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ENGLISH ABSTRACT
人工智能结合CT影像组学在慢性阻塞性肺疾病中的应用
王若雨
孙伟
罗祖金
王晶
作者及单位信息
·
DOI： 10.3760/cma.j.cn112147-20240830-00517
Application of artificial intelligence in combination with CT radiomics in chronic obstructive pulmonary disease
Wang Ruoyu
Sun Wei
Luo Zujin
Wang Jing
Authors Info & Affiliations
Wang Ruoyu
Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing 100043, China
Sun Wei
Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing 100043, China
Luo Zujin
Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing 100043, China
Wang Jing
Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing 100043, China
·
DOI： 10.3760/cma.j.cn112147-20240830-00517
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摘要

随着CT成像越来越多地用于肺结节评估以及吸烟者肺癌的诊断和筛查，我们有更多机会使用CT图像来识别早期慢性阻塞性肺疾病（简称慢阻肺病）患者。此外，随着人工智能的不断发展，不仅能够通过对患者肺部CT的分析帮助临床医师对慢阻肺病患者的早诊早治，也能够对治疗方案进行指导。本文主要对人工智能结合CT影像组学在慢阻肺病诊疗中的应用进行综述。

ABSTRACT

As CT imaging is increasingly used for the evaluation of lung nodules and the diagnosis and screening of lung cancer in smokers, we have more opportunities to use CT images to identify patients with early-stage chronic obstructive pulmonary disease(COPD). Furthermore, with the continuous advancement of artificial intelligence, it can not only assist clinicians in the early diagnosis and treatment of COPD patients through the analysis of lung CT scans but also help guide treatment strategies. This article primarily reviewed the application of artificial intelligence combined with CT radiomics in the diagnosis and treatment of chronic obstructive pulmonary disease.

Wang Jing, Email: mocdef.6ab21codgnijgnaw
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引用本文

王若雨,孙伟,罗祖金,等. 人工智能结合CT影像组学在慢性阻塞性肺疾病中的应用[J]. 中华结核和呼吸杂志,2025,48(02)：186-190.

DOI:10.3760/cma.j.cn112147-20240830-00517

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慢性阻塞性肺疾病（简称慢阻肺病）是全球三大死亡原因之一，占全球总死亡人数的6%，造成了巨大的经济和社会负担。由于临床医师对慢阻肺病不典型症状识别有限、肺功能测定尚未普及、部分患者同时有其他影响慢阻肺病诊断的合并症等，使得现阶段对慢阻肺病的漏诊率、误诊率较高。随着高分辨率CT成像越来越多地用于肺结节评估以及吸烟者的肺癌诊断和筛查，我们有更多机会使用CT图像来识别慢阻肺病患者，随后再进行肺功能测定进一步确诊。因此，在早期识别、诊断慢阻肺病中，CT扫描的作用显得尤为重要。人工智能（artificial intelligence，AI）指使用计算机来模拟人的某些思维过程和智能行为（如学习、推理、思考、规划等），目前已被广泛应用于临床实践，包括慢性阻塞性肺病的早期筛查和诊断、临床分级和风险评估、成像研究和远期预后等。
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参考文献
[1]
McDonough JE , Yuan R , Suzuki M ,et al. Small-airway obstruction and emphysema in chronic obstructive pulmonary disease[J]. N Engl J Med, 2011,365(17):1567-1575. DOI: 10.1056/NEJMoa1106955 .
返回引文位置Google Scholar
百度学术
万方数据
[2]
Diaz AA , Estépar RSJ , Washko GR . Computed tomographic airway morphology in chronic obstructive pulmonary disease. Remodeling or innate anatomy?[J]. Annal Am Thorac, 2016,13(1):4-9.
返回引文位置Google Scholar
百度学术
万方数据
[3]
Diaz AA , Orejas JL , Grumley S ,et al. Airway-Occluding Mucus Plugs and Mortality in Patients With Chronic Obstructive Pulmonary Disease[J]. JAMA, 2023,329(21):1832-1839. DOI: 10.1001/jama.2023.2065 .
返回引文位置Google Scholar
百度学术
万方数据
[4]
Sheikh K , Coxson HO , Parraga G . This is what COPD looks like[J]. Respirology (Carlton, Vic.), 2016,21(2):224-236.
返回引文位置Google Scholar
百度学术
万方数据
[5]
Vegas Sánchez-Ferrero G , Díaz AA , Ash SY ,et al. Quantification of Emphysema Progression at CT Using Simultaneous Volume, Noise, and Bias Lung Density Correction[J]. Radiology, 2024,310(1):e231632. DOI: 10.1148/radiol.231632 .
返回引文位置Google Scholar
百度学术
万方数据
[6]
Shimizu K , Tanabe N , Tho NV ,et al. Percent low attenuation volume and fractal dimension of low attenuation clusters on CT predict dif ferent long-term outcomes in COPD [J]. Thorax, 2020,75(2):116-122.
返回引文位置Google Scholar
百度学术
万方数据
[7]
Matsuo Y , Ogawa E , Tsunoda Y ,et al. Inspiratory and Expiratory Computed Tomography Imaging Clusters Reflect Functional Characteristics in Chronic Obstructive Pulmonary Disease[J]. Int J Chron Obstruct Pulmon Dis, 2023,18:1047-1055. DOI: 10.2147/COPD.S405845 .
返回引文位置Google Scholar
百度学术
万方数据
[8]
Park J , Hobbs BD , Crapo JD ,et al. Subtyping COPD by Using Visual and Quantitative CT Imaging Features[J]. Chest, 2020,157(1):47-60. DOI: 10.1016/j.chest.2019.06.015 .
返回引文位置Google Scholar
百度学术
万方数据
[9]
Lynch DA , Austin JH , Hogg JC ,et al. CT-Definable Subtypes of Chronic Obstructive Pulmonary Disease: A Statement of the Fleischner Society[J]. Radiology, 2015,277(1):192-205. DOI: 10.1148/radiol.2015141579 .
返回引文位置Google Scholar
百度学术
万方数据
[10]
Bhatt SP , Soler X , Wang X ,et al. Association between Functional Small Airway Disease and FEV 1 Decline in Chronic Obstructive Pulmonary Disease [J]. Am J Respir Crit Care Med, 2016,194(2):178-184. DOI: 10.1164/rccm.201511-2219OC .
返回引文位置Google Scholar
百度学术
万方数据
[11]
Ley-Zaporozhan J , Ley S , Kauczor HU . Morphological and functional imaging in COPD with CT and MRI: present and future[J]. Eur Radiol, 2008,18(3):510-521. DOI: 10.1007/s00330-007-0772-1 .
返回引文位置Google Scholar
百度学术
万方数据
[12]
Wells JM , Washko GR , Han MK ,et al. Pulmonary arterial enlargement and acute exacerbations of COPD[J]. N Engl J Med, 2012,367(10):913-921. DOI: 10.1056/NEJMoa1203830 .
返回引文位置Google Scholar
百度学术
万方数据
[13]
Tang L , Coxson HO , Lam S ,et al. Towards large-scale case-finding: training and validation of residual networks for detection of chronic obstructive pulmonary disease using low-dose CT[J]. Lancet Digit Health, 2020,2(5):e259-e267. DOI: 10.1016/S2589-7500(20)30064-9 .
返回引文位置Google Scholar
百度学术
万方数据
[14]
Xu C , Qi S , Feng J ,et al. DCT-MIL: Deep CNN transferred multiple instance learning for COPD identification using CT images[J]. Phys Med Biol, 2020,65(14):145011. DOI: 10.1088/1361-6560/ab857d .
返回引文位置Google Scholar
百度学术
万方数据
[15]
Xu M , Qi S , Yue Y ,et al. Segmentation of lung parenchyma in CT images using CNN trained with the clustering algorithm generated dataset[J]. Biomed Eng Online, 2019,18(1):2. DOI: 10.1186/s12938-018-0619-9 .
返回引文位置Google Scholar
百度学术
万方数据
[16]
Xie W , Jacobs C , Charbonnier JP ,et al. Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans[J]. IEEE Trans Med Imaging, 2020,39(8):2664-2675. DOI: 10.1109/TMI.2020.2995108 .
返回引文位置Google Scholar
百度学术
万方数据
[17]
Souza LF de F , Silva ICL , Marques AG ,et al. Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation[J]. Sensors (Basel, Switzerland), 2020,20(23):6711.
返回引文位置Google Scholar
百度学术
万方数据
[18]
Sørensen L , Nielsen M , Petersen J ,et al. Chronic Obstructive Pulmonary Disease Quantification Using CT Texture Analysis and Densitometry: Results From the Danish Lung Cancer Screening Trial[J]. AJR Am J Roentgenol, 2020,214(6):1269-1279. DOI: 10.2214/AJR.19.22300 .
返回引文位置Google Scholar
百度学术
万方数据
[19]
Ginsburg SB , Lynch DA , Bowler RP ,et al. Automated texture-based quantification of centrilobular nodularity and centrilobular emphysema in chest CT images[J]. Acad Radiol, 2012,19(10):1241-1251. DOI: 10.1016/j.acra.2012.04.020 .
返回引文位置Google Scholar
百度学术
万方数据
[20]
Yun J , Park J , Yu D ,et al. Improvement of fully automated airway segmentation on volumetric computed tomographic images using a 2.5 dimensional convolutional neural net[J]. Medical Image Analysis, 2019,51:13-20.
返回引文位置Google Scholar
百度学术
万方数据
[21]
Nadeem SA , Hoffman EA , Sieren JC ,et al. A CT-Based Automated Algorithm for Airway Segmentation Using Freeze-and-Grow Propagation and Deep Learning[J]. IEEE Trans Med Imaging, 2021,40(1):405-418. DOI: 10.1109/TMI.2020.3029013 .
返回引文位置Google Scholar
百度学术
万方数据
[22]
周秀秀. 功能小气道、肺血管CT定量分析对COPD早期诊断的临床价值[D]:中国人民解放军海军军医大学, 2021.
返回引文位置Google Scholar
百度学术
万方数据
[23]
Chen B , Liu Z , Lu J ,et al. Deep learning parametric response mapping from inspiratory chest CT scans: a new approach for small airway disease screening[J]. Respir Res, 2023,24(1):299. DOI: 10.1186/s12931-023-02611-2 .
返回引文位置Google Scholar
百度学术
万方数据
[24]
Global Initiative for Chronic Obstructive Lung Disease. 2024 GOLD Report-Global Initiative for Chronic Obstructive Lung Disease[EB/OL].[ 2024-08-22]. https://goldcopd.org/2024-gold-report/.
返回引文位置Google Scholar
百度学术
万方数据
[25]
Angelini ED , Yang J , Balte PP ,et al. Pulmonary emphysema subtypes defined by unsupervised machine learning on CT scans[J]. Thorax, 2023,78(11):1067-1079.
返回引文位置Google Scholar
百度学术
万方数据
[26]
Peng L , Lin L , Hu H ,et al. Classification and Quantification of Emphysema Using a Multi-Scale Residual Network[J]. IEEE J Biomed Health Inform, 2019,23(6):2526-2536. DOI: 10.1109/JBHI.2018.2890045 .
返回引文位置Google Scholar
百度学术
万方数据
[27]
Hasenstab KA , Yuan N , Retson T ,et al. Automated CT Staging of Chronic Obstructive Pulmonary Disease Severity for Predicting Disease Progression and Mortality with a Deep Learning Convolutional Neural Network[J]. Radiol Cardiothorac Imaging, 2021,3(2):e200477. DOI: 10.1148/ryct.2021200477 .
返回引文位置Google Scholar
百度学术
万方数据
[28]
Sun J , Liao X , Yan Y ,et al. Detection and staging of chronic obstructive pulmonary disease using a computed tomography-based weakly supervised deep learning approach[J]. Eur Radiol, 2022,32(8):5319-5329. DOI: 10.1007/s00330-022-08632-7 .
返回引文位置Google Scholar
百度学术
万方数据
[29]
Bian Z , Charbonnier JP , Liu J ,et al. Small airway segmentation in thoracic computed tomography scans: a machine learning approach[J]. Phys Med Biol, 2018,63(15):155024. DOI: 10.1088/1361-6560/aad2a1 .
返回引文位置Google Scholar
百度学术
万方数据
[30]
张琳. 低剂量CT和深度卷积神经网络对慢性阻塞性肺疾病分类和支气管测量的应用研究[D]. 南京:南京医科大学, 2022.
返回引文位置Google Scholar
百度学术
万方数据
[31]
Yuan NF , Hasenstab K , Retson T ,et al. Unsupervised Learning Identifies Computed Tomographic Measurements as Primary Drivers of Progression, Exacerbation, and Mortality in Chronic Obstructive Pulmonary Disease[J]. Ann Am Thorac Soc, 2022,19(12):1993-2002. DOI: 10.1513/AnnalsATS.202110-1127OC .
返回引文位置Google Scholar
百度学术
万方数据
备注信息
A
王晶，Email： mocdef.6ab21codgnijgnaw
B
王若雨, 孙伟, 罗祖金, 等. 人工智能结合CT影像组学在慢性阻塞性肺疾病中的应用[J]. 中华结核和呼吸杂志, 2025, 48(2): 186-190. DOI: 10.3760/cma.j.cn112147-20240830-00517.
C
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