Identification of structural predictors of lung function improvement in adults with cystic fibrosis treated with elexacaftor-tezacaftor-ivacaftor using deep-learning.
Authors
Affiliations (5)
Affiliations (5)
- Department of Radiology, Hôpital Cochin, AP-HP.Centre, Université Paris Cité, Paris 75014, France; Unité HeKA, Université Paris Cité, Inria, INSERM, Paris 75015, France; Université Paris Cité, Paris 75006, France. Electronic address: [email protected].
- Department of Radiology, Hôpital Cochin, AP-HP.Centre, Université Paris Cité, Paris 75014, France.
- Respiratory Medicine and Cystic Fibrosis National Reference Center, Hôpital Cochin, AP-HP.Centre, Université Paris Cité, Paris 75014, France.
- Department of Radiology, Hôpital Cochin, AP-HP.Centre, Université Paris Cité, Paris 75014, France; Unité HeKA, Université Paris Cité, Inria, INSERM, Paris 75015, France; Université Paris Cité, Paris 75006, France.
- Université Paris Cité, Paris 75006, France; Respiratory Medicine and Cystic Fibrosis National Reference Center, Hôpital Cochin, AP-HP.Centre, Université Paris Cité, Paris 75014, France; Institut Cochin, INSERM U1016, Université Paris Cité, Paris 75014, France.
Abstract
The purpose of this study was to evaluate the relationship between structural abnormalities on CT and lung function prior to and after initiation of elexacaftor-tezacaftor-ivacaftor (ETI) in adults with cystic fibrosis (CF) using a deep learning model. A deep learning quantification model was developed using 100 chest computed tomography (CT) examinations of patients with CF and 150 chest CT examinations of patients with various other bronchial diseases to quantify seven types of abnormalities. This model was then applied to an independent dataset of CT examinations of 218 adults with CF who were treated with ETI. The relationship between structural abnormalities and percent predicted forced expiratory volume in one second (ppFEV<sub>1</sub>) was examined using general linear regression models. The deep learning model performed as well as radiologists for the quantification of the seven types of abnormalities. Chest CT examinations obtained before to and one year after the initiation of ETI were analyzed. The independent structural predictors of ppFEV<sub>1</sub> prior to ETI were bronchial wall thickening (P = 0.011), mucus plugging (P < 0.001), consolidation/atelectasis (P < 0.001), and mosaic perfusion (P < 0.001). An increase in ppFEV<sub>1</sub> after initiation of ETI independently correlated with a decrease in bronchial wall thicknening (-49 %; P = 0.004), mucus plugging (-92 %; P < 0.001), centrilobular nodules (-78 %; P = 0.009) and mosaic perfusion (-14 %; P < 0.001). Younger age (P < 0.001), greater mucus plugging extent (P = 0.016), and centrilobular nodules (P < 0.001) prior to ETI initiation were independent predictors of ppFEV<sub>1</sub> improvement. A deep learning model can quantify CT lung abnormalities in adults with CF. Lung function impairment in adults with CF is associated with muco-inflammatory lesions on CT, which are largely reversible with ETI, and with mosaic perfusion, which appear less reversible and is presumably related to irreversible damage. Predictors of lung function improvement are a younger age and a greater extent of muco-inflammatory lesions obstructing the airways.