Application effects of AI software and Mimics software for three dimensional reconstruction in thoracoscopic anatomic segmentectomy: A retrospective cohort study_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

SANG Chengpeng ¹ , ZHU Yi ¹ , WANG Yaqin ¹ , GONG Li ¹ , MIN Bo ¹ , HU Haibo ¹ ,  TANG Zhixian ²

1. Department of Thoracic and Cardiac Surgery, The Second People’s Hospital of Huai’an, Huai’an, Jiangsu, 223001, P. R. China;
2. Department of Cardiothoracic Surgery, The First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, 341000, P. R. China;

Corresponding author：

TANG Zhixian, Email: tzhixian2020@gmu.edu.cn

Keywords：

Artificial intelligence; three-dimensional reconstruction; segmentectomy; Mimics

DOI：

10.7507/1007-4848.202409004

Video：

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Objective To analyze the application effects of artificial intelligence (AI) software and Mimics software in preoperative three-dimensional (3D) reconstruction for thoracoscopic anatomical lung segment resection. Methods A retrospective analysis was conducted on patients who underwent thoracoscopic lung segment resection surgery at the Second People's Hospital of Huai'an from October 2019 to March 2024. Patients who underwent AI software 3D reconstruction were included in the AI group, those who underwent Mimics software 3D reconstruction were included in the Mimics group, and those who did not undergo 3D reconstruction were included in the control group. Perioperative related indicators of each group were compared. Results A total of 168 patients were included, including 73 males and 95 females, aged 25-81 (61.61±10.55) years. There were 79 patients in the AI group, 53 patients in the Mimics group, and 36 patients in the control group. There were no statistically significant differences in gender, age, smoking history, nodule size, number of lymph node dissection groups, postoperative pathological results, and postoperative complications among the three groups (P>0.05). There were statistically significant differences in operation time (P<0.001), extubation time (P<0.001), drainage volume (P<0.001), bleeding volume (P<0.001), and postoperative hospital stay (P=0.001) among the three groups. There were no statistically significant differences in operation time, extubation time, bleeding volume, and postoperative hospital stay between the AI group and the Mimics group (P>0.05). There was no statistically significant difference in drainage volume between the AI group and the control group (P>0.05). Conclusion For patients requiring thoracoscopic anatomical lung segment resection, preoperative 3D reconstruction and preoperative planning based on 3D images can shorten the operation time, postoperative extubation and hospital stay, and reduce intraoperative bleeding and postoperative drainage volume compared with reading CT images only. The use of AI software for 3D reconstruction is not inferior to Mimics manual 3D reconstruction in terms of surgical guidance and postoperative recovery, which can reduce the workload of clinicians and is worth promoting.

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2.	Zhou T, Sun C. Effect of physical manipulation pulmonary rehabilitation on lung cancer patients after thoracoscopic lobectomy. Thorac Cancer, 2022, 13(3): 308-315.
3.	王欢, 刘建, 陈剑, 等. 胸腔镜复杂肺段切除术治疗Ⅰ期非小细胞肺癌的围手术期效果. 中国胸心血管外科临床杂志, 2021, 28(5): 555-559.
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6.	Natale G, Leonardi B, Messina G, et al. Three-dimensional lung reconstructions for the localization of lung nodules to be resected during surgery. Thorac Cancer, 2023, 14(34): 3389-3396.
7.	Ma X, Lu T, Qin D, et al. Analysis of pulmonary artery variation based on 3D reconstruction of CT angiography. Front Physiol, 2023, 14: 1156513.
8.	Ikeda N, Yoshimura A, Hagiwara M, et al. Three dimensional computed tomography lung modeling is useful in simulation and navigation of lung cancer surgery. Ann Thorac Cardiovasc Surg, 2013, 19(1): 1-5.
9.	Mimae T, Miyata Y, Kumada T, et al. The intersegmental pulmonary vein is not always located on the intersegmental plane of the lung: Evaluation with 3-dimensional volume-rendering image reconstruction. JTCVS Tech, 2022 Sep 13: 16: 132-138.16-132.
10.	张望, 陈亮, 王俊, 等. DeepInsight软件术前肺部支气管血管成像的真实性研究. 中国肺癌杂志, 2021, 24(2): 88-93.
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13.	简伟健, 张欢, 王瑜, 等. 肺部支气管的分类方法、装置、电子设备和存储介质: CN202110321500. 5. 2022-02-22.
14.	黄文豪, 简伟健, 张欢, 等. 一种亚段支气管分级的方法、装置、设备及存储介质: CN202110501077. 7. 2021-10-08.
15.	郝智, 黄文豪, 简伟健, 等. 一种肺血管分段定位的方法、装置、设备及存储介质: CN202110501096. X. 2022-02-22.
16.	孙岩峰, 韦人, 邹彤, 等. 肺动脉图像分割方法及装置: CN202110706289. 9. 2022-04-19.
17.	刘恩佑, 张欢, 王少康, 等. 一种结构化分级管状结构血管的方法、装置及电子设备: CN202110397001. 4. 2022-02-22.
18.	Zheng RS, Chen R, Han BF, et al. Cancer incidence and mortality in China, 2022. Chin J Oncol, 2024, 46(3): 221-231.
19.	刘彦国, 王俊. Graham与第一例全肺切除术. 中华医史杂志, 2003, 3: 53.
20.	Christodoulides N, Fitzmaurice GJ, Bukowska I, et al. Post-pneumonectomy syndrome: A systematic review of the current evidence and treatment options. J Cardiothorac Surg, 2023, 18(1): 119.
21.	张艳娇, 高禹舜. 肺癌胸腔镜肺叶切除、胸腔镜肺段切除与开胸肺叶切除术后对肺功能影响的研究. 中国肺癌杂志, 2016, 19(10): 700-704.
22.	Saji H, Okada M, Tsuboi M, et al. Segmentectomy versus lobectomy in small-sized peripheral non-small-cell lung cancer (JCOG0802/WJOG4607L): A multicentre, open-label, phase 3, randomised, controlled, non-inferiority trial. Lancet, 2022, 399(10335): 1607-1617.
23.	Wu WB, Xia Y, Pan XL, et al. Three-dimensional navigation-guided thoracoscopic combined subsegmentectomy for intersegmental pulmonary nodules. Thorac Cancer, 2019, 10(1): 41-46.
24.	Ichinose J, Mun M, Matsuura Y, et al. Efficiency of thoracoscopic palpation in localizing small pulmonary nodules. Surg Today, 2019, 49(11): 921-926.
25.	Sato M. Precise sublobar lung resection for small pulmonary nodules: Localization and beyond. Gen Thorac Cardiovasc Surg, 2020, 68(7): 684-691.
26.	Zhang X, Yang D, Li L, et al. Application of three-dimensional technology in video-assisted thoracoscopic surgery sublobectomy. Front Oncol, 2024, 14: 1280075.
27.	Martin CM, Roach VA, Nguyen N, et al. Comparison of 3D reconstructive technologies used for morphometric research and the translation of knowledge using a decision matrix. Anat Sci Educ, 2013, 6(6): 393-403.
28.	Nam JG, Park S, Hwang EJ, et al. Development and validation of deep learning-based automatic detection algorithm for malignant pulmonary nodules on chest radiographs. Radiology, 2019, 290(1): 218-228.
29.	Ardila D, Kiraly AP, Bharadwaj S, et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med, 2019, 25(6): 954-961.
30.	Santos-García G, Varela G, Novoa N, et al. Prediction of postoperative morbidity after lung resection using an artificial neural network ensemble. Artif Intell Med, 2004, 30(1): 61-69.
31.	Chang YJ, Hung KC, Wang LK, et al. A real-time artificial intelligence-assisted system to predict weaning from ventilator immediately after lung resection surgery. Int J Environ Res Public Health, 2021, 18(5): 2713.
32.	Salati M, Migliorelli L, Moccia S, et al. A machine learning approach for postoperative outcome prediction: Surgical data science application in a thoracic surgery setting. World J Surg, 2021, 45(5): 1585-1594.
33.	Chen X, Wang Z, Qi Q, et al. A fully automated noncontrast CT 3-D reconstruction algorithm enabled accurate anatomical demonstration for lung segmentectomy. Thorac Cancer, 2022, 13(6): 795-803.
34.	Li X, Zhang S, Luo X, et al. Accuracy and efficiency of an artificial intelligence-based pulmonary broncho-vascular three-dimensional reconstruction system supporting thoracic surgery: Retrospective and prospective validation study. EBioMedicine, 2023 Jan: 87: 104422.
35.	Birkhoff DC, van Dalen ASHM, Schijven MP. A review on the current applications of artificial intelligence in the operating room. Surg Innov, 2021, 28(5): 611-619.
36.	Andras I, Mazzone E, van Leeuwen FWB, et al. Artificial intelligence and robotics: A combination that is changing the operating room. World J Urol, 2020, 38(10): 2359-2366.
37.	Ujiie H, Yamaguchi A, Gregor A, et al. Developing a virtual reality simulation system for preoperative planning of thoracoscopic thoracic surgery. J Thorac Dis, 2021, 13(2): 778-783.
38.	Sardari Nia P, Olsthoorn JR, Heuts S, et al. Interactive 3D reconstruction of pulmonary anatomy for preoperative planning, virtual simulation, and intraoperative guiding in video-assisted thoracoscopic lung surgery. Innovations (Phila), 2019, 14(1): 17-26.
39.	Tokuno J, Chen-Yoshikawa TF, Nakao M, et al. Resection process map: A novel dynamic simulation system for pulmonary resection. J Thorac Cardiovasc Surg, 2020, 159(3): 1130-1138.
40.	蒋可为, 陈鹤峰, 范志强, 等. 磁导航支气管镜与肺穿刺在肺外周病变中的诊断率和安全性研究. 中国呼吸与危重监护杂志, 2023, 22(9): 640-645.

1. 原静民, 孙妍, 任徽, 等. 低剂量螺旋CT肺癌筛查的卫生经济效益及其结合戒烟干预的研究进展. 中华肿瘤防治杂志, 2024, 31(18): 1146-1153.
2. Zhou T, Sun C. Effect of physical manipulation pulmonary rehabilitation on lung cancer patients after thoracoscopic lobectomy. Thorac Cancer, 2022, 13(3): 308-315.
3. 王欢, 刘建, 陈剑, 等. 胸腔镜复杂肺段切除术治疗Ⅰ期非小细胞肺癌的围手术期效果. 中国胸心血管外科临床杂志, 2021, 28(5): 555-559.
4. Suzuki K, Saji H, Aokage K, et al. Comparison of pulmonary segmentectomy and lobectomy: Safety results of a randomized trial. J Thorac Cardiovasc Surg, 2019, 158(3): 895-907.
5. Chen L, Gu Z, Lin B, et al. Pulmonary function changes after thoracoscopic lobectomy versus intentional thoracoscopic segmentectomy for early-stage non-small cell lung cancer. Transl Lung Cancer Res, 2021, 10(11): 4141-4151.
6. Natale G, Leonardi B, Messina G, et al. Three-dimensional lung reconstructions for the localization of lung nodules to be resected during surgery. Thorac Cancer, 2023, 14(34): 3389-3396.
7. Ma X, Lu T, Qin D, et al. Analysis of pulmonary artery variation based on 3D reconstruction of CT angiography. Front Physiol, 2023, 14: 1156513.
8. Ikeda N, Yoshimura A, Hagiwara M, et al. Three dimensional computed tomography lung modeling is useful in simulation and navigation of lung cancer surgery. Ann Thorac Cardiovasc Surg, 2013, 19(1): 1-5.
9. Mimae T, Miyata Y, Kumada T, et al. The intersegmental pulmonary vein is not always located on the intersegmental plane of the lung: Evaluation with 3-dimensional volume-rendering image reconstruction. JTCVS Tech, 2022 Sep 13: 16: 132-138.16-132.
10. 张望, 陈亮, 王俊, 等. DeepInsight软件术前肺部支气管血管成像的真实性研究. 中国肺癌杂志, 2021, 24(2): 88-93.
11. 龙涛, 任正兵, 邵爱中, 等. 三维重建技术在早期肺癌外科治疗中的研究进展. 中国胸心血管外科临床杂志, 2025, 32(1).
12. 孙岩峰, 张欢, 王少康, 等. 图像处理方法及装置、调整检测框的方法及装置: CN202111471005. 9. 2022-03-15.
13. 简伟健, 张欢, 王瑜, 等. 肺部支气管的分类方法、装置、电子设备和存储介质: CN202110321500. 5. 2022-02-22.
14. 黄文豪, 简伟健, 张欢, 等. 一种亚段支气管分级的方法、装置、设备及存储介质: CN202110501077. 7. 2021-10-08.
15. 郝智, 黄文豪, 简伟健, 等. 一种肺血管分段定位的方法、装置、设备及存储介质: CN202110501096. X. 2022-02-22.
16. 孙岩峰, 韦人, 邹彤, 等. 肺动脉图像分割方法及装置: CN202110706289. 9. 2022-04-19.
17. 刘恩佑, 张欢, 王少康, 等. 一种结构化分级管状结构血管的方法、装置及电子设备: CN202110397001. 4. 2022-02-22.
18. Zheng RS, Chen R, Han BF, et al. Cancer incidence and mortality in China, 2022. Chin J Oncol, 2024, 46(3): 221-231.
19. 刘彦国, 王俊. Graham与第一例全肺切除术. 中华医史杂志, 2003, 3: 53.
20. Christodoulides N, Fitzmaurice GJ, Bukowska I, et al. Post-pneumonectomy syndrome: A systematic review of the current evidence and treatment options. J Cardiothorac Surg, 2023, 18(1): 119.
21. 张艳娇, 高禹舜. 肺癌胸腔镜肺叶切除、胸腔镜肺段切除与开胸肺叶切除术后对肺功能影响的研究. 中国肺癌杂志, 2016, 19(10): 700-704.
22. Saji H, Okada M, Tsuboi M, et al. Segmentectomy versus lobectomy in small-sized peripheral non-small-cell lung cancer (JCOG0802/WJOG4607L): A multicentre, open-label, phase 3, randomised, controlled, non-inferiority trial. Lancet, 2022, 399(10335): 1607-1617.
23. Wu WB, Xia Y, Pan XL, et al. Three-dimensional navigation-guided thoracoscopic combined subsegmentectomy for intersegmental pulmonary nodules. Thorac Cancer, 2019, 10(1): 41-46.
24. Ichinose J, Mun M, Matsuura Y, et al. Efficiency of thoracoscopic palpation in localizing small pulmonary nodules. Surg Today, 2019, 49(11): 921-926.
25. Sato M. Precise sublobar lung resection for small pulmonary nodules: Localization and beyond. Gen Thorac Cardiovasc Surg, 2020, 68(7): 684-691.
26. Zhang X, Yang D, Li L, et al. Application of three-dimensional technology in video-assisted thoracoscopic surgery sublobectomy. Front Oncol, 2024, 14: 1280075.
27. Martin CM, Roach VA, Nguyen N, et al. Comparison of 3D reconstructive technologies used for morphometric research and the translation of knowledge using a decision matrix. Anat Sci Educ, 2013, 6(6): 393-403.
28. Nam JG, Park S, Hwang EJ, et al. Development and validation of deep learning-based automatic detection algorithm for malignant pulmonary nodules on chest radiographs. Radiology, 2019, 290(1): 218-228.
29. Ardila D, Kiraly AP, Bharadwaj S, et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med, 2019, 25(6): 954-961.
30. Santos-García G, Varela G, Novoa N, et al. Prediction of postoperative morbidity after lung resection using an artificial neural network ensemble. Artif Intell Med, 2004, 30(1): 61-69.
31. Chang YJ, Hung KC, Wang LK, et al. A real-time artificial intelligence-assisted system to predict weaning from ventilator immediately after lung resection surgery. Int J Environ Res Public Health, 2021, 18(5): 2713.
32. Salati M, Migliorelli L, Moccia S, et al. A machine learning approach for postoperative outcome prediction: Surgical data science application in a thoracic surgery setting. World J Surg, 2021, 45(5): 1585-1594.
33. Chen X, Wang Z, Qi Q, et al. A fully automated noncontrast CT 3-D reconstruction algorithm enabled accurate anatomical demonstration for lung segmentectomy. Thorac Cancer, 2022, 13(6): 795-803.
34. Li X, Zhang S, Luo X, et al. Accuracy and efficiency of an artificial intelligence-based pulmonary broncho-vascular three-dimensional reconstruction system supporting thoracic surgery: Retrospective and prospective validation study. EBioMedicine, 2023 Jan: 87: 104422.
35. Birkhoff DC, van Dalen ASHM, Schijven MP. A review on the current applications of artificial intelligence in the operating room. Surg Innov, 2021, 28(5): 611-619.
36. Andras I, Mazzone E, van Leeuwen FWB, et al. Artificial intelligence and robotics: A combination that is changing the operating room. World J Urol, 2020, 38(10): 2359-2366.
37. Ujiie H, Yamaguchi A, Gregor A, et al. Developing a virtual reality simulation system for preoperative planning of thoracoscopic thoracic surgery. J Thorac Dis, 2021, 13(2): 778-783.
38. Sardari Nia P, Olsthoorn JR, Heuts S, et al. Interactive 3D reconstruction of pulmonary anatomy for preoperative planning, virtual simulation, and intraoperative guiding in video-assisted thoracoscopic lung surgery. Innovations (Phila), 2019, 14(1): 17-26.
39. Tokuno J, Chen-Yoshikawa TF, Nakao M, et al. Resection process map: A novel dynamic simulation system for pulmonary resection. J Thorac Cardiovasc Surg, 2020, 159(3): 1130-1138.
40. 蒋可为, 陈鹤峰, 范志强, 等. 磁导航支气管镜与肺穿刺在肺外周病变中的诊断率和安全性研究. 中国呼吸与危重监护杂志, 2023, 22(9): 640-645.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Latest ArticlesApplication effects of AI software and Mimics software for three dimensional reconstruction in thoracoscopic anatomic segmentectomy: A retrospective cohort study

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