Introduction Quantitative CT (QCT) imaging-based metrics have quantified disease alterations in asthma and chronic obstructive pulmonary disease (COPD), respectively. We seek to characterise the similarity and disparity between these groups using QCT-derived airway and parenchymal metrics.
Methods Asthma and COPD subjects (former-smoker status) were selected with a criterion of post-bronchodilator FEV1 <80%. Healthy non-smokers were included as a control group. Inspiratory and expiratory QCT images of 75 asthmatic, 215 COPD and 94 healthy subjects were evaluated. We compared three segmental variables: airway circularity, normalised wall thickness and normalised hydraulic diameter, indicating heterogeneous airway shape, wall thickening and luminal narrowing, respectively. Using an image registration, we also computed six lobar variables including per cent functional small-airway disease, per cent emphysema, tissue fraction at inspiration, fractional-air-volume change, Jacobian and functional metric characterising anisotropic deformation.
Results Compared with healthy subjects, both asthma and COPD subjects demonstrated a decreased airway circularity especially in large and upper lobar airways, and a decreased normalised hydraulic diameter in segmental airways. Besides, COPD subjects had more severe emphysema and small-airway disease, as well as smaller regional tissue fraction and lung deformation, compared with asthmatic subjects. The difference of emphysema, small-airway disease and tissue fraction between asthma and COPD was more prominent in upper and middle lobes.
Conclusions Patients with asthma and COPD, with a persistent FEV1 <80%, demonstrated similar alterations in airway geometry compared with controls, but different degrees of alterations in parenchymal regions. Density-based metrics measured at upper and middle lobes were found to be discriminant variables between patients with asthma and COPD.
- functional small airway disease
- image registration
- quantitative computed tomography
- airway luminal narrowing
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Contributors Conception and design: SC. Acquisition of data: SC, BH, EAH, SEW, MC, SBF, SBF, NNJ, MLS, RGB., MKH, ERB, CBC, DC, NH, REK, EAK, EACK, FJM, WKO, PGW and C-LL. Analysis and interpretation of data and final approval of the version to be published: all authors. Drafting the article or revising it critically for important intellectual content: SC, JC, EAH, APC, MLS, REK, WKO and C-LL.
Funding This study was supported by the NIH grants: U01 HL114494, HL209152; R01HL094315, HL112986, HL69174, HL064368, HL091762, HL069116; S10RR022421; U10 HL109257, HL109168; UL1 RR024153 (CTSI), UL1 TR000448, UL1 TR000427 (CTSA), and by BasicScience Research Program through the National Research Foundation ofKorea (NRF) funded by the Ministry of Education(NRF-2017R1D1A1B03034157). SPIROMICS was supported bycontracts from the NIH/NHLBI (HHSN268200900013C, HHSN268200900014C,HHSN268200900015C, HHSN268200900016C, HHSN268200900017C,HHSN268200900018C, HHSN268200900019C, HHSN268200900020C), and supplemented by contributions made through the Foundation for the NIH and the COPD Foundation from AstraZeneca/MedImmune; Bayer; Bellerophon Therapeutics; Boehringer-Ingelheim Pharmaceuticals, Inc; Chiesi Farmaceutici S.p.A; Forest Research Institute, Inc; GlaxoSmithKline; Grifols Therapeutics, Inc; Ikaria, Inc; Nycomed GmbH; Takeda Pharmaceutical Company; Novartis Pharmaceuticals Corporation; ProterixBio; Regeneron Pharmaceuticals, Inc; Sanofi; and Sunovion.
Competing interests EAH is a shareholder in VIDA diagnostics, a company that is commercialising lung image analysis software derived by the University of Iowa lung imaging group. He is also a member of the Siemens CT advisory board. SBF receives grant funding from GE Healthcare.
Patient consent Obtained.
Ethics approval The imaging protocols for acquiring CT images were approved by the institutional review boards of the respective institutions.
Provenance and peer review Not commissioned; internally peer reviewed.
Data sharing statement No additional data are available.
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