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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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.
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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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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