Advances in the application of AI-assisted 3D reconstruction in thoracoscopic pulmonary nodule resection surgery_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

WANG Lei ^1,2 ,  XIANG Zhiming ²

1. Postgraduate Cultivation Base of Guangzhou University of Chinese Medicine, Panyu Central Hospital, Guangzhou, 510006, P. R. China;
2. Department of Radiology, The Affiliated Panyu Central Hospital of Guangzhou Medical University, Guangzhou, 511400, P. R. China;

Corresponding author：

XIANG Zhiming, Email: xiangzhiming@pyhospital.com.cn

Keywords：

Artificial intelligence; three-dimensional reconstruction; thoracoscopic surgery; pulmonary nodules; surgical planning; review

DOI：

10.7507/1007-4848.202404059

Video：

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Abstract Full text Figures/Tables Video References Cited by

In thoracoscopic pulmonary nodule resection surgery, precise preoperative planning is crucial. Artificial intelligence (AI)-assisted three-dimensional (3D) reconstruction technologies have shown great potential in this area. AI-assisted 3D reconstruction technologies can provide accurate, personalized models of the pulmonary vasculature and bronchial anatomy, assisting surgeons in detailed surgical planning and thus enhancing the precision and safety of surgeries. This article reviews the application progress of AI-assisted 3D reconstruction technologies in pulmonary nodule surgery, including their applications in preoperative diagnosis, surgical planning, and intraoperative navigation, as well as the advancements in AI-assisted 3D reconstruction technologies. It analyzes the technical features of all kinds of 3D reconstruction methods, their clinical applications, and the challenges they face.

Citation： WANG Lei, XIANG Zhiming. Advances in the application of AI-assisted 3D reconstruction in thoracoscopic pulmonary nodule resection surgery. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2025, 32(2): 252-257. doi: 10.7507/1007-4848.202404059 Copy

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1. 李迎晨, 赵明芳, 吴瑛. 《肺癌筛查: 美国预防临床服务指南工作组推荐声明》现状与最新指南解读. 中国全科医学, 2024, 27(11): 1283-1287.Li YC, Zhao MF, Wu Y. Current status of lung cancer screening and interpretation of the U.S. Preventive Services Task Force guidelines on lung cancer screening. Chin Gen Pract, 2024, 27(11): 1283-1287.
2. 秦娜, 马红霞, 靳光付, 等. 肺癌流行病学研究年度进展2022. 中华医学杂志, 2023, 103(14): 1068-1073.Qin N, Ma HX, Jin GF, et al. Annual progress in lung cancer epidemiology research 2022. Natl Med J Chin, 2023, 103(14): 1068-1073.
3. Xia C, Dong X, Li H, et al. Cancer statistics in China and United States, 2022: Profiles, trends, and determinants. Chin Med J (Engl), 2022, 135(5): 584-590.
4. Ettinger DS, Wood DE, Aisner DL, et al. Non-small cell lung cancer, version 3 2022, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw, 2022, 20(5): 497-530.
5. Iwamoto R, Tanoue S, Nagata S, et al. T1 invasive lung adenocarcinoma: Thin section CT solid score and histological periostin expression predict tumor recurrence. Mol Clin Oncol, 2021, 15(5): 1-10.
6. McWilliams A, Tammemagi MC, Mayo JR, et al. Probability of cancer in pulmonary nodules detected on first screening CT. N Engl J Med, 2013, 369(10): 910-919.
7. Liu B, Gu C. Expert consensus workshop report: Guidelines for preoperative assisted localization of small pulmonary nodules. J Canc Res Ther, 2020, 16(5): 967.
8. Cannone G, Verzeletti V, Busetto A, et al. Three-dimensional imaging-guided lung anatomic segmentectomy: A single-center preliminary experiment. Medicina (Kaunas), 2023, 59(12): 2079.
9. Kudo Y, Shimada Y, Matsubayashi J, et al. Artificial intelligence analysis of three-dimensional imaging data derives factors associated with postoperative recurrence in patients with radiologically solid-predominant small-sized lung cancers. Eur J Cardio-Thorac Surg, 2022, 61(4): 751-760.
10. Huang X, Yue S, Wang C, et al. Optimal three-dimensional reconstruction for lung cancer tissues. Technol Health Care, 2017, 25(S1): 423-434.
11. Li X, Wang X, Dai Y, et al. Supervised recursive segmentation of volumetric CT images for 3D reconstruction of lung and vessel tree. Comput Methods Programs Biomed, 2015, 122(3): 316-329.
12. Chen-Yoshikawa TF, Date H. Update on three-dimensional image reconstruction for preoperative simulation in thoracic surgery. J Thorac Dis, 2016, 8(Suppl 3): S295-S301.
13. Wu WB, Xu XF, Wen W, et al. Three-dimensional computed tomography bronchography and angiography in the preoperative evaluation of thoracoscopic segmentectomy and subsegmentectomy. J Thorac Dis, 2016, 8(Suppl 9): S710-S715.
14. Yokoyama Y, Sato M, Omasa M, et al. Three-dimensional imaging for thoracoscopic resection of complex lung anomalies. Surg Case Rep, 2017, 3(1): 106.
15. Sandri A, Gagliasso M, Veltri A, et al. Report of an interactive three-dimensional anatomical model to be used as an intraoperative aid in lung anatomical resections for non-small lung cancer. Interact Cardiovasc Thorac Surg, 2021, 33(2): 316-318.
16. 王腾腾, 刘宝东, 张毅, 等. 术前辅助定位联合CT三维重建在以肺结节为中心的单孔胸腔镜联合亚段/肺段切除术中的应用. 中国胸心血管外科临床杂志, 2023, 30(3): 364-368.Wang TT, Liu BD, Zhang Y, et al. Application of preoperative localization coupled with CT three-dimensional reconstruction in pulmonary nodule-centered uniportal thoracoscopic combined subsegmental/segmental resection. Chin J Clin Thorac Cardiovasc Surg, 2023, 30(3): 364-368.
17. 胡硕, 王琦, 魏海星, 等. 三维导航免穿刺和穿刺定位行解剖性肺段切除术治疗肺结节的回顾性队列研究. 中国胸心血管外科临床杂志, 2021, 28(10): 1202-1206.Hu S, Wang Q, Wei HX, et al. Puncture positioning versus free-of-puncture positioning under three-dimensional navigation in the anatomical segmentectomy for pulmonary nodules: A retrospective cohort study. Chin J Clin Thorac Cardiovasc Surg, 2021, 28(10): 1202-1206.
18. Zhang G, Xu D, Yu Z, et al. Preoperative non-invasive visual localization of synchronous multiple lung cancers using three-dimensional computed tomography lung reconstruction. J Cardiothorac Surg, 2021, 16(1): 273.
19. Chen Y, Zhang J, Chen Q, et al. Three-dimensional printing technology for localised thoracoscopic segmental resection for lung cancer: A quasi-randomised clinical trial. World J Surg Oncol, 2020, 18(1): 223.
20. 胡坚, 刘伦旭, 张毅, 等. 人工智能一体化三维重建应用于胸外科的中国专家共识. 中国胸心血管外科临床杂志, 2023, 30(5): 641-646.Hu J, Liu LX, Zhang Y, et al. Chinese expert consensus on the application of integrated 3D reconstruction with artificial intelligence in thoracic surgery. Chin J Clin Thorac Cardiovasc Surg, 2023, 30(5): 641-646.
21. 支修益, 胡坚, 刘伦旭, 等. 人工智能平台下肺结节的三维可视化定位与手术规划专家共识. 中国胸心血管外科临床杂志, 2019, 26(12): 1161-1166.Zhi XY, Hu J, Liu LX, et al. Expert consensus on three-dimensional visualization, localization, and surgical planning of pulmonary nodules using artificial intelligence platforms. Chin J Clin Thorac Cardiovasc Surg, 2019, 26(12): 1161-1166.
22. Fan Z, Zhao S, Wang L, et al. Comparison between functional lung volume measurement and segment counting for predicting postoperative pulmonary function after pulmonary resection in lung cancer patients. BMC Pulm Med, 2023, 23(1): 6.
23. Rui W, Yuhang S, Yang L, et al. A new method for evaluating lung volume: AI-3D reconstruction. Front Physiol, 2023, 14: 1217411.
24. Wu MT, Chang JM, Chiang AA, et al. Use of quantitative CT to predict postoperative lung function in patients with lung cancer. Radiology, 1994, 191(1): 257-262.
25. Guni A, Varma P, Zhang J, et al. Artificial intelligence in surgery: The future is now. Eur Surg Res, 2024. [Epub ahead of print].
26. van Rikxoort EM, van Ginneken B. Automated segmentation of pulmonary structures in thoracic computed tomography scans: A review. Phys Med Biol, 2013, 58(17): R187-R220.
27. Shimizu K, Nagashima T, Ohtaki Y, et al. Analysis of the variation pattern in right upper pulmonary veins and establishment of simplified vein models for anatomical segmentectomy. Gen Thorac Cardiovasc Surg, 2016, 64(10): 604-611.
28. Maki R, Miyajima M, Ogura K, et al. Anatomy of the left subsuperior segment for segmentectomy. Surg Today, 2022, 52(7): 1054-1062.
29. Zhou D, Gao Y, Wang H, et al. Prevalence and anatomical characteristics of subsuperior segment in lung lower lobe. J Thorac Cardiovasc Surg, 2023, 165(3): 864-872.
30. Zhang M, Mao N, Wang SH, et al. The B1 defective type of bifurcated right upper lobe bronchus. J Thorac Dis, 2019, 11(10): 4218-4223.
31. Martín-Ruiz S, Gutiérrez-Collar C, Forcén Vicente De Vera E, et al. The bronchial segmentation and its anatomical variations. A clinical-anatomic and bronchoscopy study. Ann Anat, 2021, 235: 151677.
32. Zhang M, Wu QC, Ge MJ. The VVBA method for thoracoscopic right middle lobe segmentectomy. Gen Thorac Cardiovasc Surg, 2021, 69(1): 175-177.
33. 于跃, 王伟, 叶俊, 等. 三维影像重建技术在胸外科规培生肺段解剖教学中的应用研究. 科技视界, 2019(1): 180-181, 151.Yue Y, Wang W, Ye J, et al. Application of three-dimensional imaging reconstruction technology in the teaching of pulmonary segmental anatomy for standardization training residents in the department of thoracic surgery. Sci Technol Vis, 2019(1): 180-181, 151.
34. 丁成, 方子尧, 沈子青, 等. 三维重建工具在胸外科教学中的应用探析—以全自动胸部CT三维重建为例. 教育教学论坛, 2023(28): 34-39.Ding C, Fang ZY, Shen ZQ, et al. Application of 3D reconstruction tools in thoracic surgery teaching: Taking the 3D reconstruction of fully automatic chest CT as an example. Educ Teach Forum, 2023(28): 34-39.
35. 王哲, 张广健, 张勇, 等. 虚拟3D肺脏模型在胸外科规培住院医师临床教学中的应用. 中国医学教育技术, 2017, 31(6): 697-699.Wang Z, Zhang GJ, Zhang Y, et al. Application of the virtual three-dimensional lung model in thoracic surgery clinical teaching. China Med Educ Technol, 2017, 31(6): 697-699.
36. 宋明磊, 曹富民, 邢晓英, 等. 交互式医学图像控制系统软件三维重建在肺腺癌复杂肺段切除术中的应用. 中华全科医学, 2022, 20(5): 735-739.Song ML, Cao FM, Xin XY, et al. Application of MIMICS software three-dimensional reconstruction in complex segmental resection of lung adenocarcinoma. Chin J Gen Pract, 2022, 20(5): 735-739.
37. 李爽, 施益军, 丁国文, 等. Mimics软件系统行三维重建指导胸腔镜下解剖性肺段切除术的临床应用分析. 中国胸心血管外科临床杂志, 2024, 31(1): 59-64.Li S, Shi YJ, Ding GW, et al. Clinical application of Mimics software system to three-dimensional reconstruction to guide thoracoscopic anatomic pulmonary segmentectomy. Chin J Clin Thorac Cardiovasc Surg, 2024, 31(1): 59-64.
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