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Local heterogeneity of normal lung parenchyma and small airways disease are associated with COPD severity and progression.
- Source :
- Respiratory Research; 2/28/2024, Vol. 25 Issue 1, p1-12, 12p
- Publication Year :
- 2024
-
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<superscript>Norm</superscript>) and functional SAD (PRM<superscript>fSAD</superscript>) 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<superscript>Norm</superscript> and PRM<superscript>fSAD</superscript>. Association with COPD severity, emphysema, and spirometric measures were assessed via multivariable regression models. Readouts were evaluated as inputs for predicting FEV<subscript>1</subscript> decline using a machine learning model. Results: Multivariable cross-sectional analysis of COPD subjects showed that V and χ measures for PRM<superscript>fSAD</superscript> and PRM<superscript>Norm</superscript> were independently associated with the amount of emphysema. Readouts χ<superscript>fSAD</superscript> (β of 0.106, p < 0.001) and V<superscript>fSAD</superscript> (β of 0.065, p = 0.004) were also independently associated with FEV<subscript>1</subscript>% predicted. The machine learning model using PRM topologies as inputs predicted FEV<subscript>1</subscript> 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<superscript>fSAD</superscript> and PRM<superscript>Norm</superscript> may show promise as an early indicator of emphysema onset and COPD progression. [ABSTRACT FROM AUTHOR]
- Subjects :
- LUNGS
MACHINE learning
CHRONIC obstructive pulmonary disease
AIRWAY (Anatomy)
Subjects
Details
- Language :
- English
- ISSN :
- 14659921
- Volume :
- 25
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Respiratory Research
- Publication Type :
- Academic Journal
- Accession number :
- 176006176
- Full Text :
- https://doi.org/10.1186/s12931-024-02729-x