Computed tomography–based biomarker provides unique signature for diagnosis of COPD phenotypes and disease progression

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.

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Figure 1: Schematic diagram of the PRM method.
Figure 2: COPD phenotypes identified by PRM.
Figure 3: COPD progression as determined by PRM.
Figure 4: PRM as an imaging biomarker of COPD progression.

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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.

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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

Correspondence to Brian D Ross.

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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.

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Supplementary Note, Supplementary Tables 1–3, Supplementary Figures 1–4 and Supplementary Methods (PDF 1900 kb)

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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

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