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Artificial intelligence in CT for quantifying lung changes in the era of CFTR modulators

hal.structure.identifierCentre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB]
hal.structure.identifierCIC Bordeaux
hal.structure.identifierCentre Hospitalier Universitaire de Bordeaux [CHU Bordeaux]
dc.contributor.authorDOURNES, Gael
hal.structure.identifierUniversity of Kansas [Kansas City]
dc.contributor.authorHALL, Chase
hal.structure.identifierCincinnati Children's Hospital Medical Center
dc.contributor.authorWILLMERING, Matthew
hal.structure.identifierCincinnati Children's Hospital Medical Center
dc.contributor.authorBRODY, Alan
hal.structure.identifierCentre Hospitalier Universitaire de Bordeaux [CHU Bordeaux]
hal.structure.identifierCIC Bordeaux
dc.contributor.authorMACEY, Julie
hal.structure.identifierCIC Bordeaux
hal.structure.identifierHôpital Pellegrin
hal.structure.identifierBiothérapies des maladies génétiques et cancers
dc.contributor.authorBUI, Stephanie
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierModélisation Mathématique pour l'Oncologie [MONC]
dc.contributor.authorDENIS DE SENNEVILLE, Baudouin
hal.structure.identifierCentre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB]
hal.structure.identifierCIC Bordeaux
hal.structure.identifierCentre Hospitalier Universitaire de Bordeaux [CHU Bordeaux]
dc.contributor.authorBERGER, Patrick
hal.structure.identifierCentre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB]
hal.structure.identifierCIC Bordeaux
hal.structure.identifierCentre Hospitalier Universitaire de Bordeaux [CHU Bordeaux]
dc.contributor.authorLAURENT, François
hal.structure.identifierCentre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB]
hal.structure.identifierCIC Bordeaux
hal.structure.identifierCentre Hospitalier Universitaire de Bordeaux [CHU Bordeaux]
dc.contributor.authorBENLALA, Ilyes
hal.structure.identifierUniversity of Cincinnati [UC]
hal.structure.identifierCincinnati Children's Hospital Medical Center
dc.contributor.authorWOODS, Jason
dc.date.accessioned2024-04-04T02:43:46Z
dc.date.available2024-04-04T02:43:46Z
dc.date.issued2022
dc.identifier.issn0903-1936
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/191357
dc.description.abstractEnBackground: Chest computed tomography (CT) remains the imaging standard for demonstrating cystic fibrosis (CF) airway structural disease in vivo. However, visual scoring systems as an outcome measure are time consuming, require training and lack high reproducibility. Our objective was to validate a fully automated artificial intelligence (AI)-driven scoring system of CF lung disease severity.Methods: Data were retrospectively collected in three CF reference centres, between 2008 and 2020, in 184 patients aged 4-54 years. An algorithm using three 2D convolutional neural networks was trained with 78 patients' CT scans (23 530 CT slices) for the semantic labelling of bronchiectasis, peribronchial thickening, bronchial mucus, bronchiolar mucus and collapse/consolidation. 36 patients' CT scans (11 435 CT slices) were used for testing versus ground-truth labels. The method's clinical validity was assessed in an independent group of 70 patients with or without lumacaftor/ivacaftor treatment (n=10 and n=60, respectively) with repeat examinations. Similarity and reproducibility were assessed using the Dice coefficient, correlations using the Spearman test, and paired comparisons using the Wilcoxon rank test.Results: The overall pixelwise similarity of AI-driven versus ground-truth labels was good (Dice 0.71). All AI-driven volumetric quantifications had moderate to very good correlations to a visual imaging scoring (p<0.001) and fair to good correlations to forced expiratory volume in 1 s % predicted at pulmonary function tests (p<0.001). Significant decreases in peribronchial thickening (p=0.005), bronchial mucus (p=0.005) and bronchiolar mucus (p=0.007) volumes were measured in patients with lumacaftor/ivacaftor. Conversely, bronchiectasis (p=0.002) and peribronchial thickening (p=0.008) volumes increased in patients without lumacaftor/ivacaftor. The reproducibility was almost perfect (Dice >0.99).Conclusion: AI allows fully automated volumetric quantification of CF-related modifications over an entire lung. The novel scoring system could provide a robust disease outcome in the era of effective CF transmembrane conductance regulator modulator therapy.
dc.description.sponsorshipTranslational Research and Advanced Imaging Laboratory - ANR-10-LABX-0057
dc.language.isoen
dc.publisherEuropean Respiratory Society
dc.title.enArtificial intelligence in CT for quantifying lung changes in the era of CFTR modulators
dc.typeArticle de revue
dc.identifier.doi10.1183/13993003.00844-2021
dc.subject.halSciences du Vivant [q-bio]/Ingénierie biomédicale/Imagerie
dc.subject.halSciences du Vivant [q-bio]/Médecine humaine et pathologie/Pneumologie et système respiratoire
bordeaux.journalEuropean Respiratory Journal
bordeaux.page2100844
bordeaux.volume59
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-03453536
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03453536v1
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