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.
Highlights
The association between occupational asbestos exposure and the increase in benign and malignant respiratory diseases has been demonstrated through several studies in the last decades [1]
The study population consisted of 141 asbestos-exposed participants with pleural plaques at both the second and the third screening rounds after excluding 20 participants with unreadable CD-ROMs (Figure 1)
Stratified randomization based on the computed tomography (CT) scanner models was used to split the original CT dataset into two non-overlapping groups [31], i.e., Train cohort (n = 8280 CT slices from 138 CT examinations of 69 participants) and independent Test cohort (n = 2160 CT slices from 36 CT examinations of 18 participants) without crossvalidation
Summary
The association between occupational asbestos exposure and the increase in benign and malignant respiratory diseases has been demonstrated through several studies in the last decades [1]. Among these asbestos exposure manifestations, pleural plaques (PP) are the most frequent effects of such exposure [2,3,4]. Well-demarcated areas of thickening in the parietal pleura consisting of hyaline fibrosis [6,7]. They may have a smooth or irregular/nodular surface with the parenchyma, and they exhibit a soft-tissue attenuation with sometimes calcifications [1]. Studies [12,13]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Environmental Research and Public Health
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
