Abstract: Lung transplantation is a life-saving treatment for individuals suffering from end-stage lung diseases. The number of lung transplants is increasing annually, and over 50% of worldwide lung transplants are performed in the United States. However, the long-term survival of lung transplant patients lags behind other solid organ transplants. To improve the lung transplant outcome and optimize the allocation of donor lungs, it is essential to identify factors that are associated with transplant outcomes. We propose to systematically validate a new concept called "Bio-Geo-Composition" as a potential biomarker for assessing lung transplant candidates. Our goal is to develop an automated frailty and fitness scoring system to objectively assess a candidate's fitness for a lung transplant, which we call the “Pittsburgh Transplant Fitness Score.” The PTFS will be designed to accurately predict the intra- and post-operative outcomes primarily based on recipients' pre-transplant chest computed tomography (CT) scans. The Bio-Geo-Composition concept assesses an individual's biological and geometric attributes through three components: body tissues, lung characteristics, and thoracic geometry. We will use advanced automated algorithms to comprehensively quantify Bio-Geo-Composition features depicted on pre-transplant CT images and analyze their association with transplant outcomes during and after the surgery. The significant factors will be integrated as a computer model with other patient characteristics to produce the PTFS. The model will optimize to predict intraoperative complications (e.g., delayed chest closure), postoperative complications (e.g., primary graft dysfunction, postoperative mechanical support, and ICU stay), and survival. Awareness of the potential factors contributing to unfitness will allow for pre-transplant care to be tailored to address these issues with the aim of improving fitness and maximizing the benefit of lung transplants.