TY - JOUR
T1 - Artificial intelligence based ct assessment of bronchiectasis
T2 - The copdgene study
AU - Diaz, Alejandro A.
AU - Nardelli, Pietro
AU - Wang, Wei
AU - Estepar, Ruben San Jose
AU - Yen, Andrew
AU - Kligerman, Seth
AU - Maselli, Diego J.
AU - Dolliver, Wojciech R.
AU - Tsao, Andrew
AU - Orejas, Jose L.
AU - Aliberti, Stefano
AU - Aksamit, Timothy R.
AU - Young, Kendra A.
AU - Kinney, Gregory L.
AU - Washko, George R.
AU - Silverman, Edwin K.
AU - Estepar, Raul San Jose
N1 - Publisher Copyright:
© 2023 Radiological Society of North America Inc.. All rights reserved.
PY - 2023/4
Y1 - 2023/4
N2 - Background: CT is the standard method used to assess bronchiectasis. A higher airway-To-Artery diameter ratio (AAR) is typically used to identify enlarged bronchi and bronchiectasis; however, current imaging methods are limited in assessing the extent of this metric in CT scans. Purpose: To determine the extent of AARs using an artificial intelligence based chest CT and assess the association of AARs with exacerbations over time. Materials and Methods: In a secondary analysis of ever-smokers from the prospective, observational, multicenter COPDGene study, AARs were quantified using an artificial intelligence tool. The percentage of airways with AAR greater than 1 (a measure of airway dilatation) in each participant on chest CT scans was determined. Pulmonary exacerbations were prospectively determined through biannual follow-up (from July 2009 to September 2021). Multivariable zero-inflated regression models were used to assess the association between the percentage of airways with AAR greater than 1 and the total number of pulmonary exacerbations over follow-up. Covariates included demographics, lung function, and conventional CT parameters. Results: Among 4192 participants (median age, 59 years; IQR, 52 67 years; 1878 men [45%]), 1834 had chronic obstructive pulmonary disease (COPD). During a 10-year follow-up and in adjusted models, the percentage of airways with AARs greater than 1 (quartile 4 vs 1) was associated with a higher total number of exacerbations (risk ratio [RR], 1.08; 95% CI: 1.02, 1.15; P = .01). In participants meeting clinical and imaging criteria of bronchiectasis (ie, clinical manifestations with 3% of AARs 1) versus those who did not, the RR was 1.37 (95% CI: 1.31, 1.43; P 001). Among participants with COPD, the corresponding RRs were 1.10 (95% CI: 1.02, 1.18; P = .02) and 1.32 (95% CI: 1.26, 1.39; P .001), respectively. Conclusion: In ever-smokers with chronic obstructive pulmonary disease, artificial intelligence-based CT measures of bronchiectasis were associated with more exacerbations over time.
AB - Background: CT is the standard method used to assess bronchiectasis. A higher airway-To-Artery diameter ratio (AAR) is typically used to identify enlarged bronchi and bronchiectasis; however, current imaging methods are limited in assessing the extent of this metric in CT scans. Purpose: To determine the extent of AARs using an artificial intelligence based chest CT and assess the association of AARs with exacerbations over time. Materials and Methods: In a secondary analysis of ever-smokers from the prospective, observational, multicenter COPDGene study, AARs were quantified using an artificial intelligence tool. The percentage of airways with AAR greater than 1 (a measure of airway dilatation) in each participant on chest CT scans was determined. Pulmonary exacerbations were prospectively determined through biannual follow-up (from July 2009 to September 2021). Multivariable zero-inflated regression models were used to assess the association between the percentage of airways with AAR greater than 1 and the total number of pulmonary exacerbations over follow-up. Covariates included demographics, lung function, and conventional CT parameters. Results: Among 4192 participants (median age, 59 years; IQR, 52 67 years; 1878 men [45%]), 1834 had chronic obstructive pulmonary disease (COPD). During a 10-year follow-up and in adjusted models, the percentage of airways with AARs greater than 1 (quartile 4 vs 1) was associated with a higher total number of exacerbations (risk ratio [RR], 1.08; 95% CI: 1.02, 1.15; P = .01). In participants meeting clinical and imaging criteria of bronchiectasis (ie, clinical manifestations with 3% of AARs 1) versus those who did not, the RR was 1.37 (95% CI: 1.31, 1.43; P 001). Among participants with COPD, the corresponding RRs were 1.10 (95% CI: 1.02, 1.18; P = .02) and 1.32 (95% CI: 1.26, 1.39; P .001), respectively. Conclusion: In ever-smokers with chronic obstructive pulmonary disease, artificial intelligence-based CT measures of bronchiectasis were associated with more exacerbations over time.
UR - https://www.scopus.com/pages/publications/85151044810
UR - https://www.scopus.com/pages/publications/85151044810#tab=citedBy
U2 - 10.1148/radiol.221109
DO - 10.1148/radiol.221109
M3 - Article
C2 - 36511808
AN - SCOPUS:85151044810
SN - 0033-8419
VL - 307
JO - Radiology
JF - Radiology
IS - 1
M1 - e221109
ER -