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

Ilyes Benlala, Baudouin Denis De Senneville, Gael Dournes, Morgane Menant, Celine Gramond, Isabelle Thaon, Bénédicte Clin, Patrick Brochard, Antoine Gislard, Pascal Andujar, Soizick Chammings, Justine Gallet, Aude Lacourt, Fleur Delva, Christophe Paris, Gilbert Ferretti, Jean-Claude Pairon and François Laurent
Additional contact information
Ilyes Benlala: Faculté de Médecine, Université de Bordeaux, 33000 Bordeaux, France
Baudouin Denis De Senneville: Mathematical Institute of Bordeaux (IMB), CNRS, INRIA, Bordeaux INP, UMR 5251, Université de Bordeaux, 33400 Talence, France
Gael Dournes: Faculté de Médecine, Université de Bordeaux, 33000 Bordeaux, France
Morgane Menant: Epicene Team, Bordeaux Population Health Research Center, INSERM UMR 1219, Université de Bordeaux, 33000 Bordeaux, France
Celine Gramond: Epicene Team, Bordeaux Population Health Research Center, INSERM UMR 1219, Université de Bordeaux, 33000 Bordeaux, France
Isabelle Thaon: Centre de Consultation de Pathologies Professionnelles, CHRU de Nancy, Université de Lorraine, 54000 Nancy, France
Bénédicte Clin: Service de Santé au Travail et Pathologie Professionnelle, CHU Caen, 14000 Caen, France
Patrick Brochard: Faculté de Médecine, Université de Bordeaux, 33000 Bordeaux, France
Antoine Gislard: Faculté de Médecine, Normandie Université, UNIROUEN, UNICAEN, ABTE, 76000 Rouen, France
Pascal Andujar: Equipe GEIC20, INSERM U955, 94000 Créteil, France
Soizick Chammings: Institut Interuniversitaire de Médecine du Travail de Paris-Ile de France, 94000 Créteil, France
Justine Gallet: Epicene Team, Bordeaux Population Health Research Center, INSERM UMR 1219, Université de Bordeaux, 33000 Bordeaux, France
Aude Lacourt: Epicene Team, Bordeaux Population Health Research Center, INSERM UMR 1219, Université de Bordeaux, 33000 Bordeaux, France
Fleur Delva: Epicene Team, Bordeaux Population Health Research Center, INSERM UMR 1219, Université de Bordeaux, 33000 Bordeaux, France
Christophe Paris: Service de Santé au Travail et Pathologie Professionnelle, CHU Rennes, 35000 Rennes, France
Gilbert Ferretti: INSERM U 1209 IAB, 38700 La Tronche, France
Jean-Claude Pairon: Equipe GEIC20, INSERM U955, 94000 Créteil, France
François Laurent: Faculté de Médecine, Université de Bordeaux, 33000 Bordeaux, France

IJERPH, 2022, vol. 19, issue 3, 1-13

Abstract: 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.

Keywords: artificial intelligence; pleural plaques; asbestos exposure (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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