Local heterogeneity of normal lung parenchyma and small airways disease are associated with COPD severity and progression
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 $ FEV_{1} $ 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 $ FEV_{1} $% predicted. The machine learning model using PRM topologies as inputs predicted $ FEV_{1} $ 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..
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:25 |
|---|---|
| Enthalten in: |
Respiratory research - 25(2024), 1 vom: 28. Feb. |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Bell, Alexander J. [VerfasserIn] |
|---|
| Links: |
Volltext [kostenfrei] |
|---|
| Themen: |
Chronic obstructive pulmonary disease |
|---|
| Anmerkungen: |
© The Author(s) 2024 |
|---|
| doi: |
10.1186/s12931-024-02729-x |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
SPR054954185 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | SPR054954185 | ||
| 003 | DE-627 | ||
| 005 | 20240229064717.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240229s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1186/s12931-024-02729-x |2 doi | |
| 035 | |a (DE-627)SPR054954185 | ||
| 035 | |a (SPR)s12931-024-02729-x-e | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Bell, Alexander J. |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Local heterogeneity of normal lung parenchyma and small airways disease are associated with COPD severity and progression |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a © The Author(s) 2024 | ||
| 520 | |a 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 $ FEV_{1} $ 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 $ FEV_{1} $% predicted. The machine learning model using PRM topologies as inputs predicted $ FEV_{1} $ 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. | ||
| 650 | 4 | |a Chronic obstructive pulmonary disease |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Small airways disease |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Parametric response mapping |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Computed tomography of the chest |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Machine learning |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Emphysema |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Pal, Ravi |4 aut | |
| 700 | 1 | |a Labaki, Wassim W. |4 aut | |
| 700 | 1 | |a Hoff, Benjamin A. |4 aut | |
| 700 | 1 | |a Wang, Jennifer M. |4 aut | |
| 700 | 1 | |a Murray, Susan |4 aut | |
| 700 | 1 | |a Kazerooni, Ella A. |4 aut | |
| 700 | 1 | |a Galban, Stefanie |4 aut | |
| 700 | 1 | |a Lynch, David A. |4 aut | |
| 700 | 1 | |a Humphries, Stephen M. |4 aut | |
| 700 | 1 | |a Martinez, Fernando J. |4 aut | |
| 700 | 1 | |a Hatt, Charles R. |4 aut | |
| 700 | 1 | |a Han, MeiLan K. |4 aut | |
| 700 | 1 | |a Ram, Sundaresh |4 aut | |
| 700 | 1 | |a Galban, Craig J. |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Respiratory research |d London : BioMed Central, 2001 |g 25(2024), 1 vom: 28. Feb. |w (DE-627)SPR028499905 |w (DE-600)2041675-1 |x 1465-993X |7 nnns |
| 773 | 1 | 8 | |g volume:25 |g year:2024 |g number:1 |g day:28 |g month:02 |
| 856 | 4 | 0 | |u https://dx.doi.org/10.1186/s12931-024-02729-x |z kostenfrei |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_SPRINGER | ||
| 951 | |a AR | ||
| 952 | |d 25 |j 2024 |e 1 |b 28 |c 02 | ||