首页|经验性与人工智能指导下精准肺段切除术效果比较的回顾性队列研究

经验性与人工智能指导下精准肺段切除术效果比较的回顾性队列研究

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目的 比较通过薄层CT二维图像进行胸腔镜下经验性肺段切除手术和通过人工智能(artificial intelligence,AI)软件进行规划的精准肺段切除手术的临床效果,为临床开展肺段切除术提供一些参考.方法 回顾性分析2019-2022年在安庆市立医院心胸外科完成的胸腔镜下肺段手术患者的临床资料.将2019年1月-2021年9月经验性肺段切除手术患者纳入A组,2021年10月-2022年12月精准肺段切除手术患者纳入B组.比较两组术前Hookwire定位针用量、满足肿瘤学标准患者比例、手术时间、术中出血量、术后胸腔引流时间、术后住院时间和中转开胸率.结果 共纳入患者322例.A组共158例,其中男56例、女102例,平均年龄(56.86±8.82)岁.B组共164例,其中男55例、女109例,平均年龄(56.69±9.05)岁.所有患者均顺利在胸腔镜下完成肺段切除手术,切缘不能满足肿瘤学标准患者进一步行扩大范围切除甚至肺叶切除手术,无围术期死亡.A组肺段切除使用定位针数量多于B组[47(29.7%)vs.9(5.5%),P<0.001],两组同期楔形切除使用定位针数量差异无统计学意义(P=0.572).A组中3例无法在切除靶段中找到病灶,10例切缘不足;B组中1例无法找到病灶,3例切缘不足;两组肺段切除后无法满足肿瘤学标准患者比例差异有统计学意义[13(8.2%)vs.4(2.4%),P=0.020].两组手术时间、术中出血量、术后胸腔引流管放置时间、术后住院时间及中转开胸等方面差异无统计学意义(P>0.05).结论 术前借助AI软件进行手术规划,能有效指导胸腔镜下解剖性肺段切除,在保证结节切缘符合肿瘤学要求的同时能显著减少肺结节定位针的数量.
Outcomes of empirical versus precise lung segmentectomy guided by artificial intelligence:A retrospective cohort study
Objective To compare the clinical application of empirical thoracoscopic segmentectomy and precise segmentectomy planned by artificial intelligence software,and to provide some reference for clinical segmentectomy.Methods A retrospective analysis was performed on the patients who underwent thoracoscopic segmentectomy in our department from 2019 to 2022.The patients receiving empirical thoracoscopic segmentectomy from January 2019 to September 2021 were selected as a group A,and the patients receiving precise segmentectomy from October 2021 to December 2022 were selected as a group B.The number of preoperative Hookwire positioning needle,proportion of patients meeting oncology criteria,surgical time,intraoperative blood loss,postoperative chest drainage time,postoperative hospital stay,and number of patients converted to thoracotomy between the two groups were compared.Results A total of 322 patients were collected.There were 158 patients in the group A,including 56 males and 102 females with a mean age of 56.86±8.82 years,and 164 patients in the group B,including 55 males and 109 females with a mean age of 56.69±9.05 years.All patients successfully underwent thoracoscopic segmentectomy,and patients whose resection margin did not meet the oncology criteria were further treated with extended resection or even lobectomy.There was no perioperative death.The number of positioning needles used for segmentectomy in the group A was more than that in the group B[47(29.7%)vs.9(5.5%),P<0.001].There was no statistical difference in the number of positioning needles used for wedge resection between the two groups during the same period(P=0.572).In the group A,the nodule could not be found in the resection target segment in 3 patients,and the resection margin was insufficient in 10 patients.While in the group B,the nodule could not be found in 1 patient,and the resection margin was insufficient in 3 patients.There was a statistical difference between the two groups[13(8.2%)vs.4(2.4%),P=0.020].There was no statistical difference between the two groups in terms of surgical time,intraoperative blood loss,duration of postoperative thoracic drainage,postoperative hospital stay,or conversion to open chest surgery(P>0.05).Conclusion Preoperative surgical planning performed with the help of artificial intelligence software can effectively guide the completion of thoracoscopic anatomical segmentectomy.It can effectively ensure the resection margin of pulmonary nodules meeting the oncological requirements and significantly reduce the number of positioning needles of pulmonary nodules.

Artificial intelligencelung segmentectomypulmonary nodulespreoperative localization

陈剑、詹必成、汤勇、刘永志、李根水、刘建

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安庆市立医院心胸外科(安徽安庆 246003)

人工智能 肺段切除术 肺结节 术前定位

安庆市科技局2021年度科研基金

2021Z2013

2024

中国胸心血管外科临床杂志
四川大学华西医院

中国胸心血管外科临床杂志

CSTPCD北大核心
影响因子:0.846
ISSN:1007-4848
年,卷(期):2024.31(10)
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