Quantitative CT of Normal Lung Parenchyma and Small Airways Disease Topologies are Associated With COPD Severity and Progression

Abstract

AbstractObjectivesSmall 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.Materials and MethodsPRM metrics of normal lung (PRMNorm) and functional SAD (PRMfSAD) 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 PRMNormand PRMfSAD. Association with COPD severity, emphysema, and spirometric measures were assessed via multivariable regression models. Readouts were evaluated as inputs for predicting FEV1decline using a machine learning model.ResultsMultivariable cross-sectional analysis of COPD subjects showed that V and χ measures for PRMfSADand PRMNormwere independently associated with the amount of emphysema. Readouts χfSAD(β of 0.106, p<0.001) and VfSAD(β of 0.065, p=0.004) were also independently associated with FEV1% predicted. The machine learning model using PRM topologies as inputs predicted FEV1decline over five years with an AUC of 0.69.ConclusionsWe 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 PRMfSADand PRMNormmay show promise as an early indicator of emphysema onset and COPD progression.