Minimally invasive thoracic surgery has advanced the treatment of lung cancer since its introduction in the 1990s. Video-assisted thoracoscopic surgery (VATS) and robotic-assisted thoracic surgery (RATS) offer the advantage of smaller incisions without compromising patient outcomes. These techniques have been shown to be safe and effective in standard pulmonary resections (lobectomy and sub-lobar resection) and in complex pulmonary resections (sleeve resection and pneumonectomy). Furthermore, several studies show these techniques enhance patient outcomes from early recovery to improved quality of life (QoL) and excellent oncologic results. The rise of RATS has yielded further operative benefits compared to thoracoscopic surgery. The wristed instruments, neutralization of tremor, dexterity, and magnification allow for more precise and delicate dissection of tissues and vessels. This review summarizes of the advancements in minimally invasive thoracic surgery and the positive impact on patient outcomes.

: OBJECTIVES: Machine learning (ML) has great potential, but there are few examples of its implementation improving outcomes. The thoracic surgeon must be aware of pertinent ML literature and how to evaluate this field for the safe translation to patient care. This scoping review provides an introduction to ML applications specific to the thoracic surgeon. We review current applications, limitations and future directions. METHODS: A search of the PubMed database was conducted with inclusion requirements being the use of an ML algorithm to analyse patient information relevant to a thoracic surgeon and contain sufficient details on the data used, ML methods and results. Twenty-two papers met the criteria and were reviewed using a methodological quality rubric. RESULTS: ML demonstrated enhanced preoperative test accuracy, earlier pathological diagnosis, therapies to maximize survival and predictions of adverse events and survival after surgery. However, only 4 performed external validation. One demonstrated improved patient outcomes, nearly all failed to perform model calibration and one addressed fairness and bias with most not generalizable to different populations. There was a considerable variation to allow for reproducibility. CONCLUSIONS: There is promise but also challenges for ML in thoracic surgery. The transparency of data and algorithm design and the systemic bias on which models are dependent remain issues to be addressed. Although there has yet to be widespread use in thoracic surgery, it is essential thoracic surgeons be at the forefront of the eventual safe introduction of ML to the clinic and operating room.

Background: Lung cancer is the leading cause of cancer-related mortality in the United States, which disproportionately affect racial and ethnic minorities. Disparities in lung cancer screening, diagnosis, treatment, and survival outcomes are due to a complex interplay of socioeconomic factors, structural racism, and limited access to high-quality care. This review aims to examine the underlying causes of these disparities and explore potential mitigation strategies to improve lung cancer care equity. Methods: A review of the literature was conducted, evaluating racial and ethnic disparities in lung cancer care. Disparities in lung cancer screening, genomic testing, surgical and systemic treatment, and survival were explored. Additionally, interventional strategies such as risk-based screening, patient navigation programs, and policy reforms were examined. Results: Racial and ethnic minority patients are diagnosed at younger ages with fewer pack-years yet are less likely to qualify for screening under current guidelines. They receive lower rates of guideline-concordant treatment, including surgery, radiation, chemotherapy, and biomarker testing, and have reduced access to specialty care. Socioeconomic barriers, medical mistrust, and geographic disparities further contribute to these inequities. Targeted interventions, including mobile screening programs, financial assistance initiatives, and culturally competent care, have shown promise in improving lung cancer outcomes. Conclusion: A multi-level approach, incorporating healthcare policy changes, improved screening criteria, and an enhanced community engagement strategy, is essential for achieving equitable lung cancer care, ultimately improving outcomes for racial minority populations.