biostudies-literatureUnknownBell AJNIAID NIH HHSNHLBI NIH HHSCLC NIH HHS<h4>Objectives</h4>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.<h4>Materials and methods</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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.medRxiv : the preprint server for health sciences2023.05.26.23290532https://www.ebi.ac.uk/biostudies/studies/S-EPMC10274970biostudies-literatureQuantitative CT of Normal Lung Parenchyma and Small Airways Disease Topologies are Associated With COPD Severity and Progression.PMC10274970T32 HL00774975N92021D00011U01 HL089897R01 HL15002375N93023D00011U01 HL089856R01 HL13969075N90023D00011Ram SWang JMMurray SKazerooni EAHatt CRHoff BAHan MKLabaki WWLynch DAPal RGalban CJBell AJGalban SHumphries SMMartinez FJfalseQuantitative CT of Normal Lung Parenchyma and Small Airways Disease Topologies are Associated With COPD Severity and Progression.<h4>Objectives</h4>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.<h4>Materials and methods</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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.2023-01-01T00:00:00Z2023 Nov2024-11-15T03:04:38.616Z2024-11-15T03:04:38.616ZS-EPMC102749703733338210.1101/2023.05.26.23290532