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Multicenter Study
. 2022 Jan 27;19(3):1417.
doi: 10.3390/ijerph19031417.

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

Ilyes Benlala  1   2   3 , Baudouin Denis De Senneville  4 , Gael Dournes  1   2   3 , Morgane Menant  5 , Celine Gramond  5 , Isabelle Thaon  6 , Bénédicte Clin  7   8 , Patrick Brochard  1   9 , Antoine Gislard  10   11 , Pascal Andujar  12   13   14   15 , Soizick Chammings  15 , Justine Gallet  5 , Aude Lacourt  5 , Fleur Delva  5 , Christophe Paris  16   17 , Gilbert Ferretti  18   19   20 , Jean-Claude Pairon  12   13   14   15 , François Laurent  1   2   3
Affiliations
Multicenter Study

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

Ilyes Benlala et al. Int J Environ Res Public Health. .

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; asbestos exposure; pleural plaques.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Study Flow-Chart of selected patients with related Computed Tomography (CT). CT = computed tomography.
Figure 2
Figure 2
Axial MIP (5 mm) CT images of 80-year-old male. Left panel represents GT pleural plaques segmentation (green). Right panel represents AI pleural plaques segmentation (red). Note the different pleural plaques localization at three levels of the chest. Anterolateral PPs at the aortic arch level (A,B); Calcified PPs at the carina level (C,D) Posterolateral and diaphragmatic PPs at the lower chest (E,F). Legends: MIP = maximum intensity projection; CT = computed tomography; GT= ground truth; AI = artificial intelligence.
Figure 3
Figure 3
CT images of 71 years-old male at the 2nd (left panel) and the 3rd (right panel) CT screening rounds. Note the increase in pleural plaques volume ((E) 28.01 mL and (F) 49.25 mL), with the increase in calcifications (red arrows). (A,B) Axial native CT images (1 mm slice thickness); (C,D) Coronal native CT images; (E,F) 3D-volume rendering of CT images.
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