ArticleJournal of thoracic disease2026
Safe transition from video-assisted thoracic surgery to robotic-assisted thoracic surgery: early experience and stepwise learning curve of robotic-assisted thoracic surgery.
Article in Journal of thoracic disease, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
Background: Transitioning from video-assisted thoracic surgery (VATS) to robotic-assisted thoracic surgery (RATS) presents a significant learning curve, which may compromise patient safety during early adoption. This study reports the early experience of a single, highly experienced VATS surgeon utilizing a novel "stepwise hybrid approach" to safely implement a RATS program. Methods: A retrospective analysis was conducted on the first 41 consecutive patients who underwent robotic lung resection between February 2025 and February 2026. To mitigate risks, the initial three cases were performed using a stepwise hybrid technique, gradually increasing the proportion of robotic utilization while relying on conventional VATS for critical dissections. Clinical, surgical, and oncologic outcomes were evaluated, and the learning curve was analyzed. Results: The cohort consisted predominantly of lobectomies (95.1%). The mean total operation time was 225.1±52.3 minutes, and the mean console time was 114.9±45.4 minutes. The conversion rate to open surgery was low (4.9%). Postoperative recovery was stable with a mean hospital stay of 9.2±2.6 days. There was a 0% 30-day mortality rate, and major complications (Clavien-Dindo Grade IIIa or higher) occurred in only 7.3% of patients. Oncologically, a mean of 14.6±7.9 lymph nodes were harvested, achieving an R0 resection rate of 97.6%. Conclusions: This stepwise hybrid transition approach is feasible and demonstrated an acceptable perioperative profile within the context of an expert VATS surgeon introducing a structured robotic program. Rather than universally ensuring procedural safety across all surgical settings, this model provides a pragmatic educational framework to decouple initial robotic interface adaptation from acute clinical risks, though broader multi-center validation remains warranted.
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