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山东大学学报 (医学版) ›› 2026, Vol. 64 ›› Issue (5): 74-82.doi: 10.6040/j.issn.1671-7554.0.2025.0895

• 临床医学 • 上一篇    

三维重建联合CT引导下穿刺定位在微创治疗肺磨玻璃结节中的应用

梁晨1,姚丽娟2,吕龙飞1,唐泽1,秦达1,崔有斌1,余孝淇1   

  1. 1.吉林大学第一医院胸外二科, 吉林 长春 130021;2.中国人民解放军93175部队门诊部, 吉林 长春 130051
  • 发布日期:2026-05-13
  • 通讯作者: 余孝淇. E-mails:yuxq23@mails.jlu.edu.cn
  • 基金资助:
    国家自然科学基金青年项目(82002429)

Application of 3D reconstruction combined with CT-guided puncture localization in minimally invasive treatment of pulmonary ground-glass nodules

LIANG Chen1, YAO Lijuan2, LYU Longfei1, TANG Ze1, QIN Da1, CUI Youbin1,YU Xiaoqi1   

  1. 1. Second Department of Thoracic Surgery, The First Hospital of Jilin University, Changchun 130021, Jilin, China;
    2. Department of Outpatient, 93175 Unit, Chinese Peoples Liberation Army, Changchun 130051, Jilin, China
  • Published:2026-05-13

摘要: 目的 比较人工智能(artificial intelligence, AI)辅助肺部CT三维重建联合CT引导下肺结节细针穿刺定位术前管理模式与常规胸腔镜手术术前管理模式在电视辅助胸腔镜手术(video-assisted thoracic surgery, VATS)治疗肺磨玻璃结节(ground-glass nodule, GGN)中的效果。 方法 收集2023年8月至2025年4月吉林大学第一医院胸外二科收治的114例行VATS亚肺叶切除术的GGN患者的临床资料,按照术前是否行AI辅助肺部CT三维重建及CT引导下肺结节穿刺定位,将其分为空白组(n=19)、穿刺组(n=25)、三维重建组(n=16)及联合组(n=54)。空白组术前仅行肺部增强CT检查,穿刺组和三维重建组在此基础上分别于术前行CT引导下肺结节细针穿刺定位或普通CT三维重建,联合组则综合利用了AI辅助肺部CT三维重建联合术前CT引导下肺结节细针穿刺定位。收集并比较以上4组穿刺定位成功率、围手术期相关指标(手术方式、手术时长、术中出血量、术后胸管留置时间、术后引流总量、住院时间、住院费用)等相关临床指标。 结果 联合组与空白组、穿刺组及三维重建组的一般临床资料差异无统计学意义;联合组、穿刺组及三维重建组在麻醉时间、引流总量、引流管留置时间、手术时长、术中出血量以及手术总费用6项术后指标均优于空白组(P<0.05);三维重建组与联合组的住院时间与病灶切除体积小于空白组(P<0.05),而联合组住院时间与病灶切除体积小于穿刺组(P<0.05);穿刺组与联合组术后感染指标降低天数均优于空白组(P<0.05);分析穿刺定位及三维重建组的交互效应结果表明,在麻醉时间、引流总量、引流管留置时间、手术时长、术中出血量及总费用等6项指标中,三维重建与穿刺定位存在交互相应,联合组在定位时间及患者耐受度评分上优于穿刺组。 结论 术前AI辅助CT三维重建联合CT引导下穿刺定位术前管理模式能显著提高VATS亚肺叶切除术围手术期效果,提高患者的生存质量及预后疗效,具有广泛的临床应用前景。

关键词: 人工智能, 三维重建, 穿刺定位, 电视辅助胸腔镜手术, 肺磨玻璃结节

Abstract: Objective To compare the therapeutic outcomes of an artificial intelligence(AI)-assisted lung CT three-dimensional reconstruction combined with CT-guided lung nodule fine-needle aspiration biopsy preoperative management model versus a conventional thoracoscopic surgery preoperative management model in the treatment of ground-glass nodules(GGN)via video-assisted thoracic surgery(VATS). Methods Clinical data were collected from 114 patients with GGN undergoing VATS sublobar resection between August 2023 and April 2025. Patients were categorized into four groups based on whether they underwent AI-assisted 3D reconstruction of pulmonary CT and CT-guided lung nodule localization preoperatively: the control group(n=19), the biopsy group(n=25), the 3D reconstruction group(n=16), and the combinaton group(n=54). The control group underwent only preoperative contrast-enhanced lung CT. The biopsy group and 3D reconstruction group additionally underwent CT-guided lung nodule fine-needle aspiration localization or conventional CT 3D reconstruction, respectively. The combination group utilized both AI-assisted lung CT 3D reconstruction and preoperative CT-guided lung nodule fine-needle aspiration localization. The following clinical indicators were collected and compared across the four groups: needle localization success rate, perioperative parameters(surgical approach, surgical duration, intraoperative blood loss, postoperative chest tube retention time, total postoperative drainage volume, length of hospital stay, and hospitalization costs). Results There were no statistically significant differences in general clinical data between the combination group and the control group, the puncture group, or the 3D reconstruction group. The combination group, puncture group, and 3D reconstruction group all demonstrated superior outcomes compared to the control group in six postoperative indicators: anesthesia duration, total drainage volume, drainage tube retention time, surgical duration, intraoperative blood loss, and total surgical cost(P<0.05). The 3D reconstruction group and combination group had shorter hospital stays and smaller lesion resection volumes than the control group(P<0.05), while the combination group had shorter hospital stays and smaller lesion resection volumes than the puncture group(P<0.05). Both the puncture group and combination group had fewer days of postoperative infection than the control group(P<0.05). There was an interaction between 3D reconstruction and needle localization in six indicators: anesthesia duration, total drainage volume, drainage tube retention time, surgical duration, intraoperative blood loss, and total cost. The combination group demonstrated superiority over the puncture group in localization time and patient tolerance scores. Conclusion The preoperative management model combining AI-assisted CT three-dimensional reconstruction with CT-guided needle localization significantly improves perioperative outcomes in VATS sublobar resection, enhances patient quality of life and prognosis, and holds broad clinical application prospects.

Key words: Artificial intelligence, Three-dimensional reconstruction, Puncture positioning, Video-assisted thoracoscopic surgery, Lung ground-glass nodule

中图分类号: 

  • R615
[1] Li C, Lei S, Ding L, et al. Global burden and trends of lung cancer incidence and mortality[J]. Chin Med J(Engl), 2023, 136(13): 1583-1590.
[2] Aberle DR, Adams AM, Berg CD, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening[J]. N Engl J Med, 2011, 365(5): 395-409.
[3] Suzuki K, Saji H, Aokage K, et al. Comparison of pulmonary segmentectomy and lobectomy: safety results of a randomized trial[J]. J Thorac Cardiovasc Surg, 2019, 158(3): 895-907.
[4] Cornella KN, Repper DC, Palafox BA, et al. A surgeons guide for various lung nodule localization techniques and the newest technologies[J]. Innovations(Phila), 2021, 16(1): 26-33.
[5] Ichinose J, Mun M, Matsuura Y, et al. Efficiency of thoracoscopic palpation in localizing small pulmonary nodules[J]. Surg Today, 2019, 49(11): 921-926.
[6] 王江南, 严卫亚, 丁学兵. 肺结节定位方式研究进展[J].中国医疗器械杂志, 2024, 48(2): 192-198, 227. WANG Jiangnan, YAN Weiya, DING Xuebing. Research progress on localization of pulmonary nudules [J]. Chinese Journal of Medical Instrumentation, 2024, 48(2): 192-198, 227.
[7] Li Z, Li R, Liu L, et al. The utility and feasibility of three-dimensional reconstruction in surgical planning for multiple pulmonary nodules: a prospective self-controlled study[J]. Transl Lung Cancer Res, 2025, 14(1): 194-208.
[8] Laven I, Oosterhoff VPS, Franssen A, et al. Evaluating three-dimensional lung reconstructions for thoracoscopic lung resections using open-source software: a pilot study[J]. Transl Lung Cancer Res, 2024, 13(7): 1595-1608.
[9] Hong Z, Lu Y, Sheng Y, et al. Comparison of three-dimensional reconstruction and CT-guided Hook-wire segmental resection for pulmonary nodules: a propensity score matching study[J]. World J Surg Oncol, 2023, 21(1): 161. doi: 10.1186/s12957-023-03035-4
[10] Chen K, Niu Z, Jin R, et al. Three-dimensional reconstruction computed tomography in thoracoscopic segmentectomy: a randomized controlled trial[J]. Eur J Cardiothorac Surg, 2024, 66(1): ezae250. doi: 10.1093/ejcts/ezae250
[11] Yamaguchi H, Sato M, Yamamoto K, et al. Virtual-assisted lung mapping in sublobar resection of small pulmonary nodules, long-term results[J]. Eur J Cardiothorac Surg, 2022, 61(4): 761-768.
[12] Rho J, Lee JW, Quan YH, et al. Fluorescent and iodized emulsion for preoperative localization of pulmonary nodules[J]. Ann Surg, 2021, 273(5): 989-996.
[13] McDermott S, Fintelmann FJ, Bierhals AJ, et al. Image-guided preoperative localization of pulmonary nodules for video-assisted and robotically assisted surgery[J]. Radiographics, 2019, 39(5): 1264-1279.
[14] Liu J, Jiang Y, He R, et al. Robotic-assisted navigation system for preoperative lung nodule localization: a pilot study[J]. Transl Lung Cancer Res, 2023, 12(11): 2283-2293.
[15] Lee NH, Chung HS, Cho JS, et al. Localization technique using mixture of indigo carmine and lipiodol of pulmonary nodule via bronchoscopic navigation[J]. Medicina(Kaunas), 2022, 58(9): 1235. doi: 10.3390/medicina58091235
[16] Galetta D, Rampinelli C, Funicelli L, et al. Computed tomography-guided percutaneous radiotracer localization and resection of indistinct/small pulmonary lesions[J]. Ann Thorac Surg, 2019, 108(3): 852-858.
[17] Doo KW, Yong HS, Kim HK, et al. Needlescopic resection of small and superficial pulmonary nodule after computed tomographic fluoroscopy-guided dual localization with radiotracer and hookwire[J]. Ann Surg Oncol, 2015, 22(1): 331-337.
[18] Anayama T, Qiu J, Chan H, et al. Localization of pulmonary nodules using navigation bronchoscope and a near-infrared fluorescence thoracoscope[J]. Ann Surg Oncol, 2015, 99(1): 224-230.
[19] Ciriaco P, Negri G, Puglisi A, et al. Video-assisted thoracoscopic surgery for pulmonary nodules: rationale for preoperative computed tomography-guided hookwire localization[J]. Eur J Cardiothorac Surg, 2004, 25(3): 429-433.
[20] 孙毅, 李丽, 刘长清, 等. 术前CT引导穿刺定位辅助胸腔镜手术治疗肺小结节的效果及对定位相关并发症的影响[J]. 影像研究与医学应用, 2024, 8(4): 14-16. SUN Yi, LI Li, LIU Changqing, et al. The effect of preoperative CT guided puncture localization assisted thoracoscopic surgery for small pulmonary nodules and its impact on location-related complications [J]. Journal of Imaging Research and Medical Applications, 2024, 8(4): 14-16.
[21] 中华医学会呼吸病学分会. 早期肺癌诊断中国专家共识(2023年版)[J]. 中华结核和呼吸杂志, 2023, 46(1): 1-18. Chinese Thoracic Society. Chinese expert consensus on diagnosis of early lung cancer(2023 Edition)[J]. Chinese Journal of Tuberculosis and Respiratory Diseases, 2023, 46(1): 1-18.
[22] 李镭, 刘丹, 朱盈盈, 等. 肺磨玻璃结节临床研究进展[J].中国肺癌杂志, 2016, 19(2): 102-107. LI Lei, LIU Dan, ZHU Yingying, et al. Overview of clinical progress in pulmonary ground-glass nodules[J]. Chinese Journal of Lung Cancer, 2016, 19(2): 102-107.
[23] Avery E, Sanelli PC, Aboian M, et al. Radiomics: aprimer on processing workflow and analysis[J]. Semin Ultrasound CT MR, 2022, 43(2): 142-146.
[24] 中华医学会肿瘤学分会. 中华医学会肺癌临床诊疗指南(2024版)[J]. 中华肿瘤杂志, 2024, 104(34): 3175-3213. Oncology Society of Chinese Medical Association. Chinese Medical Association guideline for clinical diagnosis and treatment of lung cancer(2024 edition)[J]. Chinese Journal of Oncology, 2024, 104(34): 3175-3213.
[25] Wood DE, Kazerooni EA, Aberle DR, et al. NCCN guidelines® insights: lung cancer screening, version 1.2025[J]. J Natl Compr Canc Netw, 2025, 23(1): e250002. doi: 10.6004/jnccn.2025.0002
[26] Riely GJ, Wood DE, Ettinger DS, et al. Non-small cell lung cancer, version 4.2024, NCCN clinical practice guidelines in oncology[J]. J Natl Compr Canc Netw, 2024, 22(4): 249-274.
[27] Park CH, Han K, Hur J, et al. Comparative effectiveness and safety of preoperative lung localization for pulmonary nodules: asystematic review and meta-analysis[J]. Chest, 2017, 151(2): 316-328.
[28] 韩丁培, 杨溯, 陈香, 等. 多种定位方法在亚肺叶切除治疗肺小结节中的临床应用现状[J]. 中国胸心血管外科临床杂志, 2024, 31(1): 160-165. HAN Dingpei, YANG Su, CHEN Xiang, et al. Clinical application status of multiple localization methods in the treatment of pulmonary nodules by sub-lobectomy[J]. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2024, 31(1): 160-165.
[29] 孟繁茂. CT引导下经皮穿刺亚甲蓝+生物胶辅助肺结节定位的安全性、有效性及其影响因素研究[D]. 北京: 北京协和医学院, 2024.
[30] 陆志斌, 陆龙, 周存荣, 等. CT辅助下穿刺亚甲蓝染色且不留置带倒钩导丝在外周肺结节手术定位中的应用[J]. 河南外科学杂志, 2025, 31(1): 1-3. LU Zhibin, LU Long, ZHOU Cunrong, et al. Application of CT-guided puncture and methylene blue staining without indwelling fine guide wire with reverse Hook in surgical localization of peripheral pulmonary nodules[J]. Henan Journal of Surgery, 2025, 31(1): 1-3.
[31] 桑池学, 黄大侠, 郑舒豪, 等. 空间三角定位法精准引导CT下经皮肺穿刺活检术的临床应用研究[J]. 中国CT和MRI杂志, 2025, 23(1): 66-69. SANG Chixue, HUANG Daxia, ZHENG Shuhao, et al. The clinical application of spatial triangulation guided percutaneous lung biopsy under CT[J]. Chinese Journal of CT and MRI, 2025, 23(1): 66-69.
[32] 陈光耀, 陈海琳, 刘海华, 等. CT引导下经皮穿刺钢丝爪钩定位技术在胸腔镜肺结节切除术中的应用[J]. 南通大学学报(医学版), 2023, 43(6): 566-568.
[33] 付瑞壮. 术前CT引导Hook-wire穿刺定位对胸腔镜切除肺小结节的临床价值[J]. 中国药物经济学, 2024, 19(S1): 80-82.
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