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結節CT三維重建下相關影像學參數對癌細胞ki-67表達指數的術前預測模型研究. 上海交通大學, 2016.
4.	冀瑛. 同時性多原發肺癌外科治療的預后研究及基因組學分析. 北京協和醫學院, 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.	馬曉超. CT血管造影三維重建肺動脈的變異分析. 吉林大學, 2019.
14.	張宇辰, 張文強, 郝蒙福, 等. 三維重建虛擬現實導航技術在胸腔鏡肺段切除術中的應用價值. 中國醫藥導報, 2021, 18(32): 34-37, 198.
15.	劉艷虎, 薛棟, 李亞東. 三維重建技術在肺裂發育不全肺癌患者中的應用. 南通大學學報(醫學版), 2021, 41(6): 563-566.
16.	陳星, 林鏗強, 馬晨暉, 等. 3D-CTBA聯合3D打印技術在早期非小細胞肺癌胸腔鏡解剖性肺段切除術中的應用研究. 微創醫學, 2022, 17(1): 16-22.
17.	殷志敏, 陳良亮, 王霄霖, 等. 三維CT支氣管血管成像在解剖性肺段切除術中的應用研究進展. 實用臨床醫藥雜志, 2020, 24(3): 5-9.
18.	吳文博, 張霄鵬, 薛文飛, 等. 胸部CT平掃三維重建與增強掃描三維重建技術在解剖性肺段切除中的應用分析. 臨床外科雜志, 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.	馮振. 胸腔巨大腫瘤手術預后分析及三維重建影像技術臨床應用研究. 山東大學, 2017.
21.	魏鑫. 三維重建模型在微創食管癌根治術中的應用研究. 河南大學, 2020.
22.	楊曉東. 下段食管癌的三維重建在疾病診治過程中的應用. 吉林大學, 2016.
23.	Majercik S, Pieracci FM. Chest wall trauma. Thorac Surg Clin, 2017, 27(2): 113-121.
24.	趙嘉華. 三維重建技術在胸外科臨床中的應用. 中國人民解放軍軍醫進修學院, 2011.
25.	Senekjian L, Nirula R. Rib fracture fixation: Indications and outcomes. Crit Care Clin, 2017, 33(1): 153-165.
26.	胡堅, 陳秋強, 李晨蔚, 等. 肋骨胸骨創傷診治: 浙江省胸外科專家共識(2021版). 中華危重癥醫學雜志(電子版), 2021, 14(2): 89-99.
27.	李滿元. 胸腔鏡輔助手術結合三維重建技術治療多發性肋骨骨折的臨床研究. 遵義醫科大學, 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, 張強, 寧少南, 等. 3D打印在肋骨骨折內固定中的應用. 中華胸心血管外科雜志, 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結節CT三維重建下相關影像學參數對癌細胞ki-67表達指數的術前預測模型研究. 上海交通大學, 2016.
4. 冀瑛. 同時性多原發肺癌外科治療的預后研究及基因組學分析. 北京協和醫學院, 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. 馬曉超. CT血管造影三維重建肺動脈的變異分析. 吉林大學, 2019.
14. 張宇辰, 張文強, 郝蒙福, 等. 三維重建虛擬現實導航技術在胸腔鏡肺段切除術中的應用價值. 中國醫藥導報, 2021, 18(32): 34-37, 198.
15. 劉艷虎, 薛棟, 李亞東. 三維重建技術在肺裂發育不全肺癌患者中的應用. 南通大學學報(醫學版), 2021, 41(6): 563-566.
16. 陳星, 林鏗強, 馬晨暉, 等. 3D-CTBA聯合3D打印技術在早期非小細胞肺癌胸腔鏡解剖性肺段切除術中的應用研究. 微創醫學, 2022, 17(1): 16-22.
17. 殷志敏, 陳良亮, 王霄霖, 等. 三維CT支氣管血管成像在解剖性肺段切除術中的應用研究進展. 實用臨床醫藥雜志, 2020, 24(3): 5-9.
18. 吳文博, 張霄鵬, 薛文飛, 等. 胸部CT平掃三維重建與增強掃描三維重建技術在解剖性肺段切除中的應用分析. 臨床外科雜志, 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. 馮振. 胸腔巨大腫瘤手術預后分析及三維重建影像技術臨床應用研究. 山東大學, 2017.
21. 魏鑫. 三維重建模型在微創食管癌根治術中的應用研究. 河南大學, 2020.
22. 楊曉東. 下段食管癌的三維重建在疾病診治過程中的應用. 吉林大學, 2016.
23. Majercik S, Pieracci FM. Chest wall trauma. Thorac Surg Clin, 2017, 27(2): 113-121.
24. 趙嘉華. 三維重建技術在胸外科臨床中的應用. 中國人民解放軍軍醫進修學院, 2011.
25. Senekjian L, Nirula R. Rib fracture fixation: Indications and outcomes. Crit Care Clin, 2017, 33(1): 153-165.
26. 胡堅, 陳秋強, 李晨蔚, 等. 肋骨胸骨創傷診治: 浙江省胸外科專家共識(2021版). 中華危重癥醫學雜志(電子版), 2021, 14(2): 89-99.
27. 李滿元. 胸腔鏡輔助手術結合三維重建技術治療多發性肋骨骨折的臨床研究. 遵義醫科大學, 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, 張強, 寧少南, 等. 3D打印在肋骨骨折內固定中的應用. 中華胸心血管外科雜志, 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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