RESUMO
Bronchiolitis obliterans syndrome (BOS) after hematopoietic cell transplantation (HCT) is associated with substantial morbidity and mortality. Quantitative CT (qCT) can help diagnose advanced BOS meeting National Institutes of Health (NIH) criteria (NIH-BOS) but has not been used to diagnose early, often asymptomatic BOS (early BOS), limiting the potential for early intervention and improved outcomes. Using Pulmonary Function Tests (PFT) to define NIH-BOS, early BOS, and mixed BOS (NIH-BOS with restrictive lung disease) in patients from two large cancer centers, we applied qCT to identify early BOS and distinguish between types of BOS. Patients with transient impairment or healthy lungs were included for comparison. PFT were done at month 0, 6, and 12. Analysis was performed with association statistics, principal component analysis, conditional inference trees (CIT), and machine learning (ML) classifier models. Our cohort included 84 allogeneic HCT recipients -- 66 BOS (NIH-defined, early, or mixed) and 18 without BOS. All qCT metrics had moderate correlation with Forced Expiratory Volume in 1 second, and each qCT metric differentiated BOS from those without BOS (non-BOS) (P < 0.0001). CIT's distinguished 94% of participants with BOS versus non-BOS, 85% early BOS versus non-BOS, 92% early BOS versus NIH-BOS. ML models diagnosed BOS with area under the curve (AUC) 0.84 (95% confidence interval [CI] 0.74-0.94) and early BOS with AUC 0.84 (95% CI 0.69 - 0.97). Quantitative CT metrics can identify individuals with early BOS, paving the way for closer monitoring and earlier treatment in this vulnerable population.