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

Respiratory Research
Alexander J. Bell1, Ranjan Pal1, Wassim W. Labaki2, Benjamin A. Hoff1, Jennifer M. Wang2, Susan Murray3, Ella A. Kazerooni2, Stefanie Galbán1, David A. Lynch4, Stephen Humphries4, Fernando J. Martinez5, Charles R. Hatt6, MeiLan K. Han2, Sundaresh Ram7, Craig J. Galbán7
1Department of Radiology, University of Michigan, 109 Zina Pitcher Place BSRB A506, Ann Arbor, MI, 48109-2200, USA
2Department of Internal Medicine, Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, MI, USA
3School of Public Health, University of Michigan, Ann Arbor, MI, USA
4Department of Radiology, National Jewish Health, Denver, CO, USA
5Weill Cornell Medical College, New York, NY, USA
6Imbio, LLC, Minneapolis, MN, USA
7Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA

Tóm tắt

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 (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 PRMNorm and PRMfSAD. 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 PRMfSAD and PRMNorm were 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 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 PRMfSAD and PRMNorm may show promise as an early indicator of emphysema onset and COPD progression.

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Respiratory Research

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