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 (fSADTLC).
Objectives: To evaluate an AI model for estimating fSADTLC 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 fSADTLC in the remaining 1458 SPIROMICS participants. We compared fSADTLC with dual volume, parametric response mapping fSADPRM. We investigated univariate and multivariable associations of fSADTLC with FEV1, FEV1/FVC, six-minute walk distance (6MWD), St. George's Respiratory Questionnaire (SGRQ), and FEV1 decline. The results were validated in a subset (n = 458) from COPDGene study. Multivariable models were adjusted for age, race, sex, BMI, baseline FEV1, smoking pack years, smoking status, and percent emphysema.
Measurements and main results: Inspiratory fSADTLC was highly correlated with fSADPRM in SPIROMICS (Pearson's R = 0.895) and COPDGene (R = 0.897) cohorts. In SPIROMICS, fSADTLC was associated with FEV1 (L) (adj.β = -0.034, P < 0.001), FEV1/FVC (adj.β = -0.008, P < 0.001), SGRQ (adj.β = 0.243, P < 0.001), and FEV1 decline (mL / year) (adj.β = -1.156, P < 0.001). fSADTLC was also associated with FEV1 (L) (adj.β = -0.032, P < 0.001), FEV1/FVC (adj.β = -0.007, P < 0.001), SGRQ (adj.β = 0.190, P = 0.02), and FEV1 decline (mL / year) (adj.β = -0.866, P = 0.001) in COPDGene. We found fSADTLC to be more repeatable than fSADPRM with intraclass correlation of 0.99 (95% CI: 0.98, 0.99) vs. 0.83 (95% CI: 0.76, 0.88).
Conclusions: Inspiratory fSADTLC captures small airways disease as reliably as fSADPRM and is associated with FEV1 decline.