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Acad Radiol. 2015 Feb;22(2):186-94. doi: 10.1016/j.acra.2014.08.015. Epub 2014 Nov 4.

Parametric response mapping monitors temporal changes on lung CT scans in the subpopulations and intermediate outcome measures in COPD Study (SPIROMICS).

Author information

1
Department of Radiology, University of Michigan, Center for Molecular Imaging, Ann Arbor, MI 48109.
2
Department of Biostatistics, University of Michigan, Center for Molecular Imaging, Ann Arbor, Michigan.
3
Department of Radiation Oncology, University of Michigan, Center for Molecular Imaging, Ann Arbor, MI.
4
Imbio, LLC, Minneapolis, Minnesota.
5
Department of Radiology, University of Iowa, Iowa City, Iowa.
6
Department of Medicine, Weill Cornell Medical College, New York, New York.
7
Department of Internal Medicine, University of Michigan, Center for Molecular Imaging, Ann Arbor, Michigan.
8
Department of Radiology, University of Michigan, Center for Molecular Imaging, Ann Arbor, MI 48109. Electronic address: cgalban@med.umich.edu.

Abstract

RATIONALE AND OBJECTIVES:

The longitudinal relationship between regional air trapping and emphysema remains unexplored. We have sought to demonstrate the utility of parametric response mapping (PRM), a computed tomography (CT)-based biomarker, for monitoring regional disease progression in chronic obstructive pulmonary disease (COPD) patients, linking expiratory- and inspiratory-based CT metrics over time.

MATERIALS AND METHODS:

Inspiratory and expiratory lung CT scans were acquired from 89 COPD subjects with varying Global Initiative for Chronic Obstructive Lung Disease (GOLD) status at 30 days (n = 13) or 1 year (n = 76) from baseline as part of the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS) clinical trial. PRMs of CT data were used to quantify the relative volumes of normal parenchyma (PRM(Normal)), emphysema (PRM(Emph)), and functional small airways disease (PRM(fSAD)). PRM measurement variability was assessed using the 30-day interval data. Changes in PRM metrics over a 1-year period were correlated to pulmonary function (forced expiratory volume at 1 second [FEV1]). A theoretical model that simulates PRM changes from COPD was compared to experimental findings.

RESULTS:

PRM metrics varied by ∼6.5% of total lung volume for PRM(Normal) and PRM(fSAD) and 1% for PRM(Emph) when testing 30-day repeatability. Over a 1-year interval, only PRM(Emph) in severe COPD subjects produced significant change (19%-21%). However, 11 of 76 subjects showed changes in PRM(fSAD) greater than variations observed from analysis of 30-day data. Mathematical model simulations agreed with experimental PRM results, suggesting fSAD is a transitional phase from normal parenchyma to emphysema.

CONCLUSIONS:

PRM of lung CT scans in COPD patients provides an opportunity to more precisely characterize underlying disease phenotypes, with the potential to monitor disease status and therapy response.

KEYWORDS:

Chronic obstructive pulmonary disease; computed tomography; diagnostic imaging; disease progression; parametric response map; small airways disease; voxel-wise analysis

PMID:
25442794
PMCID:
PMC4289437
DOI:
10.1016/j.acra.2014.08.015
[Indexed for MEDLINE]
Free PMC Article

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