TY - JOUR T1 - Chronic lung allograft dysfunction phenotype and prognosis by machine learning CT analysis JF - European Respiratory Journal JO - Eur Respir J DO - 10.1183/13993003.01652-2021 SP - 2101652 AU - Micheal C. McInnis AU - Jin Ma AU - Gauri Rani Karur AU - Christian Houbois AU - Liran Levy AU - Jan Havlin AU - Eyal Fuchs AU - Jussi Tikkanen AU - Chung-Wai Chow AU - Ella Huszti AU - Tereza Martinu Y1 - 2021/01/01 UR - http://erj.ersjournals.com/content/early/2021/11/25/13993003.01652-2021.abstract N2 - Background Chronic lung allograft dysfunction (CLAD) is the principal cause of graft failure in lung transplant recipients and prognosis depends on CLAD phenotype. We used machine learning computed tomography (CT) lung texture analysis tool at CLAD diagnosis for phenotyping and prognostication compared to radiologists’ scoring.Methods This retrospective study included all adult first double-lung transplant patients (01/2010–12/2015) with CLAD (censored 12/2019) and inspiratory CT near CLAD diagnosis. The machine learning tool quantified ground-glass opacity, reticulation, hyperlucent lung, and pulmonary vessel volume (PVV). Two radiologists scored for ground-glass opacity, reticulation, consolidation, pleural effusion, air trapping and bronchiectasis. Receiver operating characteristic curve analysis was used to evaluate the diagnostic performance of machine learning and radiologist for CLAD phenotype. Multivariable Cox proportional-hazards regression analysis for allograft survival controlled for age, sex, native lung disease, cytomegalovirus serostatus, and CLAD phenotype (bronchiolitis obliterans syndrome [BOS] and restrictive allograft syndrome [RAS]/mixed).Results 88 patients were included (57 BOS, 20 RAS/mixed, and 11 unclassified/undefined) with CT a median 9.5 days from CLAD onset. Radiologist and machine learning parameters phenotyped RAS/mixed with PVV as the strongest indicator (AUC 0.85). Machine learning hyperlucent lung phenotyped BOS using only inspiratory CT (AUC=0.76). Radiologist and machine learning parameters predicted graft failure in the multivariable analysis, best with PVV (HR=1.23, 95%CI 1.05–1.44, p=0.01).Conclusions Machine learning discriminated between CLAD phenotypes on CT. Both radiologist and machine learning scoring were associated with graft failure, independent of CLAD phenotype. PVV, unique to machine learning, was the strongest in phenotyping and prognostication.FootnotesThis manuscript has recently been accepted for publication in the European Respiratory Journal. It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJ online. Please open or download the PDF to view this article.Conflict of Interest: Christian P. Houbois reports grants from German Research Foundation (project number 419344766), outside the submitted work. Tereza Martinu reports grants from Canadian Institutes of Health Research, Cystic Fibrosis Foundation, Ontario Thoracic Society, National Institutes of Health, Sanofi; receipt of materials for experiments rom APCBio; outside the submitted work. All other authors have nothing to disclose. ER -