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Deep Learning for the Automatic Quantification of Pleural Plaques in Asbestos-Exposed Subjects
hal.structure.identifier | Centre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB] | |
hal.structure.identifier | CHU Bordeaux | |
dc.contributor.author | BENLALA, Ilyes | |
hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
hal.structure.identifier | Modélisation Mathématique pour l'Oncologie [MONC] | |
dc.contributor.author | DENIS DE SENNEVILLE, Baudouin | |
hal.structure.identifier | Centre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB] | |
hal.structure.identifier | CHU Bordeaux | |
dc.contributor.author | DOURNES, Gael | |
hal.structure.identifier | Bordeaux population health [BPH] | |
dc.contributor.author | MENANT, Morgane | |
hal.structure.identifier | Bordeaux population health [BPH] | |
dc.contributor.author | GRAMOND, Celine | |
hal.structure.identifier | Centre Hospitalier Régional Universitaire de Nancy [CHRU Nancy] | |
dc.contributor.author | THAON, Isabelle | |
hal.structure.identifier | CHU Caen | |
hal.structure.identifier | Université de Caen Normandie [UNICAEN] | |
dc.contributor.author | CLIN, Benedicte | |
hal.structure.identifier | CHU Bordeaux | |
hal.structure.identifier | Université de Bordeaux [UB] | |
dc.contributor.author | BROCHARD, Patrick | |
hal.structure.identifier | Aliments Bioprocédés Toxicologie Environnements [ABTE] | |
hal.structure.identifier | CHU Rouen | |
dc.contributor.author | GISLARD, Antoine | |
hal.structure.identifier | Institut Mondor de Recherche Biomédicale [IMRB] | |
hal.structure.identifier | Centre Hospitalier Intercommunal de Créteil [CHIC] | |
hal.structure.identifier | Institut Interuniversitaire de Médecine du Travail de Paris Ile-de-France [IIMTPIF] | |
dc.contributor.author | ANDUJAR, Pascal | |
hal.structure.identifier | Institut Interuniversitaire de Médecine du Travail de Paris Ile-de-France [IIMTPIF] | |
dc.contributor.author | CHAMMINGS, Soizick | |
hal.structure.identifier | Bordeaux population health [BPH] | |
dc.contributor.author | GALLET, Justine | |
hal.structure.identifier | Bordeaux population health [BPH] | |
dc.contributor.author | LACOURT, Aude | |
hal.structure.identifier | Bordeaux population health [BPH] | |
dc.contributor.author | DELVA, Fleur | |
hal.structure.identifier | Centre Hospitalier Universitaire de Rennes [CHU Rennes] = Rennes University Hospital [Pontchaillou] | |
hal.structure.identifier | Institut de recherche en santé, environnement et travail [Irset] | |
dc.contributor.author | PARIS, Christophe | |
hal.structure.identifier | Institute for Advanced Biosciences / Institut pour l'Avancée des Biosciences (Grenoble) [IAB] | |
hal.structure.identifier | Centre Hospitalier Universitaire [CHU Grenoble] [CHUGA] | |
dc.contributor.author | FERRETTI, Gilbert | |
hal.structure.identifier | Institut Mondor de Recherche Biomédicale [IMRB] | |
hal.structure.identifier | Centre Hospitalier Intercommunal de Créteil [CHIC] | |
hal.structure.identifier | Institut Interuniversitaire de Médecine du Travail de Paris Ile-de-France [IIMTPIF] | |
dc.contributor.author | PAIRON, Jean-Claude | |
hal.structure.identifier | Centre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB] | |
hal.structure.identifier | CHU Bordeaux | |
dc.contributor.author | LAURENT, Francois | |
dc.date.accessioned | 2024-04-04T02:41:52Z | |
dc.date.available | 2024-04-04T02:41:52Z | |
dc.date.issued | 2022-01 | |
dc.identifier.issn | 1661-7827 | |
dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/191190 | |
dc.description.abstractEn | OBJECTIVE: 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.iso | en | |
dc.publisher | MDPI | |
dc.rights.uri | http://creativecommons.org/licenses/by/ | |
dc.subject.en | Artificial intelligence | |
dc.subject.en | Pleural plaques | |
dc.subject.en | Asbestos exposure | |
dc.title.en | Deep Learning for the Automatic Quantification of Pleural Plaques in Asbestos-Exposed Subjects | |
dc.type | Article de revue | |
dc.identifier.doi | 10.3390/ijerph19031417 | |
dc.subject.hal | Sciences du Vivant [q-bio]/Santé publique et épidémiologie | |
bordeaux.journal | International Journal of Environmental Research and Public Health | |
bordeaux.page | 1417 | |
bordeaux.volume | 19 | |
bordeaux.hal.laboratories | Institut de Mathématiques de Bordeaux (IMB) - UMR 5251 | * |
bordeaux.issue | 3 | |
bordeaux.institution | Université de Bordeaux | |
bordeaux.institution | Bordeaux INP | |
bordeaux.institution | CNRS | |
bordeaux.peerReviewed | oui | |
hal.identifier | hal-03610113 | |
hal.version | 1 | |
hal.popular | non | |
hal.audience | Internationale | |
hal.origin.link | https://hal.archives-ouvertes.fr//hal-03610113v1 | |
bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=International%20Journal%20of%20Environmental%20Research%20and%20Public%20Health&rft.date=2022-01&rft.volume=19&rft.issue=3&rft.spage=1417&rft.epage=1417&rft.eissn=1661-7827&rft.issn=1661-7827&rft.au=BENLALA,%20Ilyes&DENIS%20DE%20SENNEVILLE,%20Baudouin&DOURNES,%20Gael&MENANT,%20Morgane&GRAMOND,%20Celine&rft.genre=article |
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