Skip to main content

Advertisement

Log in

Quantitative texture-based assessment of one-year changes in fibrotic reticular patterns on HRCT in scleroderma lung disease treated with oral cyclophosphamide

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

Abstract

Objectives

The Scleroderma Lung Study showed the efficacy of cyclophosphamide in modestly improving the forced vital capacity (FVC) compared with placebo over 1 year. Using changes in texture-based scores that quantify lung fibrosis as the percentage involvement of reticulation patterns, the effectiveness of cyclophosphamide was re-assessed by examining its impact on quantitative lung fibrosis (QLF).

Methods

Axial HRCT images were acquired (1-mm slice thickness, 10-mm increments) in the prone position at inspiration. A validated model for quantifying interstitial disease patterns was applied to images from 83 subjects at baseline and 12 months. Scores were calculated for six zones (upper, mid, lower of the right/left lung) and the whole lung. Average changes were compared. Correlations were performed between QLF and physiological and clinical scores.

Results

From the most severe zones identified at baseline, QLF scores decreased by 2.6% in the cyclophosphamide group, whereas they increased by 9.1% in the placebo group, leading to ~12% difference (p = 0.0027). Between-treatment difference in whole lung QLF was ~5% (p = 0.0190). Significant associations were observed between changes in QLF and FVC (r = −0.33), dyspnea score (r = −0.29), and consensus visual score (p = 0.0001).

Conclusions

QLF scores provide an objective quantitative tool for assessing treatment efficacy in scleroderma-related interstitial lung disease.

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.

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

Similar content being viewed by others

References

  1. Steen VD, Conte C, Owens GR, Medsger TA Jr (1994) Severe restrictive lung disease in systemic sclerosis. Arthritis Rheum 37:1283–1289

    Article  PubMed  CAS  Google Scholar 

  2. Steen VD, Medsger TA Jr (2000) Severe organ involvement in systemic sclerosis with diffuse scleroderma. Arthritis Rheum 43:2437–2444

    Article  PubMed  CAS  Google Scholar 

  3. Medsger TA Jr (2004) Classification, prognosis. In: Clements PJ, Furst DE (eds) Systemic Sclerosis. Lippincott, Williams and Wilkins, Philadelphia, pp 17–28

    Google Scholar 

  4. Bouros D, Wells AU, Nicholson AG, Colby TV, Polychronopoulos V, Pantelidis P, Haslam PL, Vassilakis DA, Black CM, du Bois RM (2002) Histopathologic subsets of fibrosing alveolitis in patients with systemic sclerosis and their relationship to outcome. Am J Respir Crit Care Med 165:1581–1586

    Article  PubMed  Google Scholar 

  5. Kim DS, Yoo B, Lee JS, Kim EK, Lim CM, Lee SD, Koh Y, Kim WS, Kim WD, Colby TV, Kitiaichi M (2002) The major histopathologic pattern of pulmonary fibrosis in scleroderma is nonspecific interstitial pneumonia. Sarcoidosis Vasc Diffuse Lung Dis 19:121–127

    PubMed  Google Scholar 

  6. Minai OA, Dweik RA, Arroliga AC (1998) Manifestations of scleroderma pulmonary disease. Clin Chest Med 19:713–731

    Article  PubMed  CAS  Google Scholar 

  7. Fischer A, Swigris JJ, Groshong SD, Cool CD, Sahin H, Lynch DA, Curran-Everett D, Gillis JZ, Meehan RT, Brown KK (2008) Clinically significant interstitial lung disease in limited scleroderma: histopathology, clinical features, and survival. Chest 134:601–605

    Article  PubMed  Google Scholar 

  8. Kim TS, Lee KS, Chung MP, Han J, Park JS, Hwang JH, Kwon OJ, Rhee CH (1998) Nonspecific interstitial pneumonia with fibrosis: high-resolution CT and pathologic findings. AJR Am J Roentgenol 171:1645–1650

    PubMed  CAS  Google Scholar 

  9. Johkoh T, Muller NL, Colby TV, Ichikado K, Taniguchi H, Kondoh Y, Fujimoto K, Kinoshita M, Arakawa H, Yamada H, Suga M, Ando M, Koyama M, Nakamura H (2002) Nonspecific interstitial pneumonia: correlation between thin-section CT findings and pathologic subgroups in 55 patients. Radiology 225:199–204

    Article  PubMed  Google Scholar 

  10. Hoyles RK, Ellis RW, Wellsbury J, Lees B, Newlands P, Goh NS, Roberts C, Desai S, Herrick AL, McHugh NJ, Foley NM, Pearson SB, Emery P, Veale DJ, Denton CP, Wells AU, Black CM, du Bois RM (2006) A multicenter, prospective, randomized, double-blind, placebo-controlled trial of treatment with corticosteroids and intravenous cyclophosphamide followed by oral azathioprine for the treatment of pulmonary fibrosis in scleroderma. Arthritis Rheum 54:3962–3970

    Article  PubMed  CAS  Google Scholar 

  11. Tashkin DP, Elashoff R, Clements PJ, Goldin J, Roth MD, Furst DE, Arriola E, Silver R, Strange C, Bolster M, Seibold JR, Riley DJ, Hsu VM, Varga J, Schraufnagel DE, Theodore A, Simms R, Wise R, Wigley F, White B, Steen V, Read C, Mayes M, Parsley E, Mubarak K, Connolly MK, Golden J, Olman M, Fessler B, Rothfield N, Metersky M, Scleroderma Lung Study Research Group (2006) Cyclophosphamide versus placebo in scleroderma lung disease. N Engl J Med 354:2655–2666

    Article  PubMed  CAS  Google Scholar 

  12. Goldin J, Elashoff R, Kim HJ, Yan X, Lynch D, Strollo D, Roth MD, Clements P, Furst DE, Khanna D, Vasunilashorn S, Li G, Tashkin DP (2009) Treatment of scleroderma-interstitial lung disease with cyclophosphamide is associated with less progressive fibrosis on serial thoracic high-resolution CT scan than placebo: findings from the scleroderma lung study. Chest 136:1333–1340

    Article  PubMed  CAS  Google Scholar 

  13. Martinez FJ, McCune WJ (2006) Cyclophosphamide for scleroderma lung disease. N Engl J Med 354:2707–2709

    Article  PubMed  CAS  Google Scholar 

  14. Morgenthau AS, Padilla ML (2009) Spectrum of fibrosing diffuse parenchymal lung disease. Mt Sinai J Med 76:2–23

    Article  PubMed  Google Scholar 

  15. Kim HJ, Li G, Gjertson D, Elashoff R, Shah SK, Ochs R, Vasunilashorn F, Abtin F, Brown MS, Goldin JG (2008) Classification of parenchymal abnormality in scleroderma lung using a novel approach to denoise images collected via a multicenter study. Acad Radiol 15:1004–1016

    Article  PubMed  Google Scholar 

  16. Kim HJ, Tashkin DP, Clements PJ, Li G, Brown MS, Elashoff R, Gjertson DW, Abtin F, Lynch DA, Strollo DC, Goldin JG (2010) A computer-aided diagnosis system for quantitative scoring of extent of lung fibrosis in scleroderma patients. Clin Exp Rheumatol 5:S26–S35

    Google Scholar 

  17. Collins CD, Wells AU, Hansell DM, Morgan RA, MacSweeney JE, du Bois RM, Rubens MB (1994) Observer variation in pattern type and extent of disease in fibrosing alveolitis on thin section computed tomography and chest radiography. Clin Radiol 49:236–240

    Article  PubMed  CAS  Google Scholar 

  18. Camiciottoli G, Orlandi I, Bartolucci M, Meoni E, Nacci F, Diciotti S, Barcaroli C, Conforti ML, Pistolesi M, Matucci-Cerinic M, Mascalchi M (2007) Lung CT densitometry in systemic sclerosis: correlation with lung function, exercise testing, and quality of life. Chest 131:672–681

    Article  PubMed  Google Scholar 

  19. Lynch DA (2007) Quantitative CT of fibrotic interstitial lung disease. Chest 131:643–644

    Article  PubMed  Google Scholar 

  20. Sumikawa H, Johkoh T, Yamamoto S, Yanagawa M, Inoue A, Honda O, Yoshida S, Tomiyama N, Nakamura H (2009) Computed tomography values calculation and volume histogram analysis for various computed tomographic patterns of diffuse lung diseases. J Comput Assist Tomogr 33:731–738

    Article  PubMed  Google Scholar 

  21. Uppaluri R, Hoffman EA, Sonka M, Hartley PG, Hunninghake GW, McLennan G (1999) Computer recognition of regional lung disease patterns. Am J Respir Crit Care Med 160:648–654

    PubMed  CAS  Google Scholar 

  22. Chabat F, Yang GZ, Hansell DM (2003) Obstructive lung diseases: texture classification for differentiation at CT. Radiology 228:871–877

    Article  PubMed  Google Scholar 

  23. Boehm HF, Fink C, Attenberger U, Becker C, Behr J, Reiser M (2008) Automated classification of normal and pathologic pulmonary tissue by topological texture features extracted from multi-detector CT in 3D. Eur Radiol 18:2745–2755

    Article  PubMed  CAS  Google Scholar 

  24. Depeursinge A, Iavindrasana J, Hidki A, Cohen G, Geissbuhler A, Platon A, Poletti PA, Muller H (2010) Comparative performance analysis of state-of-the-art classification algorithms applied to lung tissue categorization. Digit Imaging 23:18–30

    Article  Google Scholar 

  25. Zavaletta VA, Bartholmai BJ, Robb RA (2007) High resolution multidetector CT-aided tissue analysis and quantification of lung fibrosis. Acad Radiol 14:772–787

    Article  PubMed  Google Scholar 

  26. Iwasawa T, Asakura A, Sakai F, Kanauchi T, Gotoh T, Ogura T, Yazawa T, Nishimura J, Inoue T (2009) Assessment of prognosis of patients with idiopathic pulmonary fibrosis by computer-aided analysis of CT images. J Thorac Imaging 24:216–222

    Article  PubMed  Google Scholar 

  27. Arzhaeva Y, Prokop M, Murphy K, van Rikxoort EM, de Jong PA, Gietema HA, Viergever MA, van Ginneken B (2010) Automated estimation of progression of interstitial lung disease in CT images. Med Phys 37:63–73

    Article  PubMed  Google Scholar 

  28. Best AC, Meng J, Lynch AM, Bozic CM, Miller D, Grunwald GK, Lynch DA (2008) Idiopathic pulmonary fibrosis: physiologic tests, quantitative CT indexes, and CT visual scores as predictors of mortality. Radiology 246:935–940

    Article  PubMed  Google Scholar 

  29. Mahler DA, Weinberg DH, Wells CK, Feinstein AR (1984) The measurement of dyspnea: contents, interobserver agreement and physiologic correlates of two new clinical indexes. Chest 85:751–758

    Article  PubMed  CAS  Google Scholar 

  30. Goldin JG, Lynch DA, Strollo DC, Suh RD, Schraufnagel DE, Clements PJ, Elashoff RM, Furst DE, Vasunilashorn S, McNitt-Gray MF, Brown MS, Roth MD, Tashkin DP, Scleroderma Lung Study Research Group (2008) High-resolution CT scan findings in patients with symptomatic scleroderma-related interstitial lung disease. Chest 134:358–367

    Article  PubMed  Google Scholar 

  31. Bland JM, Altman DG (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1:307–310

    Article  PubMed  CAS  Google Scholar 

  32. Khanna D, Yan X, Tashkin DP, Furst DE, Elashoff R, Roth MD, Silver R, Strange C, Bolster M, Seibold JR, Riley DJ, Hsu VM, Varga J, Schraufnagel DE, Theodore A, Simms R, Wise R, Wigley F, White B, Steen V, Read C, Parsley E, Mubarak K, Connolly MK, Golden J, Olman M, Fessler B, Rothfield N, Metersky M, Clements PJ, Scleroderma Lung Study Group (2007) Impact of oral cyclophosphamide on health-related quality of life in patients with active scleroderma lung disease: results from the scleroderma lung study. Arthritis Rheum 56(5):1676–1684

    Google Scholar 

  33. Wells AU, Behr J, Silver R (2008) Outcome measures in the lung. Rheumatology 47(Suppl 5):v48–v50

    Article  PubMed  Google Scholar 

  34. Prentice RL (1989) Surrogate endpoints in clinical trials: definitions and operational criteria. Stat Med 8:431–440

    Article  PubMed  CAS  Google Scholar 

  35. Buyse M, Molenberghs G, Burzykowski T, Renard D, Geys H (2000) The validation of surrogate endpoints in meta-analyses of randomized experiments. Biostatistics 1:49–67

    Article  PubMed  Google Scholar 

  36. Altar CA, Amakye D, Bounos D, Bloom J, Clack G, Dean R, Devanarayan V, Fu D, Furlong S, Hinman L, Girman C, Lathia C, Lesko L, Madani S, Mayne J, Meyer J, Raunig D, Sager P, Williams SA, Wong P, Zerba K (2008) A prototypical process for creating evidentiary standards for biomarkers and diagnostics. Clin Pharmacol Ther 83:368–371

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgements

Supported by Public Health Service grants from the National Heart, Lung, and Blood Institute and the National Institute of Arthritis and Musculoskeletal and Skin Diseases; cyclophosphamide (Cytoxan) was supplied by Bristol-Myers Squibb.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hyun J. Kim.

Appendix

Appendix

figure a

Bland-Altman plot of quantitative lung fibrosis (QLF) score that was calculated repeatedly using a random sampling technique from 4 by 4 grid. a Mean difference was −0.008% (±0.36). The two dashed lines are 0.72% and −0.72%. For the outlying point where the difference was close to −2%, the patient had severe disease, with QLF score on the 1st run of 76.68% and 74.71% on the 2nd run. b Mean difference was 0.008% (±0.072). The two dashed lines are 0.14% and −0.14%. In Bland Altman plots, two boundaries are ±0.72% from the most severe zones and ±0.14% from the whole lung, indicating the small variation from a sampling technique from the 4 by 4 grid in the classifier model. Differences in QLF score greater than potential variation due to random sampling were demonstrated in 93% subjects (77/83) in the most severe zone at baseline and in 96% subjects (80/83) in the whole lung.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kim, H.J., Brown, M.S., Elashoff, R. et al. Quantitative texture-based assessment of one-year changes in fibrotic reticular patterns on HRCT in scleroderma lung disease treated with oral cyclophosphamide. Eur Radiol 21, 2455–2465 (2011). https://doi.org/10.1007/s00330-011-2223-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-011-2223-2

Keywords

Navigation