Correcting Nonpathological Variation in Longitudinal Parametric Response Maps of CT Scans in COPD Subjects: SPIROMICS.

Antonio Fernández-Baldera,Eric A. Hoffman,Fernando J. Martinez,Charles R. Hatt,Susan Murray,Craig J. Galbán,Ella A. Kazerooni,MeiLan K. Han

doi:10.18383/j.tom.2017.00013

Abstract

Small airways disease (SAD) is one of the leading causes of airflow limitations in patients diagnosed with chronic obstructive pulmonary disease (COPD). Parametric response mapping (PRM) of computed tomography (CT) scans allows for the quantification of this previously invisible COPD component. Although PRM is being investigated as a diagnostic tool for COPD, variability in the longitudinal measurements of SAD by PRM has been reported. Here, we show a method for correcting longitudinal PRM data because of nonpathological variations in serial CT scans. In this study, serial whole-lung high-resolution CT scans over a 30-day interval were obtained from 90 subjects with and without COPD accrued as part of SPIROMICS. It was assumed in all subjects that the COPD did not progress between examinations. CT scans were acquired at inspiration and expiration, spatially aligned to a single geometric frame, and analyzed using PRM. By modeling variability in longitudinal CT scans, our method could identify, at the voxel-level, shifts in PRM classification over the 30-day interval. In the absence of any correction, PRM generated serial percent volumes of functional SAD with differences as high as 15%. Applying the correction strategy significantly mitigated this effect with differences ∼1%. At the voxel-level, significant differences were found between baseline PRM classifications and the follow-up map computed with and without correction (P < .01 over GOLD). This strategy of accounting for nonpathological sources of variability in longitudinal PRM may improve the quantification of COPD phenotypes transitioning with disease progression.

Highlights

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity, mortality, and burden on the world’s health and financial systems [1, 2]
Parametric response mapping (PRM) was evaluated as a marker for monitoring change in disease classification from subjects accrued as part of SPIROMICS [5]
Lung volumes, forced expiratory volume at 1 second (FEV1), and PRM metrics at both interval examinations were found to be dependent on GOLD

Summary

Introduction

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity, mortality, and burden on the world’s health and financial systems [1, 2]. Parametric response mapping (PRM) is an analytical approach that, when applied to spatially aligned high-resolution computed tomography (HRCT) scans, allows both visualization and quantification of lung parenchyma affected by small airways disease (SAD), even when only emphysema is visibly observed [4]. This technique quantifies a previously occult component of COPD and can be applied to retrospective HRCT data. “voxel-based tracking,” a method for evaluating longitudinal changes in PRM classification at the voxel level, has been used This approach when applied to PRM shows promise at providing local disease progression, variability in Correction in Longitudinal PRM

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