Abstract
Chronic obstructive pulmonary disease (COPD) is increasingly being recognized as a highly heterogeneous disorder, composed of varying pathobiology. Accurate detection of COPD subtypes by image biomarkers is urgently needed to enable individualized treatment, thus improving patient outcome. We adapted the parametric response map (PRM), a voxel-wise image analysis technique, for assessing COPD phenotype. We analyzed whole-lung computed tomography (CT) scans acquired at inspiration and expiration of 194 individuals with COPD from the COPDGene study. PRM identified the extent of functional small airways disease (fSAD) and emphysema as well as provided CT-based evidence that supports the concept that fSAD precedes emphysema with increasing COPD severity. PRM is a versatile imaging biomarker capable of diagnosing disease extent and phenotype while providing detailed spatial information of disease distribution and location. PRM's ability to differentiate between specific COPD phenotypes will allow for more accurate diagnosis of individual patients, complementing standard clinical techniques.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Rent or buy this article
Prices vary by article type
from$1.95
to$39.95
Prices may be subject to local taxes which are calculated during checkout
References
Buist, A.S. et al. The Burden of Obstructive Lung Disease Initiative (BOLD): rationale and design. COPD 2, 277–283 (2005).
Agusti, A. & Vestbo, J. Current controversies and future perspectives in COPD. Am. J. Respir. Crit. Care Med. 184, 507–513 (2011).
Regan, E.A. et al. Genetic epidemiology of COPD (COPDGene) study design. COPD 7, 32–43 (2010).
Han, M.K. et al. Chronic obstructive pulmonary disease phenotypes: the future of COPD. Am. J. Respir. Crit. Care Med. 182, 598–604 (2010).
Gevenois, P.A., de Maertelaer, V., De Vuyst, P., Zanen, J. & Yernault, J.C. Comparison of computed density and macroscopic morphometry in pulmonary emphysema. Am. J. Respir. Crit. Care Med. 152, 653–657 (1995).
Gevenois, P.A. et al. Comparison of computed density and microscopic morphometry in pulmonary emphysema. Am. J. Respir. Crit. Care Med. 154, 187–192 (1996).
Newman, K.B., Lynch, D.A., Newman, L.S., Ellegood, D. & Newell, J.D. Jr. Quantitative computed tomography detects air trapping due to asthma. Chest 106, 105–109 (1994).
Eda, S. et al. The relations between expiratory chest CT using helical CT and pulmonary function tests in emphysema. Am. J. Respir. Crit. Care Med. 155, 1290–1294 (1997).
Gorbunova, V. et al. Early detection of emphysema progression. Med. Image Comput. Comput. Assist. Interv. 13, 193–200 (2010).
Gorbunova, V. et al. Weight preserving image registration for monitoring disease progression in lung CT. Med. Image Comput. Comput. Assist. Interv. 11, 863–870 (2008).
Kubo, K. et al. Expiratory and inspiratory chest computed tomography and pulmonary function tests in cigarette smokers. Eur. Respir. J. 13, 252–256 (1999).
Matsuoka, S. et al. Quantitative assessment of air trapping in chronic obstructive pulmonary disease using inspiratory and expiratory volumetric MDCT. AJR Am. J. Roentgenol. 190, 762–769 (2008).
Matsuoka, S., Kurihara, Y., Yagihashi, K. & Nakajima, Y. Quantitative assessment of peripheral airway obstruction on paired expiratory/inspiratory thin-section computed tomography in chronic obstructive pulmonary disease with emphysema. J. Comput. Assist. Tomogr. 31, 384–389 (2007).
Reinhardt, J.M. et al. Registration-based estimates of local lung tissue expansion compared to xenon CT measures of specific ventilation. Med. Image Anal. 12, 752–763 (2008).
Li, B., Christensen, G.E., Hoffman, E.A., McLennan, G. & Reinhardt, J.M. Pulmonary CT image registration and warping for tracking tissue deformation during the respiratory cycle through 3D consistent image registration. Med. Phys. 35, 5575–5583 (2008).
Moffat, B.A. et al. Functional diffusion map: a noninvasive MRI biomarker for early stratification of clinical brain tumor response. Proc. Natl. Acad. Sci. USA 102, 5524–5529 (2005).
Galbán, C.J. et al. A feasibility study of parametric response map analysis of diffusion-weighted magnetic resonance imaging scans of head and neck cancer patients for providing early detection of therapeutic efficacy. Transl. Oncol. 2, 184–190 (2009).
Hamstra, D.A. et al. Functional diffusion map as an early imaging biomarker for high-grade glioma: correlation with conventional radiologic response and overall survival. J. Clin. Oncol. 26, 3387–3394 (2008).
Ma, B. et al. Voxel-by-voxel functional diffusion mapping for early evaluation of breast cancer treatment. Inf. Process. Med. Imaging 21, 276–287 (2009).
Reischauer, C. et al. Bone metastases from prostate cancer: assessing treatment response by using diffusion-weighted imaging and functional diffusion maps–initial observations. Radiology 257, 523–531 (2010).
Galbán, C.J. et al. The parametric response map is an imaging biomarker for early cancer treatment outcome. Nat. Med. 15, 572–576 (2009).
McDonough, J.E. et al. Small-airway obstruction and emphysema in chronic obstructive pulmonary disease. N. Engl. J. Med. 365, 1567–1575 (2011).
Kohansal, R., Soriano, J.B. & Agusti, A. Investigating the natural history of lung function: facts, pitfalls, and opportunities. Chest 135, 1330–1341 (2009).
Oga, T. et al. Multidimensional analyses of long-term clinical courses of asthma and chronic obstructive pulmonary disease. Allergol. Int. 59, 257–265 (2010).
Cazzola, M. et al. Outcomes for COPD pharmacological trials: from lung function to biomarkers. Eur. Respir. J. 31, 416–469 (2008).
Martinez, F.J., Donohue, J.F. & Rennard, S.I. The future of chronic obstructive pulmonary disease treatment–difficulties of and barriers to drug development. Lancet 378, 1027–1037 (2011).
Yamamoto, T. et al. Investigation of four-dimensional computed tomography-based pulmonary ventilation imaging in patients with emphysematous lung regions. Phys. Med. Biol. 56, 2279–2298 (2011).
Hu, S., Hoffman, E.A. & Reinhardt, J.M. Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images. IEEE Trans. Med. Imaging 20, 490–498 (2001).
Meyer, C.R. et al. Demonstration of accuracy and clinical versatility of mutual information for automatic multimodality image fusion using affine and thin-plate spline warped geometric deformations. Med. Image Anal. 1, 195–206 (1997).
Acknowledgements
We would like to acknowledge S. Sarkar, M. Bule and S.A. Blanks for their indispensable contribution in processing the CT data sets. We would also like to acknowledge D.A. Lynch and the COPDGene investigators for providing the CT scans from National Jewish Health and recruiting the subjects included in this analysis. This work was supported by the US National Institutes of Health research grant P50CA93990 and COPDGene grants U01HL089897 and U01HL089856. J.L.B. is a recipient of support from the US National Institutes of Health training grant T32EB005172.
Author information
Authors and Affiliations
Contributions
C.J.G. conducted data and statistical analyses and wrote the manuscript, M.K.H. acquired images, PFT data and clinical information from COPDGene, C.R.M. and J.L.B. optimized and performed image registrations, K.A.C. aided in image registration and performed PRM on image data, T.D.J. assisted with the statistical analysis, S.G. and A.R. contributed to the design of the study and E.A.K., F.J.M. and B.D.R. supervised the project, including data analysis and manuscript preparation.
Corresponding author
Ethics declarations
Competing interests
C.J.G., A.R. and B.D.R. have a financial interest in the underlying technology, which has been licensed from the University of Michigan to Imbio, LLC, in which A.R. and B.D.R. have a financial interest.
Supplementary information
Supplementary Text and Figures
Supplementary Note, Supplementary Tables 1–3, Supplementary Figures 1–4 and Supplementary Methods (PDF 1900 kb)
Rights and permissions
About this article
Cite this article
Galbán, C., Han, M., Boes, J. et al. Computed tomography–based biomarker provides unique signature for diagnosis of COPD phenotypes and disease progression. Nat Med 18, 1711–1715 (2012). https://doi.org/10.1038/nm.2971
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/nm.2971
This article is cited by
-
Structural features on quantitative chest computed tomography of patients with maximal mid-expiratory flow impairment in a normal lung function population
BMC Pulmonary Medicine (2023)
-
Principal component analysis of flow-volume curves in COPDGene to link spirometry with phenotypes of COPD
Respiratory Research (2023)
-
Computer-assisted evaluation of small airway disease in CT scans of Iran-Iraq war victims of chemical warfare by a locally developed software: comparison between different quantitative methods
BMC Medical Imaging (2023)
-
Deep learning parametric response mapping from inspiratory chest CT scans: a new approach for small airway disease screening
Respiratory Research (2023)
-
Micro-CT-derived ventilation biomarkers for the longitudinal assessment of pathology and response to therapy in a mouse model of lung fibrosis
Scientific Reports (2023)