Using chest CT scan and unsupervised machine learning for predicting and evaluating response to lumacaftor-ivacaftor in people with cystic fibrosis - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles European Respiratory Journal Year : 2021

Using chest CT scan and unsupervised machine learning for predicting and evaluating response to lumacaftor-ivacaftor in people with cystic fibrosis

Alienor Campredon
  • Function : Author
Enzo Battistella
  • Function : Author
Clémence Martin
Isabelle Durieu
  • Function : Author
Laurent Mely
  • Function : Author
Christophe Marguet
  • Function : Author
Chantal Belleguic
  • Function : Author
Marlène Murris-Espin
  • Function : Author
Raphaël Chiron
  • Function : Author
Annlyse Fanton
  • Function : Author
Stéphanie Bui
  • Function : Author
Martine Reynaud-Gaubert
  • Function : Author
Philippe Reix
Trieu-Nghi Hoang-Thi
Marie-Pierre Revel
Jennifer da Silva
  • Function : Author
Pierre-Régis Burgel
Guillaume Chassagnon
  • Function : Author

Abstract

Objectives Lumacaftor-ivacaftor is a cystic fibrosis transmembrane conductance regulator (CFTR) modulator known to improve clinical status in people with cystic fibrosis (CF). This study aimed to assess lung structural changes after one year of lumacaftor-ivacaftor treatment, and to use unsupervised machine learning to identify morphological phenotypes of lung disease that are associated with response to lumacaftor-ivacaftor. Methods Adolescents and adults with CF from the French multicenter real-world prospective observational study evaluating the first year of treatment with lumacaftor-ivacaftor were included if they had pretherapeutic and follow-up chest computed tomography (CT)-scans available. CT scans were visually scored using a modified Bhalla score. A k-mean clustering method was performed based on 120 radiomics features extracted from unenhanced pretherapeutic chest CT scans. Results A total of 283 patients were included. The Bhalla score significantly decreased after 1 year of lumacaftor-ivacaftor (−1.40±1.53 points compared with pretherapeutic CT; p<0.001). This finding was related to a significant decrease in mucus plugging (−0.35±0.62 points; p<0.001), bronchial wall thickening (−0.24±0.52 points; p<0.001) and parenchymal consolidations (−0.23±0.51 points; p<0.001). Cluster analysis identified 3 morphological clusters. Patients from cluster C were more likely to experience an increase in percent predicted forced expiratory volume in 1 sec (ppFEV 1 ) ≥5 under lumacaftor–ivacaftor than those in the other clusters (54% of responders versus 32% and 33%; p=0.01). Conclusion One year treatment with lumacaftor-ivacaftor was associated with a significant visual improvement of bronchial disease on chest CT. Radiomics features on pretherapeutic CT scan may help in predicting lung function response under lumacaftor-ivacaftor.
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Dates and versions

hal-03539118 , version 1 (21-01-2022)

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Alienor Campredon, Enzo Battistella, Clémence Martin, Isabelle Durieu, Laurent Mely, et al.. Using chest CT scan and unsupervised machine learning for predicting and evaluating response to lumacaftor-ivacaftor in people with cystic fibrosis. European Respiratory Journal, 2021, pp.2101344. ⟨10.1183/13993003.01344-2021⟩. ⟨hal-03539118⟩
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