Lung transplant candidate selection relies on assessment of physiologic reserve, with body mass index commonly used to guide eligibility despite its limitations in distinguishing muscle from adipose tissue. CT-derived body composition analysis enables precise quantification of skeletal muscle and fat compartments. This study evaluates the association between CT-derived body composition metrics and transplant committee decisions. We performed a retrospective cohort study of 704 adult lung transplant candidates evaluated from 2019 to 2024 with available preoperative abdominal CT imaging at a single academic institution. CT scans were analyzed using an automated deep learning workflow to quantify skeletal muscle, visceral fat, and subcutaneous fat at the third lumbar vertebral level. Metrics were normalized by age, sex, and race reference values. Patients were categorized as approved or denied using transplant committee decisions. Multivariable logistic regression with likelihood ratio testing was used to identify independent predictors of approval. Of 704 patients, 437 (62%) were approved and 267 (38%) were denied. Greater skeletal muscle area was independently associated with increased odds of transplant approval (per 50 cm: OR 2.969, 95% CI 1.728-5.321, <0.001). Visceral fat, subcutaneous fat, and body mass index category were not associated with approval. Skeletal muscle was lower in patients labeled as frail/deconditioned and positively associated with 6-minute walk distance (<0.001). CT-derived skeletal muscle is independently associated with lung transplant approval and may provide an objective marker of physiologic reserve that is not captured by body mass index, with potential to improve risk stratification in transplant candidate selection.