Using chest computed tomography and unsupervised machine learning for predicting and evaluating response to lumacaftor-ivacaftor in people with cystic fibrosis.

Objectives: Lumacaftor-ivacaftor is a cystic fibrosis transmembrane conductance regulator (CFTR) modulator known to improve clinical status in people with cystic fibrosis (CF). The aim of this study was to assess lung structural changes after 1 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 a French multicentre real-world prospective observational study evaluating the first year of treatment with lumacaftor-ivacaftor were included if they had pre-therapeutic and follow-up chest computed tomography (CT) scans available. CT scans were visually scored using a modified Bhalla score. A k-means clustering method was performed based on 120 radiomics features extracted from unenhanced pre-therapeutic chest CT scans.

Results: In total, 283 patients were included. The Bhalla score significantly decreased after 1 year of lumacaftor-ivacaftor (-1.40±1.53 points compared with pre-therapeutic CT, p<0.001). This finding was related to a significant decrease in mucus plugging (-0.58±0.88 points, p<0.001), bronchial wall thickening (-0.35±0.62 points, p<0.001) and parenchymal consolidations (-0.24±0.52 points, p<0.001). Cluster analysis identified three morphological clusters. Patients from cluster C were more likely to experience an increase in per cent predicted forced expiratory volume in 1 s (FEV % pred) ≥5% under lumacaftor-ivacaftor than those in the other clusters (54% of responders 32% and 33%; p=0.02).

Conclusion: 1-year treatment with lumacaftor-ivacaftor was associated with a significant visual improvement of bronchial disease on chest CT. Radiomics features on pre-therapeutic CT scans may help to predict lung function response under lumacaftor-ivacaftor.