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BMC Pulmonary Medicine
Published in: BMC Pulmonary Medicine 1/2024

Open Access 01-12-2024 | Chronic Obstructive Lung Disease | Research

Longitudinal assessment of interstitial lung abnormalities on CT in patients with COPD using artificial intelligence-based segmentation: a prospective observational study

Authors: Yusuke Shiraishi, Naoya Tanabe, Ryo Sakamoto, Tomoki Maetani, Shizuo Kaji, Hiroshi Shima, Satoru Terada, Kunihiko Terada, Kohei Ikezoe, Kiminobu Tanizawa, Tsuyoshi Oguma, Tomohiro Handa, Susumu Sato, Shigeo Muro, Toyohiro Hirai

Published in: BMC Pulmonary Medicine | Issue 1/2024

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Abstract

Background

Interstitial lung abnormalities (ILAs) on CT may affect the clinical outcomes in patients with chronic obstructive pulmonary disease (COPD), but their quantification remains unestablished. This study examined whether artificial intelligence (AI)-based segmentation could be applied to identify ILAs using two COPD cohorts.

Methods

ILAs were diagnosed visually based on the Fleischner Society definition. Using an AI-based method, ground-glass opacities, reticulations, and honeycombing were segmented, and their volumes were summed to obtain the percentage ratio of interstitial lung disease-associated volume to total lung volume (ILDvol%). The optimal ILDvol% threshold for ILA detection was determined in cross-sectional data of the discovery and validation cohorts. The 5-year longitudinal changes in ILDvol% were calculated in discovery cohort patients who underwent baseline and follow-up CT scans.

Results

ILAs were found in 32 (14%) and 15 (10%) patients with COPD in the discovery (n = 234) and validation (n = 153) cohorts, respectively. ILDvol% was higher in patients with ILAs than in those without ILA in both cohorts. The optimal ILDvol% threshold in the discovery cohort was 1.203%, and good sensitivity and specificity (93.3% and 76.3%) were confirmed in the validation cohort. 124 patients took follow-up CT scan during 5 ± 1 years. 8 out of 124 patients (7%) developed ILAs. In a multivariable model, an increase in ILDvol% was associated with ILA development after adjusting for age, sex, BMI, and smoking exposure.

Conclusion

AI-based CT quantification of ILDvol% may be a reproducible method for identifying and monitoring ILAs in patients with COPD.
Appendix
Available only for authorised users
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Metadata
Title
Longitudinal assessment of interstitial lung abnormalities on CT in patients with COPD using artificial intelligence-based segmentation: a prospective observational study
Authors
Yusuke Shiraishi
Naoya Tanabe
Ryo Sakamoto
Tomoki Maetani
Shizuo Kaji
Hiroshi Shima
Satoru Terada
Kunihiko Terada
Kohei Ikezoe
Kiminobu Tanizawa
Tsuyoshi Oguma
Tomohiro Handa
Susumu Sato
Shigeo Muro
Toyohiro Hirai
Publication date
01-12-2024
Publisher
BioMed Central
Published in
BMC Pulmonary Medicine / Issue 1/2024
Electronic ISSN: 1471-2466
DOI
https://doi.org/10.1186/s12890-024-03002-z

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