21. Kauczor HU, Heitmann K, Heussel CP, Marwede D, Uthmann T, Thelen M. Automatic detection and quantification of ground-glass opacities on high-resolution CT using multiple neural networks: comparison with a density mask. AJR Am J Roentgenol. 2000;175:1329–34.
22. Yoon RG, Seo JB, Kim N, Lee HJ, Lee SM, Lee YK, et al. Quantitative assessment of change in regional disease patterns on serial HRCT of fibrotic interstitial pneumonia with texture-based automated quantification system. Eur Radiol. 2013;23:692–701.
23. 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;33:2610–20.
24. Raghunath S, Rajagopalan S, Karwoski RA, Maldonado F, Peikert T, Moua T, et al. Quantitative stratification of diffuse parenchymal lung diseases. PLoS One. 2014;9:e93229.
25. Asakura A, Gotoh T, Iwasawa T, Saito K, Akasaka H. Classification system of the CT images with nonspecific interstitial pneumonia. J Inst Image Electron Eng Jpn. 2004;33:180–8.
26. Iwao Y, Gotoh T, Kagei S, Iwasawa T, Tsuzuki Mde S. Integrated lung field segmentation of injured region with anatomical structure analysis by failure–recovery algorithm from chest CT images. Biomed Signal Process Control. 2014;12:28–38.
27. Iwasawa T, Asakura A, Sakai F, Kanauchi T, Gotoh T, Ogura T, et al. Assessment of prognosis of patients with idiopathic pulmonary fibrosis by computer-aided analysis of CT images. J Thorac Imaging. 2009;24:216–22.
28. Iwasawa T, Ogura T, Sakai F, Kanauchi T, Komagata T, Baba T, et al. CT analysis of the effect of pirfenidone in patients with idiopathic pulmonary fibrosis. Eur J Radiol. 2014;83:32–8.
29. Japanese Respiratory Society. Guideline for pulmonary function test. 1st ed. Tokyo: Medical View; 2004.
30. Best AC, Lynch AM, Bozic CM, Miller D, Grunwald GK, Lynch DA. Quantitative CT indexes in idiopathic pulmonary fibrosis: relationship with physiologic impairment. Radiology. 2003;228:407–14.
31. Lynch DA, Godwin JD, Safrin S, Starko KM, Hormel P, Brown KK, et al. Idiopathic Pulmonary Fibrosis Study Group. High-resolution computed tomography in idiopathic pulmonary fibrosis: diagnosis and prognosis. Am J Respir Crit Care Med. 2005;172:488–93.
32. 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.
33. Katzenstein AL. Idiopathic interstitial pneumonia. In: Katzenstein AL, editor. Katzenstein and Askin’s surgical pathology of non-neoplastic lung disease. 4th ed. Philadelphia: Saunders Elsevier; 2006. p. 51–84.
34. Iwasawa T, Iwao Y, Goto T, Asakura A, Komagata T, Ogura T, et al. Computer-aided segmentation system for 3D chest CT. Rinsho Hoshasen. 2012;57:41–7 (in Japanese).
35. Yamaguchi K, Mori M, Kawai A, Takasugi T, Oyamada Y, Koda E. Inhomogeneities of ventilation and the diffusing capacity to perfusion in various chronic lung diseases. Am J Respir Crit Care Med. 1997;156:86–93.