Quantitative Image Analysis at Chronic Lung Allograft Dysfunction Onset Predicts Mortality

Abstract

Background: Chronic lung allograft dysfunction (CLAD) phenotype determines prognosis and may have therapeutic implications. Despite the clarity achieved by recent consensus statement definitions, their reliance on radiologic interpretation introduces subjectivity. The Center for Computer Vision and Imaging Biomarkers at UCLA has established protocols for chest HRCT based computer-aided quantification of both interstitial disease and air-trapping. We applied quantitative image analysis (QIA) at CLAD onset to demonstrate radiographic phenotypes with clinical implications. Methods: We studied 47 first bilateral lung transplant recipients at UCLA with chest HRCT performed within 90 days of CLAD onset and 47 no-CLAD control HRCTs. QIA determined the proportion of lung volume affected by interstitial disease and air-trapping in total lung capacity and residual volume images, respectively. We compared QIA scores between no-CLAD and CLAD, and between phenotypes. We also assigned radiographic phenotypes based solely on QIA, and compared their survival outcomes. Results: CLAD onset HRCTs had more lung affected by interstitial disease (p=0.003) than no-CLAD controls. Bronchiolitis obliterans syndrome (BOS) cases had lower scores for interstitial disease as compared to probable restrictive allograft syndrome (RAS) (p<0.0001) and mixed CLAD (p=0.02) phenotypes. BOS cases had more air-trapping than probable RAS (p<0.0001). Among phenotypes assigned by QIA, the relative risk of death was greatest for mixed (RR 11.81), followed by RAS (RR 6.27) and BOS (RR 3.15). Conclusions: Chest HRCT QIA at CLAD onset appears promising as a method for precise determination of CLAD phenotypes with survival implications.