Interpretation of Chinese experts consensus on artificial intelligence assisted management for pulmonary nodule (2022 version)_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

LIN Yaobin ¹ , LIN Yongbin ¹ , ZHAO Zerui ¹ , LIN Zhichao ² , JIANG Long ³ , ZHENG Bin ⁴ , LIAO Hu ⁵ , YAN Wanpu ⁶ , LI Bin ⁷ , WANG Luming ⁸ ,  LONG Hao ¹

1. Department of Thoracic Surgery, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, P. R. China;
2. Department of Thoracic Surgery, Jiangmen Central Hospital, Jiangmen, 529030, Guangdong, P. R. China;
3. Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai, 200030, P. R. China;
4. Department of Thoracic Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, P. R. China;
5. Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China;
6. Department of Thoracic Surgery, Peking University Cancer Hospital and Institute, Beijing, 100143, P. R. China;
7. Department of Thoracic Surgery, The Second Affiliated Hospital of Lanzhou University, Lanzhou, 730030, P. R. China;
8. Department of Thoracic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, P. R. China;

Corresponding author：

LONG Hao, Email: longhao@sysucc.org.cn

Keywords：

Pulmonary nodule; artificial intelligence; expert consensus; interpretation

DOI：

10.7507/1007-4848.202302014

Video：

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The increasing number of pulmonary nodules being detected by computed tomography scans significantly increase the workload of the radiologists for scan interpretation. Limitations of traditional methods for differential diagnosis of pulmonary nodules have been increasingly prominent. Artificial intelligence (AI) has the potential to increase the efficiency of discrimination and invasiveness classification for pulmonary nodules and lead to effective nodule management. Chinese Experts Consensus on Artificial Intelligence Assisted Management for Pulmonary Nodule (2022 Version) has been officially released recently. This article closely follows the context, significance, core implications, and the impact of future AI-assisted management on the diagnosis and treatment of pulmonary nodules. It is hoped that through our joint efforts, we can promote the standardization of management for pulmonary nodules and strive to improve the long-term survival and postoperative life quality of patients with lung cancer.

Citation： LIN Yaobin, LIN Yongbin, ZHAO Zerui, LIN Zhichao, JIANG Long, ZHENG Bin, LIAO Hu, YAN Wanpu, LI Bin, WANG Luming, LONG Hao. Interpretation of Chinese experts consensus on artificial intelligence assisted management for pulmonary nodule (2022 version). Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2023, 30(5): 665-671. doi: 10.7507/1007-4848.202302014 Copy

1.	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.
2.	Pinsky PF, Gierada DS, Black W, et al. Performance of Lung-RADS in the National Lung Screening Trial: A retrospective assessment. Ann Intern Med, 2015, 162(7): 485-491.
3.	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.
4.	吴阶平医学基金会模拟医学部胸外科专委会. 人工智能在肺结节诊治中的应用专家共识(2022年版). 中国肺癌杂志, 2022, 25(4): 219-225.
5.	Siegel RL, Miller KD, Fuchs HE, et al. Cancer statistics, 2021. CA Cancer J Clin, 2021, 71(1): 7-33.
6.	Nomori H, Watanabe K, Ohtsuka T, et al. Evaluation of F-18 fluorodeoxyglucose (FDG) PET scanning for pulmonary nodules less than 3 cm in diameter, with special reference to the CT images. Lung Cancer, 2004, 45(1): 19-27.
7.	Schuhmann M, Eberhardt R, Herth FJ. Endobronchial ultrasound for peripheral lesions: A review. Endosc Ultrasound, 2013, 2(1): 3-6.
8.	Chockalingam A, Hong K. Transthoracic needle aspiration: The past, present and future. J Thorac Dis, 2015, 7(Suppl 4): S292-S299.
9.	Wang F, Casalino LP, Khullar D. Deep learning in medicine-promise, progress, and challenges. JAMA Intern Med, 2019, 179(3): 293-294.
10.	Lee JG, Jun S, Cho YW, et al. Deep learning in medical imaging: General overview. Korean J Radiol, 2017, 18(4): 570-584.
11.	Bi WL, Hosny A, Schabath MB, et al. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J Clin, 2019, 69(2): 127-157.
12.	Venkadesh KV, Setio AAA, Schreuder A, et al. Deep learning for malignancy risk estimation of pulmonary nodules detected at low-dose screening CT. Radiology, 2021, 300(2): 438-447.
13.	Zhao Y, de Bock GH, Vliegenthart R, et al. Performance of computer-aided detection of pulmonary nodules in low-dose CT: Comparison with double reading by nodule volume. Eur Radiol, 2012, 22(10): 2076-2084.
14.	Jha S, Topol EJ. Adapting to artificial intelligence: Radiologists and pathologists as information specialists. JAMA, 2016, 316(22): 2353-2354.
15.	Wood DE, Kazerooni EA, Baum SL, et al. Lung cancer screening, version 3. 2018, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw, 2018, 16(4): 412-441.
16.	Horeweg N, van Rosmalen J, Heuvelmans MA, et al. Lung cancer probability in patients with CT-detected pulmonary nodules: A prespecified analysis of data from the NELSON trial of low-dose CT screening. Lancet Oncol, 2014, 15(12): 1332-1341.
17.	Bach PB, Silvestri GA, Hanger M, et al. Screening for lung cancer: ACCP evidence-based clinical practice guidelines (2nd edition). Chest, 2007, 132(3 Suppl): 69S-77S.
18.	Zhao W, Yang J, Sun Y, et al. 3D deep learning from CT scans predicts tumor invasiveness of subcentimeter pulmonary adenocarcinomas. Cancer Res, 2018, 78(24): 6881-6889.
19.	MacMahon H, Naidich DP, Goo JM, et al. Guidelines for management of incidental pulmonary nodules detected on CT images: From the Fleischner Society 2017. Radiology, 2017, 284(1): 228-243.
20.	Benzakoun J, Bommart S, Coste J, et al. Computer-aided diagnosis (CAD) of subsolid nodules: Evaluation of a commercial CAD system. Eur J Radiol, 2016, 85(10): 1728-1734.
21.	Silva M, Schaefer-Prokop CM, Jacobs C, et al. Detection of subsolid nodules in lung cancer screening: Complementary sensitivity of visual reading and computer-aided diagnosis. Invest Radiol, 2018, 53(8): 441-449.
22.	Ather S, Kadir T, Gleeson F. Artificial intelligence and radiomics in pulmonary nodule management: Current status and future applications. Clin Radiol, 2020, 75(1): 13-19.
23.	Huang G, Wei X, Tang H, et al. A systematic review and meta-analysis of diagnostic performance and physicians' perceptions of artificial intelligence (AI)-assisted CT diagnostic technology for the classification of pulmonary nodules. J Thorac Dis, 2021, 13(8): 4797-4811.
24.	Revel MP, Lefort C, Bissery A, et al. Pulmonary nodules: Preliminary experience with three-dimensional evaluation. Radiology, 2004, 231(2): 459-466.
25.	Revel MP, Merlin A, Peyrard S, et al. Software volumetric evaluation of doubling times for differentiating benign versus malignant pulmonary nodules. AJR Am J Roentgenol, 2006, 187(1): 135-142.
26.	Armato SG, Drukker K, Li F, et al. LUNGx Challenge for computerized lung nodule classification. J Med Imaging (Bellingham), 2016, 3(4): 044506.
27.	Detterbeck FC, Boffa DJ, Kim AW, et al. The eighth edition lung cancer stage classification. Chest, 2017, 151(1): 193-203.
28.	Murakami S, Ito H, Tsubokawa N, et al. Prognostic value of the new IASLC/ATS/ERS classification of clinical stageⅠA lung adenocarcinoma. Lung Cancer, 2015, 90(2): 199-204.
29.	Kuhn E, Morbini P, Cancellieri A, et al. Adenocarcinoma classification: Patterns and prognosis. Pathologica, 2018, 110(1): 5-11.
30.	Cheng X, Zheng D, Li Y, et al. Tumor histology predicts mediastinal nodal status and may be used to guide limited lymphadenectomy in patients with clinical stage Ⅰ non-small cell lung cancer. J Thorac Cardiovasc Surg, 2018, 155(6): 2648-2656.
31.	Suzuki K, Watanabe SI, Wakabayashi M, et al. A single-arm study of sublobar resection for ground-glass opacity dominant peripheral lung cancer. J Thorac Cardiovasc Surg, 2022, 163(1): 289-301.
32.	Moreira AL, Ocampo PSS, Xia Y, et al. A grading system for invasive pulmonary adenocarcinoma: A proposal from the International Association for the Study of Lung Cancer Pathology Committee. J Thorac Oncol, 2020, 15(10): 1599-1610.
33.	Yeh YC, Nitadori J, Kadota K, et al. Using frozen section to identify histological patterns in stageⅠlung adenocarcinoma of ≤3 cm: Accuracy and interobserver agreement. Histopathology, 2015, 66(7): 922-938.
34.	Shi L, Zhao J, Peng X, et al. CT-based radiomics for differentiating invasive adenocarcinomas from indolent lung adenocarcinomas appearing as ground-glass nodules: Asystematic review. Eur J Radiol, 2021, 144: 109956.
35.	Wang Y, Zhou L, Wang M, et al. Combination of generative adversarial network and convolutional neural network for automatic subcentimeter pulmonary adenocarcinoma classifi-cation. Quant Imaging Med Surg, 2020, 10(6): 1249-1264.
36.	Gong J, Liu J, Hao W, et al. A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images. Eur Radiol, 2020, 30(4): 1847-1855.
37.	Wang J, Chen X, Lu H, et al. Feature-shared adaptive-boost deep learning for invasiveness classification of pulmonary subsolid nodules in CT images. Med Phys, 2020, 47(4): 1738-1749.
38.	Ni Y, Yang Y, Zheng D, et al. The invasiveness classification of ground-glass nodules using 3D attention network and HRCT. J Digit Imaging, 2020, 33(5): 1144-1154.
39.	Yu Y, Wang N, Huang N, et al. Determining the invasiveness of ground-glass nodules using a 3D multi-task network. Eur Radiol, 2021, 31(9): 7162-7171.
40.	Tsushima K, Sone S, Hanaoka T, et al. Radiological diagnosis of small pulmonary nodules detected on low-dose screening computed tomography. Respirology, 2008, 13(6): 817-824.
41.	Wood DE. National Comprehensive Cancer Network (NCCN) clinical practice guidelines for lung cancer screening. Thorac Surg Clin, 2015, 25(2): 185-197.
42.	Joy Mathew C, David AM, Joy Mathew CM. Artificial intelligence and its future potential in lung cancer screening. EXCLI J, 2020, 19: 1552-1562.
43.	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.
44.	Huang P, Lin CT, Li Y, et al. Prediction of lung cancer risk at follow-up screening with low-dose CT: A training and validation study of a deep learning method. Lancet Digit Health, 2019, 1(7): e353-e362.
45.	Adams SJ, Mondal P, Penz E, et al. Development and cost analysis of a lung nodule management strategy combining artificial intelligence and Lung-RADS for baseline lung cancer screening. J Am Coll Radiol, 2021, 18(5): 741-751.
46.	中国医药教育协会胸外科专业委员会, 中国胸外科肺癌联盟, 浙江省医学会胸外科学分会, 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.
47.	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.
48.	Iwano S, Yokoi K, Taniguchi T, et al. Planning of segmentectomy using three-dimensional computed tomography angiography with a virtual safety margin: Technique and initial experience. Lung Cancer, 2013, 81(3): 410-415.
49.	Liu X, Zhao Y, Xuan Y, et al. Three-dimensional printing in the preoperative planning of thoracoscopic pulmonary segmentectomy. Transl Lung Cancer Res, 2019, 8(6): 929-937.

1. 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.
2. Pinsky PF, Gierada DS, Black W, et al. Performance of Lung-RADS in the National Lung Screening Trial: A retrospective assessment. Ann Intern Med, 2015, 162(7): 485-491.
3. 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.
4. 吴阶平医学基金会模拟医学部胸外科专委会. 人工智能在肺结节诊治中的应用专家共识(2022年版). 中国肺癌杂志, 2022, 25(4): 219-225.
5. Siegel RL, Miller KD, Fuchs HE, et al. Cancer statistics, 2021. CA Cancer J Clin, 2021, 71(1): 7-33.
6. Nomori H, Watanabe K, Ohtsuka T, et al. Evaluation of F-18 fluorodeoxyglucose (FDG) PET scanning for pulmonary nodules less than 3 cm in diameter, with special reference to the CT images. Lung Cancer, 2004, 45(1): 19-27.
7. Schuhmann M, Eberhardt R, Herth FJ. Endobronchial ultrasound for peripheral lesions: A review. Endosc Ultrasound, 2013, 2(1): 3-6.
8. Chockalingam A, Hong K. Transthoracic needle aspiration: The past, present and future. J Thorac Dis, 2015, 7(Suppl 4): S292-S299.
9. Wang F, Casalino LP, Khullar D. Deep learning in medicine-promise, progress, and challenges. JAMA Intern Med, 2019, 179(3): 293-294.
10. Lee JG, Jun S, Cho YW, et al. Deep learning in medical imaging: General overview. Korean J Radiol, 2017, 18(4): 570-584.
11. Bi WL, Hosny A, Schabath MB, et al. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J Clin, 2019, 69(2): 127-157.
12. Venkadesh KV, Setio AAA, Schreuder A, et al. Deep learning for malignancy risk estimation of pulmonary nodules detected at low-dose screening CT. Radiology, 2021, 300(2): 438-447.
13. Zhao Y, de Bock GH, Vliegenthart R, et al. Performance of computer-aided detection of pulmonary nodules in low-dose CT: Comparison with double reading by nodule volume. Eur Radiol, 2012, 22(10): 2076-2084.
14. Jha S, Topol EJ. Adapting to artificial intelligence: Radiologists and pathologists as information specialists. JAMA, 2016, 316(22): 2353-2354.
15. Wood DE, Kazerooni EA, Baum SL, et al. Lung cancer screening, version 3. 2018, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw, 2018, 16(4): 412-441.
16. Horeweg N, van Rosmalen J, Heuvelmans MA, et al. Lung cancer probability in patients with CT-detected pulmonary nodules: A prespecified analysis of data from the NELSON trial of low-dose CT screening. Lancet Oncol, 2014, 15(12): 1332-1341.
17. Bach PB, Silvestri GA, Hanger M, et al. Screening for lung cancer: ACCP evidence-based clinical practice guidelines (2nd edition). Chest, 2007, 132(3 Suppl): 69S-77S.
18. Zhao W, Yang J, Sun Y, et al. 3D deep learning from CT scans predicts tumor invasiveness of subcentimeter pulmonary adenocarcinomas. Cancer Res, 2018, 78(24): 6881-6889.
19. MacMahon H, Naidich DP, Goo JM, et al. Guidelines for management of incidental pulmonary nodules detected on CT images: From the Fleischner Society 2017. Radiology, 2017, 284(1): 228-243.
20. Benzakoun J, Bommart S, Coste J, et al. Computer-aided diagnosis (CAD) of subsolid nodules: Evaluation of a commercial CAD system. Eur J Radiol, 2016, 85(10): 1728-1734.
21. Silva M, Schaefer-Prokop CM, Jacobs C, et al. Detection of subsolid nodules in lung cancer screening: Complementary sensitivity of visual reading and computer-aided diagnosis. Invest Radiol, 2018, 53(8): 441-449.
22. Ather S, Kadir T, Gleeson F. Artificial intelligence and radiomics in pulmonary nodule management: Current status and future applications. Clin Radiol, 2020, 75(1): 13-19.
23. Huang G, Wei X, Tang H, et al. A systematic review and meta-analysis of diagnostic performance and physicians' perceptions of artificial intelligence (AI)-assisted CT diagnostic technology for the classification of pulmonary nodules. J Thorac Dis, 2021, 13(8): 4797-4811.
24. Revel MP, Lefort C, Bissery A, et al. Pulmonary nodules: Preliminary experience with three-dimensional evaluation. Radiology, 2004, 231(2): 459-466.
25. Revel MP, Merlin A, Peyrard S, et al. Software volumetric evaluation of doubling times for differentiating benign versus malignant pulmonary nodules. AJR Am J Roentgenol, 2006, 187(1): 135-142.
26. Armato SG, Drukker K, Li F, et al. LUNGx Challenge for computerized lung nodule classification. J Med Imaging (Bellingham), 2016, 3(4): 044506.
27. Detterbeck FC, Boffa DJ, Kim AW, et al. The eighth edition lung cancer stage classification. Chest, 2017, 151(1): 193-203.
28. Murakami S, Ito H, Tsubokawa N, et al. Prognostic value of the new IASLC/ATS/ERS classification of clinical stageⅠA lung adenocarcinoma. Lung Cancer, 2015, 90(2): 199-204.
29. Kuhn E, Morbini P, Cancellieri A, et al. Adenocarcinoma classification: Patterns and prognosis. Pathologica, 2018, 110(1): 5-11.
30. Cheng X, Zheng D, Li Y, et al. Tumor histology predicts mediastinal nodal status and may be used to guide limited lymphadenectomy in patients with clinical stage Ⅰ non-small cell lung cancer. J Thorac Cardiovasc Surg, 2018, 155(6): 2648-2656.
31. Suzuki K, Watanabe SI, Wakabayashi M, et al. A single-arm study of sublobar resection for ground-glass opacity dominant peripheral lung cancer. J Thorac Cardiovasc Surg, 2022, 163(1): 289-301.
32. Moreira AL, Ocampo PSS, Xia Y, et al. A grading system for invasive pulmonary adenocarcinoma: A proposal from the International Association for the Study of Lung Cancer Pathology Committee. J Thorac Oncol, 2020, 15(10): 1599-1610.
33. Yeh YC, Nitadori J, Kadota K, et al. Using frozen section to identify histological patterns in stageⅠlung adenocarcinoma of ≤3 cm: Accuracy and interobserver agreement. Histopathology, 2015, 66(7): 922-938.
34. Shi L, Zhao J, Peng X, et al. CT-based radiomics for differentiating invasive adenocarcinomas from indolent lung adenocarcinomas appearing as ground-glass nodules: Asystematic review. Eur J Radiol, 2021, 144: 109956.
35. Wang Y, Zhou L, Wang M, et al. Combination of generative adversarial network and convolutional neural network for automatic subcentimeter pulmonary adenocarcinoma classifi-cation. Quant Imaging Med Surg, 2020, 10(6): 1249-1264.
36. Gong J, Liu J, Hao W, et al. A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images. Eur Radiol, 2020, 30(4): 1847-1855.
37. Wang J, Chen X, Lu H, et al. Feature-shared adaptive-boost deep learning for invasiveness classification of pulmonary subsolid nodules in CT images. Med Phys, 2020, 47(4): 1738-1749.
38. Ni Y, Yang Y, Zheng D, et al. The invasiveness classification of ground-glass nodules using 3D attention network and HRCT. J Digit Imaging, 2020, 33(5): 1144-1154.
39. Yu Y, Wang N, Huang N, et al. Determining the invasiveness of ground-glass nodules using a 3D multi-task network. Eur Radiol, 2021, 31(9): 7162-7171.
40. Tsushima K, Sone S, Hanaoka T, et al. Radiological diagnosis of small pulmonary nodules detected on low-dose screening computed tomography. Respirology, 2008, 13(6): 817-824.
41. Wood DE. National Comprehensive Cancer Network (NCCN) clinical practice guidelines for lung cancer screening. Thorac Surg Clin, 2015, 25(2): 185-197.
42. Joy Mathew C, David AM, Joy Mathew CM. Artificial intelligence and its future potential in lung cancer screening. EXCLI J, 2020, 19: 1552-1562.
43. 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.
44. Huang P, Lin CT, Li Y, et al. Prediction of lung cancer risk at follow-up screening with low-dose CT: A training and validation study of a deep learning method. Lancet Digit Health, 2019, 1(7): e353-e362.
45. Adams SJ, Mondal P, Penz E, et al. Development and cost analysis of a lung nodule management strategy combining artificial intelligence and Lung-RADS for baseline lung cancer screening. J Am Coll Radiol, 2021, 18(5): 741-751.
46. 中国医药教育协会胸外科专业委员会, 中国胸外科肺癌联盟, 浙江省医学会胸外科学分会, 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.
47. 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.
48. Iwano S, Yokoi K, Taniguchi T, et al. Planning of segmentectomy using three-dimensional computed tomography angiography with a virtual safety margin: Technique and initial experience. Lung Cancer, 2013, 81(3): 410-415.
49. Liu X, Zhao Y, Xuan Y, et al. Three-dimensional printing in the preoperative planning of thoracoscopic pulmonary segmentectomy. Transl Lung Cancer Res, 2019, 8(6): 929-937.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Interpretation of Chinese experts consensus on artificial intelligence assisted management for pulmonary nodule (2022 version)

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