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中华肺部疾病杂志(电子版) ›› 2023, Vol. 16 ›› Issue (03) : 301 -305. doi: 10.3877/cma.j.issn.1674-6902.2023.03.001

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基于人工智能探讨多发性肺结节的诊治策略
王洪武(), 方碧霞   
  1. 100000 北京,北京中医药大学东直门医院
  • 收稿日期:2023-01-13 出版日期:2023-06-25
  • 通信作者: 王洪武

Exploring the diagnosis and treatment strategies of multiple pulmonary nodules based on artificial intelligence

Hongwu Wang(), Bixia Fang   

  • Received:2023-01-13 Published:2023-06-25
  • Corresponding author: Hongwu Wang
引用本文:

王洪武, 方碧霞. 基于人工智能探讨多发性肺结节的诊治策略[J]. 中华肺部疾病杂志(电子版), 2023, 16(03): 301-305.

Hongwu Wang, Bixia Fang. Exploring the diagnosis and treatment strategies of multiple pulmonary nodules based on artificial intelligence[J]. Chinese Journal of Lung Diseases(Electronic Edition), 2023, 16(03): 301-305.

将影像学上表现为直径≤3 cm的局灶性、类圆形、密度增高的阴影且数量≥2个的结节定义为多发性肺结节(multiple pulmonary nodules, MPN)。肺结节可分为实性(solid pulmonary nodules, SN)、混合性磨玻璃结节(mixed ground glass nodules, mGGO)和纯磨玻璃结节(pure ground glass nodules, pGGO)。随着低剂量螺旋CT(LDCT)在肺部疾病筛查中的广泛应用,越来越多的肺部多发性磨玻璃结节被发现[2]。目前,国内外已发表了多个指南或专家共识,针对单发肺结节的诊断和治疗,但对于MPN的诊治尚无统一的认识[3,4,5,6]

1
中华医学会呼吸病学分会肺癌学组,中国肺癌防治联盟专家组. 肺结节诊治中国专家共识(2018年版)[J]. 中华结核和呼吸杂志2018, 41(10): 763-771.
2
杨 丽,钱桂生. 肺结节临床精准诊断的新理念[J]. 中华肺部疾病杂志(电子版), 2022, 15(1): 1-5.
3
Bai C, Choi CM, Chu CM, et al. Evaluation of pulmonary nodules: clinical practice consensus guidelines for Asia[J]. Chest, 2016, 150(4): 877- 893.
4
Macmahon H, Naidich DP, Goo JM, et al. Guidelines for management of incidental pulmonary nodules detected on CT images: from the fleischner society 2017[J]. Radiology, 2017, 284(1): 228- 243.
5
张晓菊,白 莉,金发光,等. 肺结节诊治中国专家共识(2018年版)[J]. 中华结核和呼吸杂志2018, 41(10): 763- 771.
6
Ettinger DS, Wood DE, Aggarwal C, et al. NCCN Guidelines Insights:Non-small cell lung cancer, version 1.2020[J]. J Natl Compr Canc Netw, 2019, 17(12): 1464- 1472.
7
Ren Y, Huang S, Dai C, et al. Germline predisposition and copy number alteration in pre-stage lung adenocarcinomas presenting as ground-glass nodules[J]. Front Oncol, 2019, 9: 288.
8
Li X, Hu B, Li H, et al. Application of artificial intelligence in the diagnosis of multiple primary lung cancer[J]. Thorac Cancer, 2019, 10(11): 2168-2174.
9
Martini N, Melamed MR. Multiple primary lung cancers[J]. J Thorac Cardiovasc Surg, 1975, 70(4): 606-612.
10
陈亚男,滑炎卿. 多原发肺癌HRCT影像特点及其临床意义的研究进展[J]. 国际医学放射学杂志2018, 41(2): 175-179.
11
陈 丽,徐 培,唐丽娜,等. 530例肺结节患者的临床病理特征分析[J]. 广西医学2021, 43(16): 1977-1980.
12
解喜林. 纯磨玻璃结节的CT特征及定量分析对肺腺癌病理分类的预测价值[J]. 山西卫生健康职业学院学报2021, 31(1): 69-71.
13
Huber A, Landau J, Ebner L, et al. Performance of ultralow-dose CT with iterative reconstruction in lung cancer screening: limiting radiation exposure to the equivalent of conventional chest X-ray imaging[J]. Eur Radiol, 2016, 26(10): 3643-3652.
14
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[J]. Lancet Oncol, 2014, 15(12): 1332-1341.
15
Mun M, Kohno T. Single-stage surgical treatment of synchronous bilateral multiple lung cancers[J]. Ann Thorac Surg, 2007, 83(3): 1146-1151.
16
Rami-Porta R, Asamura H, Travis WD, et al. Lung cancer-major changes in the American Joint Committee on Cancer eighth edition cancer staging manual[J]. CA Cancer J Clin, 2017, 67(2): 138- 155.
17
Ather S, Kadir T, Gleeson F. Artificial intelligence and radiomics in pulmonary nodule management: current status and future applications[J]. Clin Radiol, 2020, 75(1): 13-19.
18
Li X, Hu B, Li H, et al. Application of artificial intelligence in the diagnosis of multiple primary lung cancer[J]. Thorac Cancer, 2019, 10(11): 2168-2174.
19
杜 林,张 洪,罗翔凤,等. 810例周边型肺阴影的人工智能辅助胸部CT诊断与术后病理诊断对比分析 [J]. 中国胸心血管外科临床杂志2022, 29(7): 854-858.
20
邹振宇,杨建丽,姚 娟,等. 肺结节人工智能检测系统的临床应用探索[J] . 新疆医学2022, 52(5): 524-526.
21
胡春红,赖 爽,秦正英,等. 深睿人工智能基于CT影像学的肺结节(直径≤1 cm)早期影像特征分析[J]. 重庆医学大学学报2022, 47(4): 473-478.
22
中国抗癌协会肿瘤介入学专业委员会. 胸部肿瘤经皮穿刺活检中国专家共识(2020版)[J]. 中华医学杂志2021, 101(3): 185-198.
23
Gould MK, James F, Iannettoni MD, et al. Evaluation of patients with pulmonary nodules: when is it lung cancer ACCP evidence based clinical practice guidelines (2nd edition)[J]. Chest, 2007, 132(3): 93-120.
24
Asano F, Aoe M, Ohsaki Y, et al. Deaths and complications associaciated with respiratory endoscopy:A survey by the Japan society for respiratory endoscopy in 2010[J]. Respirology, 2012, 17(3): 478-485.
25
Eberhardt R, Anantham D, Ernst A, et al. Multimodality bronchoscopic diagnosis of peripheral lung lesions: a randomized controlled trial[J]. Am J Respir Crit Care Med, 2007, 176(1): 36-41.
26
Wilson DS, Bartlett RJ. Improved diagnostic yield of bronchoscopy in a community practice: Combination of electromagnetic navigation system and rapid on-site evaluation[J]. J Bronchology Interv Pulmonol, 2007, 14(4): 227-232.
27
Jensen KW, Hsia DW, Seijo LM, et al. Multicenter experience with electromagnetic navigation bronchoscopy for the diagnosis of pulmonary nodules[J]. J Bronchology Interv Pulmonol, 2012, 19(3): 195-199.
28
Herth FJ, Eberhartdt R, Sterman D, et al. Bronchoscopic transparenchymal nodule access (BTPNA):First in human trial of a novel procedure for samplings solitary pulmonary nodules[J].Thorax, 2015, 70(4): 326-332.
29
Eberhartdt, Kahn N, Gompelann D, et al. Lung Point-A new approach to peripheral lesions[J]. J Thoracic Oncology, 2010, 5(10): 1559-1563.
30
(清)沈金鳌撰. 李占永,李晓林校注. 杂病源流犀烛[M]. 北京:中国中医药出版社,1994: 213.
31
(明)张介宾著. 景岳全书[M]. 北京:中国中医药出版社,1994: 472.
32
韩连奎,高树庚,谭锋维,等. 同时性多原发肺癌的诊治体会及处理策略新进展[J]. 中国肺癌杂志2018, 21(3): 180-184.
33
Lim W, Ridge CA, Nicholson AG, et al. The 8th lung cancer TNM classification and clinical staging system:review of the changes and clinical implications[J]. Quant Imaging Med Surg, 2018, 8(7): 709-718.
34
Aguiló R, Macià F, Porta M, et al. Multiple independent primary cancers do not adversely affect survival of the lung cancer patient[J]. Eur J Cardiothorac Surg, 2008, 34(5): 1075- 1080.
35
Macmahon H, Naidich DP, Goo JM, et al. Guidelines for management of incidental pulmonary nodules detected on CT images:from the fleischner society 2017[J]. Radiology, 2017, 284(1): 228-243.
36
Song YS, Park CM, Park SJ, et al. Volume and mass doubling times of persistent pulmonary subsolid nodules detected in patients without known malignancy[J]. Radiology, 2014, 273(1): 276-284.
37
刘广杰 , 贾宇轩 , 徐文华,等. 肺部同时性多发磨玻璃结节胸腔镜诊治流程研究[J] . 中国全科医学2020, 23(15): 1956-1960.
38
王洪武,张 楠,李冬妹,等. 房间隔封堵器治疗支气管残端胸膜瘘二例效果分析[J]. 中华结核和呼吸杂志2017, 40(4): 314-315.
39
Renaud S, Falcoz PE, Olland A, et al. Is radiofrequency ablation or stereotactic ablative radiotherapy the best treatment for radically treatable primary lung cancer unfit for surgery? [J]. Interact Cardiovasc Thorac Surg, 2013, 16(1): 68-73.
40
栾宏辉. 立体定向放射治疗肺癌患者的效果[J]. 中国民康医学2020, 32(22): 17-21.
41
Ye X, Fan WJ, Wang ZM, et al. Expert consensus on thermal ablation therapy of pulmonary subsolid nodules (2021 Edition) [J]. J Can Res Ther, 2021, 17: 1141-1156.
42
张 肖,肖越勇,李成利,等. 影像学引导下肺结节冷冻消融专家共识[J]. 中国介入影像与治疗学202219(1): 2-6.
43
许海柱,祝佳佳,张 栩,等. 基于聚类分析和因子分析的肺小结节患者中医证候特点研究[J]. 中国中医药信息杂志2020, 27(2): 84-87.
44
郝雪然,李晓林,郭吉卫. 肺癌患者心理状态及临床症状对生存质量的影响[J]. 中国卫生工程学2020, 19(5): 707- 709.
45
侯秋月,史锁芳. 史锁芳运用疏肝理气、化痰散结法治疗肺小结节经验[J]. 中华中医药杂志2019, 34(10): 4652-4654.
46
张 盼,李素云. 李素云教授辨证治疗肺结节病经验[J]. 世界中医药2016, 11(3): 462-463, 466.
47
南岩东,李玉娟,刘苗苗,等. 人工智能在肺结节良恶性鉴别诊断中的价值分析[J/CD]. 中华肺部疾病杂志(电子版), 2020, 13(6): 760-763.
48
Karki A, Shah R, Fein A. Multiple pulmonary nodules in malignancy[J]. Curr Opin Pulm Med, 2017, 23(4): 285-289.
49
Chun EJ, Lee HJ, Kang WJ, et al. Differentiation between malignancy and inflammation in pulmonary ground-glass nodules: The feasibility of integrated (18)F-FDG PET/CT[J]. Lung Cancer, 2009, 65(2): 180-186.
50
Silva M, Sverzellati N, Manna C, et al. Long-term surveillance of ground-glass nodules: evidence from the MILD trial[J]. J Thorac Oncol, 2012, 7(10): 1541-1546.
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