Matthew C. Henn

Objective: To meet the growing demand of donor lungs, ex vivo lung perfusion (EVLP) is being used to further assess higher risk donor lungs. This study aims to identify the association of race mismatch between donor and recipient on survival in lung transplantation with EVLP. Methods: The UNOS database was queried for all lung transplants evaluated with EVLP from 2/2018 to 3/2025. Recipients with age < 18, multi‐organ transplants, re‐transplants, or missing data were excluded. Transplants were stratified into those with race concordance (RC) and mismatch (RM) between recipients and donors. Kaplan–Meier methods with log‐rank tests were used to assess survival differences. A multivariable cox regression model was created using select variables. Results: 1246 lung transplant recipients were identified: 638 in the RC group, 608 in the RM group. RC recipients were predominantly White (92.2% vs. 55.9%), had higher incidence of former smoking history (63.2% vs. 54.3%), and lower lung allocation scores. RC donors were more frequently White (92.2% vs. 31.6%) and DCD (38.6% vs. 29.9%). Post‐operative outcomes were similar between both RC and RM groups, only treated rejection in the first year was significantly higher in the former ( p = 0.005). Unadjusted survival did not differ between the groups ( p = 0.27). On multivariate cox‐proportional hazard modeling, RM was not independently predictive of mortality (HR 1.16 95% CI: 0.92–1.46) while recipient ECMO was the strongest predictor of mortality (HR 2.68, 95% CI: 1.71–4.20). Conclusions: For recipients who underwent lung transplantation following EVLP, race mismatch was not independently associated with mortality on adjusted analysis.

Accurate donor–recipient allograft size matching remains a critical determinant of outcomes in lung transplantation, yet current approaches rely predominantly on predicted total lung capacity (pTLC) and height-based metrics derived from population-based equations. These simplified surrogates fail to capture individual anatomical variability, disease-specific alterations in thoracic geometry, and the spatial relationship between donor lungs and recipient chest cavities. In this review, we examine the limitations of conventional size matching and synthesize emerging evidence supporting imaging-based approaches, including computed tomography (CT) volumetry, radiomics, and machine learning. CT-derived volumetric analysis enables individualized anatomical assessment and has been associated with clinically relevant prediction of primary graft dysfunction and mortality. Advanced computational methods may further support the extraction of imaging-derived features and integration with clinical data, although these approaches remain investigational. Collectively, these developments signal a paradigm shift from crude population-based metrics toward imaging-driven and computational approaches in the modern era. With rigorous validation and careful clinical integration, imaging-based approaches may complement conventional size metrics and support more individualized donor–recipient assessment.

Accurate donor-recipient allograft size matching remains a critical determinant of outcomes in lung transplantation, yet current approaches rely predominantly on predicted total lung capacity (pTLC) and height-based metrics derived from population-based equations. These simplified surrogates fail to capture individual anatomical variability, disease-specific alterations in thoracic geometry, and the spatial relationship between donor lungs and recipient chest cavities. In this review, we examine the limitations of conventional size matching and synthesize emerging evidence supporting imaging-based approaches, including computed tomography (CT) volumetry, radiomics, and machine learning. CT-derived volumetric analysis enables individualized anatomical assessment and has been associated with clinically relevant prediction of primary graft dysfunction and mortality. Advanced computational methods may further support extraction of imaging-derived features and integration with clinical data, although these approaches remain investigational. Collectively, these developments signal a paradigm shift from crude population-based metrics toward imaging-driven and computational approaches in the modern era. With rigorous validation and careful clinical integration, imaging-based approaches may complement conventional size metrics and support more individualized donor-recipient assessment.