Electromagnetic navigation bronchoscopy-guided preoperative lung nodule localization in video-assisted thoracic surgery (VATS): a learning curve analysis.

IF 4 2区 医学 Q2 ONCOLOGY Translational lung cancer research Pub Date : 2024-10-31 Epub Date: 2024-10-28 DOI:10.21037/tlcr-24-337
Menghua Xue, Ke Lan, Xiaolong Yan, Tao Jiang, Xiaoping Wang, Feng Tian, Yunfeng Ni, Jinbo Zhao
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Abstract

Background: Electromagnetic navigation bronchoscopy (ENB) has been widely used to mark small peripheral pulmonary nodules (PPNs) in video-assisted thoracic surgery (VATS) resection. This technique offers the advantages of a high accuracy and fewer complications. However, few studies have analyzed the learning curve of ENB-guided preoperative localization. We aimed to describe the learning curve and factors influencing ENB-guided thoracoscopic pulmonary nodule resection.

Methods: This study included 300 consecutive patients with PPNs who underwent ENB-guided localization by the same endoscopist in our department between November 2019 and December 2021. The cumulative sum (CUSUM) method was used to analyze the learning curve of ENB-guided localization and the learning curve in different lobes, while logistic regression was used to analyze the risk factors affecting ENB operative time (OT).

Results: In 184 patients with 300 nodules, three learning phases were identified through turning points of the learning curve: Phase I (the 16th nodule), Phase II (the 17th to the 107th nodule), and Phase III (the 107th to the 300th nodule). No significant difference was found in the success rate of ENB-guided localization in each phase of the learning curve (100%, 96.7%, and 97.9%, P=0.78). The distance from the localization to the pleura in Phase I was statistically significantly shorter than that in Phase II and Phase III (0.6±0.4 vs. 1.1±0.6 vs. 1.0±0.5 cm, P=0.001 and P=0.003). Furthermore, the learning curves for nodules in different lobes were different. The learning curve for the upper lobe nodules was divided into two phases; the learning curve for the middle lobe disclosed more negative values; and the learning curve for the lower lobe nodules displayed no obvious pattern. Significant differences were found in nodule location, distance from the localization to the pleura and learning curve phase (P=0.003, P<0.001, P=0.02). The independent factors for OT included gender, smoking history, nodule type, distance from localization to the pleura, and learning curve phase.

Conclusions: ENB OT at the 107th nodule leveled off and showed a downward trend. Different lobes have different learning curves, the middle lobe is the easiest lobe to learn with ENB and can be used as the first lobe of choice for beginners. The learning curve can objectively evaluate the accuracy of ENB location and help endoscopists identify areas for improvement.

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视频辅助胸腔镜手术(VATS)中电磁导航支气管镜引导的术前肺结节定位:学习曲线分析。
背景:电磁导航支气管镜(ENB)已被广泛用于在视频辅助胸腔镜手术(VATS)切除术中标记周围肺小结节(PPN)。该技术具有精确度高、并发症少等优点。然而,很少有研究分析 ENB 引导下术前定位的学习曲线。我们旨在描述ENB引导下胸腔镜肺结节切除术的学习曲线和影响因素:本研究纳入2019年11月至2021年12月期间在我科由同一内镜医师进行ENB引导定位的连续300例PPN患者。采用累积总和(CUSUM)法分析ENB引导定位的学习曲线和不同肺叶的学习曲线,同时采用逻辑回归分析影响ENB手术时间(OT)的风险因素:在184例300个结节的患者中,通过学习曲线的转折点确定了三个学习阶段:第一阶段(第 16 个结节)、第二阶段(第 17 个至第 107 个结节)和第三阶段(第 107 个至第 300 个结节)。在学习曲线的每个阶段,ENB 引导定位的成功率均无明显差异(100%、96.7% 和 97.9%,P=0.78)。在统计学上,第一阶段的定位到胸膜的距离明显短于第二阶段和第三阶段(0.6±0.4 vs. 1.1±0.6 vs. 1.0±0.5 cm,P=0.001 和 P=0.003)。此外,不同肺叶结节的学习曲线也不同。上叶结节的学习曲线分为两个阶段;中叶结节的学习曲线显示出更多的负值;而下叶结节的学习曲线则无明显规律。结节位置、定位点到胸膜的距离和学习曲线阶段存在显著差异(P=0.003,PConclusions:ENB OT 在第 107 个结节处趋于平稳,并呈下降趋势。不同的肺叶有不同的学习曲线,中叶是最容易学习 ENB 的肺叶,可作为初学者的首选肺叶。学习曲线可以客观地评估 ENB 定位的准确性,帮助内镜医师找出需要改进的地方。
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来源期刊
CiteScore
7.20
自引率
2.50%
发文量
137
期刊介绍: Translational Lung Cancer Research(TLCR, Transl Lung Cancer Res, Print ISSN 2218-6751; Online ISSN 2226-4477) is an international, peer-reviewed, open-access journal, which was founded in March 2012. TLCR is indexed by PubMed/PubMed Central and the Chemical Abstracts Service (CAS) Databases. It is published quarterly the first year, and published bimonthly since February 2013. It provides practical up-to-date information on prevention, early detection, diagnosis, and treatment of lung cancer. Specific areas of its interest include, but not limited to, multimodality therapy, markers, imaging, tumor biology, pathology, chemoprevention, and technical advances related to lung cancer.
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