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Deep Learning for the Automatic Quantification of Pleural Plaques in Asbestos-Exposed Subjects

hal.structure.identifierCentre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB]
hal.structure.identifierCentre Hospitalier Universitaire de Bordeaux [CHU Bordeaux]
dc.contributor.authorBENLALA, Ilyes
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.identifierCentre Hospitalier Universitaire de Bordeaux [CHU Bordeaux]
dc.contributor.authorDOURNES, Gael
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorMENANT, Morgane
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorGRAMOND, Celine
hal.structure.identifierCentre Hospitalier Régional Universitaire de Nancy [CHRU Nancy]
dc.contributor.authorTHAON, Isabelle
hal.structure.identifierCHU Caen
hal.structure.identifierUniversité de Caen Normandie [UNICAEN]
dc.contributor.authorCLIN, Benedicte
hal.structure.identifierCentre Hospitalier Universitaire de Bordeaux [CHU Bordeaux]
hal.structure.identifierUniversité de Bordeaux [UB]
dc.contributor.authorBROCHARD, Patrick
hal.structure.identifierAliments Bioprocédés Toxicologie Environnements [ABTE]
hal.structure.identifierCHU Rouen
dc.contributor.authorGISLARD, Antoine
hal.structure.identifierInstitut Mondor de Recherche Biomédicale [IMRB]
hal.structure.identifierCentre Hospitalier Intercommunal de Créteil [CHIC]
hal.structure.identifierInstitut Interuniversitaire de Médecine du Travail de Paris Ile-de-France [IIMTPIF]
dc.contributor.authorANDUJAR, Pascal
hal.structure.identifierInstitut Interuniversitaire de Médecine du Travail de Paris Ile-de-France [IIMTPIF]
dc.contributor.authorCHAMMINGS, Soizick
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorGALLET, Justine
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorLACOURT, Aude
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorDELVA, Fleur
hal.structure.identifierCentre Hospitalier Universitaire de Rennes [CHU Rennes] = Rennes University Hospital [Pontchaillou]
hal.structure.identifierInstitut de recherche en santé, environnement et travail [Irset]
dc.contributor.authorPARIS, Christophe
hal.structure.identifierInstitute for Advanced Biosciences / Institut pour l'Avancée des Biosciences (Grenoble) [IAB]
hal.structure.identifierCentre Hospitalier Universitaire [CHU Grenoble] [CHUGA]
dc.contributor.authorFERRETTI, Gilbert
hal.structure.identifierInstitut Mondor de Recherche Biomédicale [IMRB]
hal.structure.identifierCentre Hospitalier Intercommunal de Créteil [CHIC]
hal.structure.identifierInstitut Interuniversitaire de Médecine du Travail de Paris Ile-de-France [IIMTPIF]
dc.contributor.authorPAIRON, Jean-Claude
hal.structure.identifierCentre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB]
hal.structure.identifierCentre Hospitalier Universitaire de Bordeaux [CHU Bordeaux]
dc.contributor.authorLAURENT, Francois
dc.date.accessioned2024-04-04T02:41:52Z
dc.date.available2024-04-04T02:41:52Z
dc.date.issued2022-01
dc.identifier.issn1661-7827
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/191190
dc.description.abstractEnOBJECTIVE: This study aimed to develop and validate an automated artificial intelligence (AI)-driven quantification of pleural plaques in a population of retired workers previously occupationally exposed to asbestos. METHODS: CT scans of former workers previously occupationally exposed to asbestos who participated in the multicenter APEXS (Asbestos PostExposure Survey) study were collected retrospectively between 2010 and 2017 during the second and the third rounds of the survey. A hundred and forty-one participants with pleural plaques identified by expert radiologists at the 2nd and the 3rd CT screenings were included. Maximum Intensity Projection (MIP) with 5 mm thickness was used to reduce the number of CT slices for manual delineation. A Deep Learning AI algorithm using 2D-convolutional neural networks was trained with 8280 images from 138 CT scans of 69 participants for the semantic labeling of Pleural Plaques (PP). In all, 2160 CT images from 36 CT scans of 18 participants were used for AI testing versus ground-truth labels (GT). The clinical validity of the method was evaluated longitudinally in 54 participants with pleural plaques. RESULTS: The concordance correlation coefficient (CCC) between AI-driven and GT was almost perfect (>0.98) for the volume extent of both PP and calcified PP. The 2D pixel similarity overlap of AI versus GT was good (DICE = 0.63) for PP, whether they were calcified or not, and very good (DICE = 0.82) for calcified PP. A longitudinal comparison of the volumetric extent of PP showed a significant increase in PP volumes (p < 0.001) between the 2nd and the 3rd CT screenings with an average delay of 5 years. CONCLUSIONS: AI allows a fully automated volumetric quantification of pleural plaques showing volumetric progression of PP over a five-year period. The reproducible PP volume evaluation may enable further investigations for the comprehension of the unclear relationships between pleural plaques and both respiratory function and occurrence of thoracic malignancy.
dc.language.isoen
dc.publisherMDPI
dc.rights.urihttp://creativecommons.org/licenses/by/
dc.subject.enArtificial intelligence
dc.subject.enPleural plaques
dc.subject.enAsbestos exposure
dc.title.enDeep Learning for the Automatic Quantification of Pleural Plaques in Asbestos-Exposed Subjects
dc.typeArticle de revue
dc.identifier.doi10.3390/ijerph19031417
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologie
bordeaux.journalInternational Journal of Environmental Research and Public Health
bordeaux.page1417
bordeaux.volume19
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.issue3
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-03610113
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03610113v1
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