Diagnostic and interventional imaging · 2025
Identification of structural predictors of lung function improvement in adults with cystic fibrosis treated with elexacaftor-tezacaftor-ivacaftor using deep-learning.
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Chassagnon Guillaume, Marini Rafael, Ong Valentin, Da Silva Jennifer, Habip Gatenyo Denis, Honore Isabelle, Kanaan Reem, Carlier Nicolas, Fesenbeckh Johanna, Burnet Espérie, Revel Marie-Pierre, Martin Clémence, Burgel Pierre-Régis
Аннотация
PURPOSE: 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.
MATERIALS AND METHODS: 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 (ppFEV1) was examined using general linear regression models.
RESULTS: 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 ppFEV1 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 ppFEV1 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 ppFEV1 improvement.
CONCLUSION: 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.
MATERIALS AND METHODS: 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 (ppFEV1) was examined using general linear regression models.
RESULTS: 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 ppFEV1 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 ppFEV1 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 ppFEV1 improvement.
CONCLUSION: 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.