Journal article
Deep Learning Estimation of Small Airways Disease from Inspiratory Chest Computed Tomography: Clinical Validation, Repeatability, and Associations with Adverse Clinical Outcomes in Chronic Obstructive Pulmonary Disease
American journal of respiratory and critical care medicine, Vol.211(7), pp.1185-1195
07/2025
DOI: 10.1164/rccm.202409-1847OC
PMCID: PMC12264693
PMID: 40072247
Abstract
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, compare it with dual-volume parametric response mapping fSAD (fSADPRM), and assess its clinical associations and repeatability in chronic obstructive pulmonary disease (COPD). Methods: We analyzed 2513 participants from the SubPopulations and InteRmediate Outcome Measures in COPD Study (SPIROMICS). Using a randomly sampled 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 showed a strong correlation with fSADPRM in both SPIROMICS (Pearson’s R = 0.895) and COPDGene (R = 0.897) cohorts. Higher fSADTLC levels were significantly associated with lower lung function, including lower postbronchodilator FEV1 (L) and FEV1/FVC ratio, and poorer quality of life reflected by higher total SGRQ scores, independent of percent CT emphysema. In SPIROMICS, individuals with higher fSADTLC experienced an annual decline in FEV1 of 1.156 mL (relative decrease; 95% CI: 0.613, 1.699; P < 0.001) per year for every 1% increase in fSADTLC. The rate of decline in COPDGene was slightly lower at 0.866 mL / year (relative decrease; 95% CI: 0.345, 1.386; P < 0.001) for percent increase in fSADTLC. Inspiratory fSADTLC demonstrated greater consistency between repeated measurements with a higher intraclass correlation coefficient (ICC) of 0.99 (95% CI: 0.98, 0.99) compared to fSADPRM [ICC: 0.83 (95% CI: 0.76, 0.88)]. Conclusions: Small airways disease can be reliably assessed from a single inspiratory CT scan using generative AI, eliminating the need for an additional expiratory CT scan. fSAD estimation from inspiratory CT correlates strongly with fSADPRM, demonstrates a significant association with FEV1 decline, and offers greater repeatability.
Details
- Title: Subtitle
- Deep Learning Estimation of Small Airways Disease from Inspiratory Chest Computed Tomography: Clinical Validation, Repeatability, and Associations with Adverse Clinical Outcomes in Chronic Obstructive Pulmonary Disease
- Creators
- Muhammad F A Chaudhary - University of Iowa, Roy J. Carver Department of Biomedical EngineeringHira A Awan - University of IowaSarah E Gerard - Harvard UniversitySandeep Bodduluri - University of Alabama at BirminghamAlejandro P Comellas - University of IowaIgor Barjaktarevic - University of California, Los AngelesR Graham Barr - Columbia UniversityChristopher B Cooper - Harbor–UCLA Medical CenterCraig J Galban - Michigan UnitedMeiLan Han - Michigan MedicineJeffrey L Curtis - Michigan MedicineNadia N Hansel - Johns Hopkins UniversityJerry A Krishnan - University of Illinois Urbana-ChampaignMartha G Menchaca - University of Illinois Urbana-ChampaignFernando J Martinez - Cornell UniversityJill Ohar - Wake Forest UniversityLuis G Vargas Buonfiglio - University of UtahRobert Paine III - University of UtahSurya P Bhatt - University of Alabama at BirminghamEric A Hoffman - University of IowaJoseph M Reinhardt - University of Iowa
- Resource Type
- Journal article
- Publication Details
- American journal of respiratory and critical care medicine, Vol.211(7), pp.1185-1195
- DOI
- 10.1164/rccm.202409-1847OC
- PMID
- 40072247
- PMCID
- PMC12264693
- NLM abbreviation
- Am J Respir Crit Care Med
- ISSN
- 1073-449X
- eISSN
- 1535-4970
- Publisher
- AMER THORACIC SOC; NEW YORK
- Grant note
- NHLBI Division of Intramural Research: R01 HL142625 NHLBI: U01 HL089897, U01 HL089856 NIH: 75N92023D00011 Roy J. Carver Charitable Trust: 19-5154 NIH/NHLBI: HHSN268200900013C, HHSN268200900014C, HHSN268200900015C, HHSN268200900016C, HHSN268200900017C, HHSN268200900018C, HHSN26820-0900019C, HHSN268200900020C, U01 HL137880, U24 HL141762
Supported by NHLBI Division of Intramural Research grant R01 HL142625 and NHLBI grants U01 HL089897 and U01 HL089856, by NIH contract 75N92023D00011, and by Roy J. Carver Charitable Trust grant 19-5154. SPIROMICS was supported by NIH/NHLBI contracts HHSN268200900013C, HHSN268200900014C, HHSN268200900015C, HHSN268200900016C, HHSN268200900017C, HHSN268200900018C, HHSN26820-0900019C, and HHSN268200900020C and grants U01 HL137880 and U24 HL141762.
- Language
- English
- Electronic publication date
- 03/12/2025
- Date published
- 07/2025
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Pulmonary, Critical Care, and Occupational Medicine; ICTS; Internal Medicine
- Record Identifier
- 9984800201202771
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