Objective: We sought to assess the outcomes of octogenarians compared to a reference group of younger patients undergoing robotic anatomic lung resection for non-small cell lung cancer (NSCLC) to better understand the postoperative expectations of this elderly patient population. Methods: A prospectively maintained Institutional General Thoracic Surgery Database was queried for patients aged 80 years and above (octogenarians) and 65–75 years (reference group) undergoing robotic lobectomy or segmentectomy for NSCLC between 2015 and 2026. Clinical data and postoperative outcomes were collected and compared using both unadjusted and 1:1 propensity-score matching. Results: In total, 822 patients were identified: 101 in the octogenarian group (mean age 82.4 years [standard deviation 2.2]) and 721 in the reference group (69.7 [3.1], p < 0.001). Postoperative complications that were more frequent in octogenarians included pneumothorax requiring chest tube insertion (5.9% vs. 2.2%, p = 0.043), atrial arrhythmias (18.8% vs. 9.2%, p = 0.005), and urinary tract infections (6.9% vs. 1.7%, p = 0.005). Air leak, atelectasis, pneumonia, respiratory failure, pleural effusion, empyema, and delirium did not differ between the groups. After propensity matching, there were no significant differences in the aforementioned postoperative outcomes. While the unadjusted length of stay was longer in octogenarians (4 days [interquartile range 3–5] vs. 3 [2–5], p = 0.012), this was no longer significant after matching (4 [3–5] vs. 3 [2–5], p = 0.369). There were no statistically significant differences in 30-day readmission rates (p = 0.197) or 30-day mortality (p = 0.781) after propensity-score matching. Conclusions: Among carefully selected octogenarians undergoing robotic anatomic lung resection for NSCLC, no statistically significant differences in short-term postoperative outcomes were detected compared with patients aged 65–75 years after adjustment for baseline differences. These findings support individualized surgical selection beyond chronologic age alone.

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.

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.