1. Using chest computed tomography and unsupervised machine learning for predicting and evaluating response to lumacaftor-ivacaftor in people with cystic fibrosis.
- Author
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Campredon A, Battistella E, Martin C, Durieu I, Mely L, Marguet C, Belleguic C, Murris-Espin M, Chiron R, Fanton A, Bui S, Reynaud-Gaubert M, Reix P, Hoang-Thi TN, Vakalopoulou M, Revel MP, Da Silva J, Burgel PR, Chassagnon G, Mounard J, Poulet C, Rames Amiens C, Person C, Troussier F, Urban Angers T, Dalphin ML, Dalphin JC, Pernet D, Richaud-Thiriez Besançon B, Bui S, Fayon M, Macey-Caro Bordeaux J, Campbell K, Laurans Caen M, Borderon C, Heraud MC, Labbé A, Montcouquiol Clermont-Ferrand S, Bassinet L, Remus Créteil N, Fanton A, Houzel-Charavel A, Huet F, Perez-Martin Dijon S, Boldron-Ghaddar A, Scalbert Dunkerque M, Mely Giens L, Camara B, Llerena C, Pin I, Quétant Grenoble S, Cottereau A, Deschildre A, Gicquello A, Perez T, Stervinou-Wemeau L, Thumerelle C, Wallaert B, Wizla Lille N, Languepin J, Ménétrey C, Dupuy-Grasset Limoges M, Bazus L, Buchs C, Jubin V, Werck-Gallois MC, Mainguy C, Perrin T, Reix P, Toutain-Rigolet Lyon Pédiatrie A, Durieu I, Durupt S, Reynaud Q, Nove-Josserand Lyon Adultes R, Baravalle-Einaudi M, Coltey B, Dufeu N, Dubus JC, Stremler Marseille N, Caimmi D, Chiron Montpellier R, Billon Y, Derelle J, Kieffer S, Pichon AS, Schweitzer C, Tatopoulos Nancy A, Abbes S, Bihouée T, Danner-Boucher I, David V, Haloun A, Tissot Nantes A, Leroy S, Bailly-Piccini Nice C, Clément A, Corvol H, Tamalet ParisTrousseau A, Burgel PR, Honoré I, Hubert D, Kanaan R, Martin Paris Cochin C, Bailly C, Chédevergne F, De Blic J, Fauroux B, Bourgeois ML, Sermet-Gaudelus Paris Necker I, Delaisi B, Gérardin M, Munck ParisRobert Debré A, Abély M, Ravoninjatovo Reims B, Belleguic C, Desrues B, Brinchault Rennes G, Dagorne M, Deneuville E, Lefeuvre Rennes-Saint Brieuc S, Dirou A, Bihan JL, Ramel Roscoff S, Dominique S, Marguet Rouen C, Payet La Réunion A, Kessler R, Porzio M, Rosner V, Weiss Strasbourg L, Miranda S, Grenet D, Hamid A, Picard Suresnes C, Brémont F, Didier A, Labouret G, Mittaine M, Murris-Espin M, Têtu Toulouse L, Cosson L, Giraut C, Henriet AC, Mankikian J, Marchand Tours S, Hugé S, Storni Vannes V, and Coirier-Duet Versailles E
- Abstract
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
1 % pred) ≥5% under lumacaftor-ivacaftor than those in the other clusters (54% of responders versus 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., Competing Interests: Conflict of interest: A. Campredon has nothing to disclose. Conflict of interest: E. Battistella has nothing to disclose. Conflict of interest: C. Martin reports lecture payments or honoraria from Chiesi and Zambon, outside the submitted work. Conflict of interest: I. Durieu has nothing to disclose. Conflict of interest: L. Mely has nothing to disclose. Conflict of interest: C. Marguet reports consulting fees from Gleamer; lecture payments or honoraria from Vertex, Viatis and Zambon; support for attending meetings and/or travel from Zambon; and participation on a Data Safety Monitoring Board or Advisory Board for Zambon and Viatis; outside the submitted work. Conflict of interest: C. Belleguic has nothing to disclose. Conflict of interest: M. Murris-Espin has nothing to disclose. Conflict of interest: R. Chiron has nothing to disclose. Conflict of interest: A. Fanton has nothing to disclose. Conflict of interest: S. Bui reports payment for expert testimony for inhaled antibiotics for Zambon, outside the submitted work. Conflict of interest: M. Reynaud-Gaubert has nothing to disclose. Conflict of interest: P. Reix has nothing to disclose. Conflict of interest: T-N. Hoang-Thi has nothing to disclose. Conflict of interest: M. Vakalopoulou has nothing to disclose. Conflict of interest: M-P. Revel has nothing to disclose. Conflict of interest: J. Da Silva has nothing to disclose. Conflict of interest: P-R. Burgel reports grants or contracts from Vertex and GSK; consulting fees from AstraZeneca, Chiesi, GSK, Insmed, Vertex and Zambon; and lecture payments or honoraria from Pfizer and Novartis; outside the submitted work. Conflict of interest: G. Chassagnon reports lecture payments or honoraria from Chiesi, outside the submitted work., (Copyright ©The authors 2022. For reproduction rights and permissions contact permissions@ersnet.org.)- Published
- 2022
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