Chinese expert consensus on the application of integrated 3D reconstruction with artificial intelligence in thoracic surgery_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

Thoracic Surgery Branch of China International Exchange and Promotive Association for Medical and Health Care , Lung Cancer Prevention Branch of China International Exchange and Promotive Association for Medical and Health Care , Thoracic Surgery Specialty Committee of Health Exchanges and Cooperation Association Across the Taiwan Straits , Thoracic Surgery Branch of Zhejiang Medical Association , Thoracic Surgeon Branch of Zhejiang Medical Doctors Association , Zhejiang Province Preventive Medicine Association Lung Cancer Prevention and Control Specialty Committee ,  HUJian

1. Department of Thoracic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, P. R. China;
2. Key Laboratory of Clinical Evaluation Technology for Medical Device of Zhejiang Province, Hangzhou, 310003, P. R. China;

Corresponding author：

HUJian, Email: dr_hujian@zju.edu.cn

Keywords：

3D reconstruction; artificial intelligence; thoracic surgery; expert consensus

DOI：

10.7507/1007-4848.202302054

Video：

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In order to further regulate the application of 3D reconstruction in thoracic surgery, the Chinese Expert Consensus Group on the Application of Integrated 3D Reconstruction with Artificial Intelligence in Thoracic Surgery conducted discussions and developed this consensus. This consensus is based on the clinical experience and existing prospective or retrospective studies of 3D reconstruction technology in various scenarios of thoracic surgery and summarizes recommendations, and also appends a list of 3D reconstruction technology application scenarios that are currently controversial, not fully studied, or still in the exploratory stage, to provide direction and evidence for future clinical research and disease diagnosis and treatment, and to reach a consensus.

Citation： Thoracic Surgery Branch of China International Exchange and Promotive Association for Medical and Health Care, Lung Cancer Prevention Branch of China International Exchange and Promotive Association for Medical and Health Care, Thoracic Surgery Specialty Committee of Health Exchanges and Cooperation Association Across the Taiwan Straits, Thoracic Surgery Branch of Zhejiang Medical Association, Thoracic Surgeon Branch of Zhejiang Medical Doctors Association, Zhejiang Province Preventive Medicine Association Lung Cancer Prevention and Control Specialty Committee. Chinese expert consensus on the application of integrated 3D reconstruction with artificial intelligence in thoracic surgery. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2023, 30(5): 641-646. doi: 10.7507/1007-4848.202302054 Copy

1.	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.
2.	El-Sherif A, Gooding WE, Santos R, et al. Outcomes of sublobar resection versus lobectomy for stageⅠ non-small cell lung cancer: A 13-year analysis. Ann Thorac Surg, 2006, 82(2): 408-415.
3.	彭明政. 基于早期肺腺癌性磨玻璃结节CT三维重建下相关影像学参数对癌细胞ki-67表达指数的术前预测模型研究. 上海交通大学, 2016.
4.	冀瑛. 同时性多原发肺癌外科治疗的预后研究及基因组学分析. 北京协和医学院, 2021.
5.	Greenspan RJ. E pluribus unum, ex uno plura: Quantitative and single-gene perspectives on the study of behavior. Annu Rev Neurosci, 2004, 27: 79-105.
6.	Zhou SK, Greenspan H, Shen D. Deep learning for medical image analysis. Academic Press, 2017.
7.	Wang S, Zhou M, Liu Z, et al. Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation. Med Image Anal, 2017, 40: 172-183.
8.	Ciompi F, Chung K, van Riel SJ, et al. Corrigendum: Towards automatic pulmonary nodule management in lung cancer screening with deep learning. Sci Rep, 2017, 7: 46878.
9.	Song Q, Zhao L, Luo X, et al. Using deep learning for classification of lung nodules on computed tomography images. J Healthc Eng, 2017, 2017: 8314740.
10.	Li D, Mikela Vilmun B, Frederik Carlsen J, et al. The performance of deep learning algorithms on automatic pulmonary nodule detection and classification tested on different datasets that are not derived from LIDC-IDRI: A systematic review. Diagnostics (Basel), 2019, 9(4): 207.
11.	Armato SG, Drukker K, Li F, et al. LUNGx Challenge for computerized lung nodule classification. J Med Imaging (Bellingham), 2016, 3(4): 044506.
12.	Qiu B, Ji Y, He H, et al. Three-dimensional reconstruction/personalized three-dimensional printed model for thoracoscopic anatomical partial-lobectomy in stageⅠlung cancer: A retrospective study. Transl Lung Cancer Res, 2020, 9(4): 1235-1246.
13.	马晓超. CT血管造影三维重建肺动脉的变异分析. 吉林大学, 2019.
14.	张宇辰, 张文强, 郝蒙福, 等. 三维重建虚拟现实导航技术在胸腔镜肺段切除术中的应用价值. 中国医药导报, 2021, 18(32): 34-37, 198.
15.	刘艳虎, 薛栋, 李亚东. 三维重建技术在肺裂发育不全肺癌患者中的应用. 南通大学学报(医学版), 2021, 41(6): 563-566.
16.	陈星, 林铿强, 马晨晖, 等. 3D-CTBA联合3D打印技术在早期非小细胞肺癌胸腔镜解剖性肺段切除术中的应用研究. 微创医学, 2022, 17(1): 16-22.
17.	殷志敏, 陈良亮, 王霄霖, 等. 三维CT支气管血管成像在解剖性肺段切除术中的应用研究进展. 实用临床医药杂志, 2020, 24(3): 5-9.
18.	吴文博, 张霄鹏, 薛文飞, 等. 胸部CT平扫三维重建与增强扫描三维重建技术在解剖性肺段切除中的应用分析. 临床外科杂志, 2021, 29(8): 730-734.
19.	Xu G, Chen C, Zheng W, et al. Application of the IQQA-3D imaging interpretation and analysis system in uniportal video-assisted thoracoscopic anatomical segmentectomy: A series study. J Thorac Dis, 2019, 11(5): 2058-2066.
20.	冯振. 胸腔巨大肿瘤手术预后分析及三维重建影像技术临床应用研究. 山东大学, 2017.
21.	魏鑫. 三维重建模型在微创食管癌根治术中的应用研究. 河南大学, 2020.
22.	杨晓东. 下段食管癌的三维重建在疾病诊治过程中的应用. 吉林大学, 2016.
23.	Majercik S, Pieracci FM. Chest wall trauma. Thorac Surg Clin, 2017, 27(2): 113-121.
24.	赵嘉华. 三维重建技术在胸外科临床中的应用. 中国人民解放军军医进修学院, 2011.
25.	Senekjian L, Nirula R. Rib fracture fixation: Indications and outcomes. Crit Care Clin, 2017, 33(1): 153-165.
26.	胡坚, 陈秋强, 李晨蔚, 等. 肋骨胸骨创伤诊治: 浙江省胸外科专家共识(2021版). 中华危重症医学杂志(电子版), 2021, 14(2): 89-99.
27.	李满元. 胸腔镜辅助手术结合三维重建技术治疗多发性肋骨骨折的临床研究. 遵义医科大学, 2019.
28.	Bellia-Munzon G, Martinez J, Toselli L, et al. From bench to bedside: 3D reconstruction and printing as a valuable tool for the chest wall surgeon. J Pediatr Surg, 2020, 55(12): 2703-2709.
29.	Pretorius ES, Haller JA, Fishman EK. Spiral CT with 3D reconstruction in children requiring reoperation for failure of chest wall growth after pectus excavatum surgery. Preliminary observations. Clin Imaging, 1998, 22(2): 108-116.
30.	Chen YY, Lin KH, Huang HK, et al. The beneficial application of preoperative 3D printing for surgical stabilization of rib fractures. PLoS One, 2018, 13(10): e0204652.
31.	宋磊, 张强, 宁少南, 等. 3D打印在肋骨骨折内固定中的应用. 中华胸心血管外科杂志, 2018, 34(5): 288-291.

1. 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.
2. El-Sherif A, Gooding WE, Santos R, et al. Outcomes of sublobar resection versus lobectomy for stageⅠ non-small cell lung cancer: A 13-year analysis. Ann Thorac Surg, 2006, 82(2): 408-415.
3. 彭明政. 基于早期肺腺癌性磨玻璃结节CT三维重建下相关影像学参数对癌细胞ki-67表达指数的术前预测模型研究. 上海交通大学, 2016.
4. 冀瑛. 同时性多原发肺癌外科治疗的预后研究及基因组学分析. 北京协和医学院, 2021.
5. Greenspan RJ. E pluribus unum, ex uno plura: Quantitative and single-gene perspectives on the study of behavior. Annu Rev Neurosci, 2004, 27: 79-105.
6. Zhou SK, Greenspan H, Shen D. Deep learning for medical image analysis. Academic Press, 2017.
7. Wang S, Zhou M, Liu Z, et al. Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation. Med Image Anal, 2017, 40: 172-183.
8. Ciompi F, Chung K, van Riel SJ, et al. Corrigendum: Towards automatic pulmonary nodule management in lung cancer screening with deep learning. Sci Rep, 2017, 7: 46878.
9. Song Q, Zhao L, Luo X, et al. Using deep learning for classification of lung nodules on computed tomography images. J Healthc Eng, 2017, 2017: 8314740.
10. Li D, Mikela Vilmun B, Frederik Carlsen J, et al. The performance of deep learning algorithms on automatic pulmonary nodule detection and classification tested on different datasets that are not derived from LIDC-IDRI: A systematic review. Diagnostics (Basel), 2019, 9(4): 207.
11. Armato SG, Drukker K, Li F, et al. LUNGx Challenge for computerized lung nodule classification. J Med Imaging (Bellingham), 2016, 3(4): 044506.
12. Qiu B, Ji Y, He H, et al. Three-dimensional reconstruction/personalized three-dimensional printed model for thoracoscopic anatomical partial-lobectomy in stageⅠlung cancer: A retrospective study. Transl Lung Cancer Res, 2020, 9(4): 1235-1246.
13. 马晓超. CT血管造影三维重建肺动脉的变异分析. 吉林大学, 2019.
14. 张宇辰, 张文强, 郝蒙福, 等. 三维重建虚拟现实导航技术在胸腔镜肺段切除术中的应用价值. 中国医药导报, 2021, 18(32): 34-37, 198.
15. 刘艳虎, 薛栋, 李亚东. 三维重建技术在肺裂发育不全肺癌患者中的应用. 南通大学学报(医学版), 2021, 41(6): 563-566.
16. 陈星, 林铿强, 马晨晖, 等. 3D-CTBA联合3D打印技术在早期非小细胞肺癌胸腔镜解剖性肺段切除术中的应用研究. 微创医学, 2022, 17(1): 16-22.
17. 殷志敏, 陈良亮, 王霄霖, 等. 三维CT支气管血管成像在解剖性肺段切除术中的应用研究进展. 实用临床医药杂志, 2020, 24(3): 5-9.
18. 吴文博, 张霄鹏, 薛文飞, 等. 胸部CT平扫三维重建与增强扫描三维重建技术在解剖性肺段切除中的应用分析. 临床外科杂志, 2021, 29(8): 730-734.
19. Xu G, Chen C, Zheng W, et al. Application of the IQQA-3D imaging interpretation and analysis system in uniportal video-assisted thoracoscopic anatomical segmentectomy: A series study. J Thorac Dis, 2019, 11(5): 2058-2066.
20. 冯振. 胸腔巨大肿瘤手术预后分析及三维重建影像技术临床应用研究. 山东大学, 2017.
21. 魏鑫. 三维重建模型在微创食管癌根治术中的应用研究. 河南大学, 2020.
22. 杨晓东. 下段食管癌的三维重建在疾病诊治过程中的应用. 吉林大学, 2016.
23. Majercik S, Pieracci FM. Chest wall trauma. Thorac Surg Clin, 2017, 27(2): 113-121.
24. 赵嘉华. 三维重建技术在胸外科临床中的应用. 中国人民解放军军医进修学院, 2011.
25. Senekjian L, Nirula R. Rib fracture fixation: Indications and outcomes. Crit Care Clin, 2017, 33(1): 153-165.
26. 胡坚, 陈秋强, 李晨蔚, 等. 肋骨胸骨创伤诊治: 浙江省胸外科专家共识(2021版). 中华危重症医学杂志(电子版), 2021, 14(2): 89-99.
27. 李满元. 胸腔镜辅助手术结合三维重建技术治疗多发性肋骨骨折的临床研究. 遵义医科大学, 2019.
28. Bellia-Munzon G, Martinez J, Toselli L, et al. From bench to bedside: 3D reconstruction and printing as a valuable tool for the chest wall surgeon. J Pediatr Surg, 2020, 55(12): 2703-2709.
29. Pretorius ES, Haller JA, Fishman EK. Spiral CT with 3D reconstruction in children requiring reoperation for failure of chest wall growth after pectus excavatum surgery. Preliminary observations. Clin Imaging, 1998, 22(2): 108-116.
30. Chen YY, Lin KH, Huang HK, et al. The beneficial application of preoperative 3D printing for surgical stabilization of rib fractures. PLoS One, 2018, 13(10): e0204652.
31. 宋磊, 张强, 宁少南, 等. 3D打印在肋骨骨折内固定中的应用. 中华胸心血管外科杂志, 2018, 34(5): 288-291.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Chinese expert consensus on the application of integrated 3D reconstruction with artificial intelligence in thoracic surgery

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