Local heterogeneity of normal lung parenchyma and small airways disease are associated with COPD severity and progression

Abstract

Background: Small airways disease (SAD) is a major cause of airflow obstruction in COPD patients and has been identified as a precursor to emphysema. Although the amount of SAD in the lungs can be quantified using our Parametric Response Mapping (PRM) approach, the full breadth of this readout as a measure of emphysema and COPD progression has yet to be explored. We evaluated topological features of PRM-derived normal parenchyma and SAD as surrogates of emphysema and predictors of spirometric decline.

Methods: PRM metrics of normal lung (PRM^Norm) and functional SAD (PRM^fSAD) were generated from CT scans collected as part of the COPDGene study (n = 8956). Volume density (V) and Euler-Poincaré Characteristic (χ) image maps, measures of the extent and coalescence of pocket formations (i.e., topologies), respectively, were determined for both PRM^Norm and PRM^fSAD. Association with COPD severity, emphysema, and spirometric measures were assessed via multivariable regression models. Readouts were evaluated as inputs for predicting FEV₁ decline using a machine learning model.

Results: Multivariable cross-sectional analysis of COPD subjects showed that V and χ measures for PRM^fSAD and PRM^Norm were independently associated with the amount of emphysema. Readouts χ^fSAD (β of 0.106, p < 0.001) and V^fSAD (β of 0.065, p = 0.004) were also independently associated with FEV₁% predicted. The machine learning model using PRM topologies as inputs predicted FEV₁ decline over five years with an AUC of 0.69.

Conclusions: We demonstrated that V and χ of fSAD and Norm have independent value when associated with lung function and emphysema. In addition, we demonstrated that these readouts are predictive of spirometric decline when used as inputs in a ML model. Our topological PRM approach using PRM^fSAD and PRM^Norm may show promise as an early indicator of emphysema onset and COPD progression.

Keywords: Chronic obstructive pulmonary disease; Computed tomography of the chest; Emphysema; Machine learning; Parametric response mapping; Small airways disease.