Deep Learning Estimation of Small Airways Disease from Inspiratory Chest CT is Associated with FEV 1 Decline in COPD.

Autor: Chaudhary MFA; The Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA, 52242.; Center for Lung Analytics and Imaging Research (CLAIR), Division of Pulmonary, Allergy and Critical Care Medicine, University of Alabama at Birmingham, Birmingham, AL 35294., Awan HA; The Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA, 52242., Gerard SE; The Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA, 52242., Bodduluri S; Center for Lung Analytics and Imaging Research (CLAIR), Division of Pulmonary, Allergy and Critical Care Medicine, University of Alabama at Birmingham, Birmingham, AL 35294., Comellas AP; Department of Internal Medicine, Division of Pulmonary, Critical Care and Occupational Medicine, University of Iowa Roy J. and Lucille A. Carver College of Medicine, Iowa City, IA, 52242., Barjaktarevic IZ; Division of Pulmonary and Critical Care Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095., Graham Barr R; Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, 10032., Cooper CB; Department of Physiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095., Galban CJ; Department of Radiology, University of Michigan, Ann Arbor, MI, 48109., Han MK; Division of Pulmonary and Critical Care Medicine, University of Michigan Health System, Ann Arbor, MI, 48109., Curtis JL; Division of Pulmonary and Critical Care Medicine, University of Michigan Health System, Ann Arbor, MI, 48109., Hansel NN; Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, 21205., Krishnan JA; Breathe Chicago Center, University of Illinois at Chicago, Chicago, IL, 60608., Menchaca MG; Department of Radiology, College of Medicine, University of Illinois at Chicago, Chicago, IL, 60612., Martinez FJ; University of Massachusetts Chan Medical School, Worcester, MA, 01655., Ohar J; Wake Forest School of Medicine, Wake Forest University, Winston-Salem, NC, 27101., Vargas Buonfiglio LG; Division of Respiratory, Critical Care and Occupational Pulmonary Medicine, University of Utah, Salt Lake City, UT, 84112., Paine R 3rd; Division of Respiratory, Critical Care and Occupational Pulmonary Medicine, University of Utah, Salt Lake City, UT, 84112., Bhatt SP; Center for Lung Analytics and Imaging Research (CLAIR), Division of Pulmonary, Allergy and Critical Care Medicine, University of Alabama at Birmingham, Birmingham, AL 35294., Hoffman EA; The Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA, 52242.; Department of Internal Medicine, Division of Pulmonary, Critical Care and Occupational Medicine, University of Iowa Roy J. and Lucille A. Carver College of Medicine, Iowa City, IA, 52242.; Department of Radiology, University of Iowa Roy J. and Lucille A. Carver College of Medicine, Iowa City, IA, 52242., Reinhardt JM; The Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA, 52242.; Department of Radiology, University of Iowa Roy J. and Lucille A. Carver College of Medicine, Iowa City, IA, 52242.
Jazyk: angličtina
Zdroj: MedRxiv : the preprint server for health sciences [medRxiv] 2024 Sep 11. Date of Electronic Publication: 2024 Sep 11.
DOI: 10.1101/2024.09.10.24313079
Abstrakt: Rationale: Quantifying functional small airways disease (fSAD) requires additional expiratory computed tomography (CT) scan, limiting clinical applicability. Artificial intelligence (AI) could enable fSAD quantification from chest CT scan at total lung capacity (TLC) alone (fSAD TLC ).
Objectives: To evaluate an AI model for estimating fSAD TLC and study its clinical associations in chronic obstructive pulmonary disease (COPD).
Methods: We analyzed 2513 participants from the SubPopulations and InteRmediate Outcome Measures in COPD Study (SPIROMICS). Using a subset ( n = 1055), we developed a generative model to produce virtual expiratory CTs for estimating fSAD TLC in the remaining 1458 SPIROMICS participants. We compared fSAD TLC with dual volume, parametric response mapping fSAD PRM . We investigated univariate and multivariable associations of fSAD TLC with FEV 1 , FEV 1 /FVC, six-minute walk distance (6MWD), St. George's Respiratory Questionnaire (SGRQ), and FEV 1 decline. The results were validated in a subset ( n = 458) from COPDGene study. Multivariable models were adjusted for age, race, sex, BMI, baseline FEV 1 , smoking pack years, smoking status, and percent emphysema.
Measurements and Main Results: Inspiratory fSAD TLC was highly correlated with fSAD PRM in SPIROMICS (Pearson's R = 0.895) and COPDGene (R = 0.897) cohorts. In SPIROMICS, fSAD TLC was associated with FEV 1 (L) (adj.β = -0.034, P < 0.001), FEV 1 /FVC (adj.β = -0.008, P < 0.001), SGRQ (adj.β = 0.243, P < 0.001), and FEV 1 decline (mL / year) (adj.β = -1.156, P < 0.001). fSAD TLC was also associated with FEV 1 (L) (adj.β = -0.032, P < 0.001), FEV 1 /FVC (adj.β = -0.007, P < 0.001), SGRQ (adj.β = 0.190, P = 0.02), and FEV 1 decline (mL / year) (adj.β = -0.866, P = 0.001) in COPDGene. We found fSAD TLC to be more repeatable than fSAD PRM with intraclass correlation of 0.99 (95% CI: 0.98, 0.99) vs. 0.83 (95% CI: 0.76, 0.88).
Conclusions: Inspiratory fSAD TLC captures small airways disease as reliably as fSAD PRM and is associated with FEV 1 decline.
Databáze: MEDLINE
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