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

Autor:	Benlala I; Faculté de Médecine, Université de Bordeaux, 33000 Bordeaux, France.; Service d'Imagerie Médicale Radiologie Diagnostique et Thérapeutique, CHU de Bordeaux, 33000 Bordeaux, France.; Centre de Recherche Cardio-Thoracique de Bordeaux, INSERM U1045, Université de Bordeaux, 33000 Bordeaux, France., De Senneville BD; Mathematical Institute of Bordeaux (IMB), CNRS, INRIA, Bordeaux INP, UMR 5251, Université de Bordeaux, 33400 Talence, France., Dournes G; Faculté de Médecine, Université de Bordeaux, 33000 Bordeaux, France.; Service d'Imagerie Médicale Radiologie Diagnostique et Thérapeutique, CHU de Bordeaux, 33000 Bordeaux, France.; Centre de Recherche Cardio-Thoracique de Bordeaux, INSERM U1045, Université de Bordeaux, 33000 Bordeaux, France., Menant M; Epicene Team, Bordeaux Population Health Research Center, INSERM UMR 1219, Université de Bordeaux, 33000 Bordeaux, France., Gramond C; Epicene Team, Bordeaux Population Health Research Center, INSERM UMR 1219, Université de Bordeaux, 33000 Bordeaux, France., Thaon I; Centre de Consultation de Pathologies Professionnelles, CHRU de Nancy, Université de Lorraine, 54000 Nancy, France., Clin B; Service de Santé au Travail et Pathologie Professionnelle, CHU Caen, 14000 Caen, France.; Faculté de Médecine, Université de Caen, 14000 Caen, France., Brochard P; Faculté de Médecine, Université de Bordeaux, 33000 Bordeaux, France.; Service de Médecine du Travail et de Pathologies Professionnelles, CHU de Bordeaux, 33000 Bordeaux, France., Gislard A; Faculté de Médecine, Normandie Université, UNIROUEN, UNICAEN, ABTE, 76000 Rouen, France.; Centre de Consultations de Pathologie Professionnelle, CHU de Rouen, CEDEX, 76031 Rouen, France., Andujar P; Equipe GEIC20, INSERM U955, 94000 Créteil, France.; Faculté de Santé, Université Paris-Est Créteil, 94000 Créteil, France.; Service de Pathologies Professionnelles et de l'Environnement, Centre Hospitalier Intercommunal Créteil, Institut Santé-Travail Paris-Est, 94000 Créteil, France.; Institut Interuniversitaire de Médecine du Travail de Paris-Ile de France, 94000 Créteil, France., Chammings S; Institut Interuniversitaire de Médecine du Travail de Paris-Ile de France, 94000 Créteil, France., Gallet J; Epicene Team, Bordeaux Population Health Research Center, INSERM UMR 1219, Université de Bordeaux, 33000 Bordeaux, France., Lacourt A; Epicene Team, Bordeaux Population Health Research Center, INSERM UMR 1219, Université de Bordeaux, 33000 Bordeaux, France., Delva F; Epicene Team, Bordeaux Population Health Research Center, INSERM UMR 1219, Université de Bordeaux, 33000 Bordeaux, France., Paris C; Service de Santé au Travail et Pathologie Professionnelle, CHU Rennes, 35000 Rennes, France.; Institut de Recherche en Santé, Environnement et Travail, INSERM U1085, 35000 Rennes, France., Ferretti G; INSERM U 1209 IAB, 38700 La Tronche, France.; Domaine de la Merci, Faculté de Médecine, Université Grenoble Alpes, 38706 La Tronche, France.; Service de Radiologie Diagnostique et Interventionnelle Nord, CHU Grenoble Alpes, CS 10217, 38043 Grenoble, France., Pairon JC; Equipe GEIC20, INSERM U955, 94000 Créteil, France.; Faculté de Santé, Université Paris-Est Créteil, 94000 Créteil, France.; Service de Pathologies Professionnelles et de l'Environnement, Centre Hospitalier Intercommunal Créteil, Institut Santé-Travail Paris-Est, 94000 Créteil, France.; Institut Interuniversitaire de Médecine du Travail de Paris-Ile de France, 94000 Créteil, France., Laurent F; Faculté de Médecine, Université de Bordeaux, 33000 Bordeaux, France.; Service d'Imagerie Médicale Radiologie Diagnostique et Thérapeutique, CHU de Bordeaux, 33000 Bordeaux, France.; Centre de Recherche Cardio-Thoracique de Bordeaux, INSERM U1045, Université de Bordeaux, 33000 Bordeaux, France.
Jazyk:	angličtina
Zdroj:	International journal of environmental research and public health [Int J Environ Res Public Health] 2022 Jan 27; Vol. 19 (3). Date of Electronic Publication: 2022 Jan 27.
DOI:	10.3390/ijerph19031417
Abstrakt:	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.
Databáze:	MEDLINE
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