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中华危重症医学杂志(电子版) ›› 2023, Vol. 16 ›› Issue (03) : 245 -249. doi: 10.3877/cma.j.issn.1674-6880.2023.03.013

综述

影像组学在肺部炎症性疾病中的研究进展
陈美西, 徐昉, 林时辉()   
  1. 400016 重庆,重庆医科大学附属第一医院重症医学科
  • 收稿日期:2022-07-08 出版日期:2023-06-30
  • 通信作者: 林时辉
  • 基金资助:
    重庆市科卫联合医学科研项目(防疫一线医务人员项目)(2020FYYX137); 重庆市科卫联合医学科研项目(中青年医学高端人才项目)(2020GDRC001)
  • Received:2022-07-08 Published:2023-06-30
引用本文:

陈美西, 徐昉, 林时辉. 影像组学在肺部炎症性疾病中的研究进展[J]. 中华危重症医学杂志(电子版), 2023, 16(03): 245-249.

肺部炎症性疾病主要包括急性肺损伤、慢性阻塞性肺疾病(chronic obstructive pulmonary disease,COPD)、肺炎等发病机制尚不明确的疾病。医学影像是临床工作中的一项重要技术,可以在临床实践中辅助疾病的诊断及疗效评价等。随着人工智能技术(artificial intelligence,AI)逐步应用到医疗领域,医学影像学成为AI的重要领域。影像组学是此类工作模式的代表,主要体现在使用以深度学习(deep learning,DL)为代表的方法对影像大数据进行挖掘、搜索和提取相关信息[1]。应用影像组学可以更好地挖掘医学影像数据,从而更加精确地指导临床疾病的诊断、治疗和预后。目前,临床上影像组学主要应用于肿瘤相关疾病,而实体肿瘤在基因、蛋白质、细胞、微环境、组织和器官等不同方面具有极大的时间及空间异质性。影像组学具有非侵入性捕捉肿瘤内异质性的能力,在区分良恶性肿瘤、评价肿瘤治疗反应及评估癌症遗传学特征等方面都显示出了巨大潜力。本文旨在介绍影像组学的发展与基本研究方法、影像组学在肺部炎症性疾病中的应用以及影像组学目前面临的挑战和未来趋势。

1
Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine[J]. Nat Rev Clin Oncol, 2017, 14 (12): 749-762.
2
Committee on the Review of Omics-Based Tests for Predicting Patient Outcomes in Clinical Trials, Board on Health Care Services, Board on Health Sciences Policy, et al. Evolution of translational omics: lessons learned and the path forward[M]. Washington (DC): National Academies Press (US), 2012.
3
Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data[J]. Radiology, 2016, 278 (2): 563-577.
4
World Health Organization. Global tuberculosis report 2017[M]. Geneva, Switzerland: WHO, 2017.
5
World Health Organization. Chest radiography in tuberculosis detection: summary of current WHO recommendations and guidance on programmatic approaches[M/OL]. Geneva: WHO Press, 2006 [2022-05-23].

URL    
6
Nachiappan AC, Rahbar K, Shi X, et al. Pulmonary tuberculosis: role of radiology in diagnosis and management[J]. Radiographics, 2017, 37 (1): 52-72.
7
Hwang EJ, Park S, Jin KN, et al. Development and validation of a deep learning-based automatic detection algorithm for active pulmonary tuberculosis on chest radiographs[J]. Clin Infect Dis, 2019, 69 (5): 739-747.
8
Lee JH, Park S, Hwang EJ, et al. Deep learning-based automated detection algorithm for active pulmonary tuberculosis on chest radiographs: diagnostic performance in systematic screening of asymptomatic individuals[J]. Eur Radiol, 2021, 31 (2): 1069-1080.
9
Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2021, 71 (3): 209-249.
10
Jonas DE, Reuland DS, Reddy SM, et al. Screening for lung cancer with low-dose computed tomography: updated evidence report and systematic review for the US Preventive Services Task Force[J]. JAMA, 2021, 325 (10): 971-987.
11
Gavelli G, Giampalma E. Sensitivity and specificity of chest X-ray screening for lung cancer: review article[J]. Cancer, 2000, 89 (11 Suppl): 2453-2456.
12
Ferreira Junior JR, Koenigkam-Santos M, Cipriano FEG, et al. Radiomics-based features for pattern recognition of lung cancer histopathology and metastases[J]. Comput Methods Programs Biomed, 2018 (159): 23-30.
13
Petkovska I, Shah SK, McNitt-Gray MF, et al. Pulmonary nodule characterization: a comparison of conventional with quantitative and visual semi-quantitative analyses using contrast enhancement maps[J]. Eur J Radiol, 2006, 59 (2): 244-252.
14
Chen CH, Chang CK, Tu CY, et al. Radiomic features analysis in computed tomography images of lung nodule classification[J]. PLoS One, 2018, 13 (2): e192002.
15
Nam JG, Park S, Hwang EJ, et al. Development and validation of deep learning-based automatic detection algorithm for malignant pulmonary nodules on chest radiographs[J]. Radiology, 2019, 290 (1): 218-228.
16
World Health Organization. The top 10 causes of death[EB/OL]. (2020-12-09) [2022-05-23].

URL    
17
Matsuoka S, Yamashiro T, Washko GR, et al. Quantitative CT assessment of chronic obstructive pulmonary disease[J]. Radiographics, 2010, 30 (1): 55-66.
18
Lafata KJ, Zhou Z, Liu JG, et al. An exploratory radiomics approach to quantifying pulmonary function in CT images[J]. Sci Rep, 2019, 9 (1): 11509.
19
Martinez FJ, Collard HR, Pardo A, et al. Idiopathic pulmonary fibrosis[J]. Nat Rev Dis Primers, 2017 (3): 17074.
20
Christe A, Peters AA, Drakopoulos D, et al. Computer-aided diagnosis of pulmonary fibrosis using deep learning and CT images[J]. Invest Radiol, 2019, 54 (10): 627-632.
21
Zhang T, Yuan M, Zhong Y, et al. Differentiation of focal organising pneumonia and peripheral adenocarcinoma in solid lung lesions using thin-section CT-based radiomics[J]. Clin Radiol, 2019, 74 (1): 78.e23-78.e30.
22
Zech JR, Badgeley MA, Liu M, et al. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study[J]. PLoS Med, 2018, 15 (11): e1002683.
23
World Health Organization. WHO coronavirus (COVID-19) dashboard[EB/OL]. [2022-05-23].

URL    
24
Xie X, Zhong Z, Zhao W, et al. Chest CT for typical coronavirus disease 2019 (COVID-19) pneumonia: relationship to negative RT-PCR testing[J]. Radiology, 2020, 296 (2): E41-E45.
25
Ai T, Yang Z, Hou H, et al. Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: a report of 1 014 cases[J]. Radiology, 2020, 296 (2): E32-E40.
26
Wang S, Zha Y, Li W, et al. A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis[J]. Eur Respir J, 2020, 56 (2): 2000775.
27
Li L, Qin L, Xu Z, et al. Using artificial intelligence to detect COVID-19 and community-acquired pneumonia based on pulmonary CT: evaluation of the diagnostic accuracy[J]. Radiology, 2020, 296 (2): E65-E71.
28
Bai HX, Wang R, Xiong Z, et al. Artificial intelligence augmentation of radiologist performance in distinguishing COVID-19 from pneumonia of other origin at chest CT[J]. Radiology, 2020, 296 (3): E156-E165.
29
Ranieri VM, Rubenfeld GD, Thompson BT, et al. Acute respiratory distress syndrome: the Berlin definition[J]. JAMA, 2012, 307 (23): 2526-2533.
30
Bellani G, Laffey JG, Pham T, et al. Epidemiology, patterns of care, and mortality for patients with acute respiratory distress syndrome in intensive care units in 50 countries[J]. JAMA, 2016, 315 (8): 788-800.
31
Reamaroon N, Sjoding MW, Gryak J, et al. Automated detection of acute respiratory distress syndrome from chest X-rays using directionality measure and deep learning features[J]. Comput Biol Med, 2021 (134): 104463.
32
Vallières M, Freeman CR, Skamene SR, et al. A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities[J]. Phys Med Biol, 2015, 60 (14): 5471-5496.
33
Wu W, Parmar C, Grossmann P, et al. Exploratory study to identify radiomics classifiers for lung cancer histology[J]. Front Oncol, 2016 (6): 71.
34
Liu Y, Kim J, Balagurunathan Y, et al. Radiomic features are associated with EGFR mutation status in lung adenocarcinomas[J]. Clin Lung Cancer, 2016, 17 (5): 441-448.e6.
35
Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach[J]. Nat Commun, 2014 (5): 4006.
36
Wang S, Shi J, Ye Z, et al. Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning[J]. Eur Respir J, 2019, 53 (3): 1800986.
37
Zhu Y, Liu YL, Feng Y, et al. A CT-derived deep neural network predicts for programmed death ligand-1 expression status in advanced lung adenocarcinomas[J]. Ann Transl Med, 2020, 8 (15): 930.
38
Song L, Zhu Z, Mao L, et al. Clinical, conventional CT and radiomic feature-based machine learning models for predicting ALK rearrangement status in lung adenocarcinoma patients[J]. Front Oncol, 2020 (10): 369.
39
Li T, Kung H, Mack PC, et al. Genotyping and genomic profiling of non-small-cell lung cancer: implications for current and future therapies[J]. J Clin Oncol, 2013, 31 (8): 1039-1049.
40
Itakura H, Achrol AS, Mitchell LA, et al. Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities[J]. Sci Transl Med, 2015, 7 (303): 303ra138.
41
Sacher AG, Dahlberg SE, Heng J, et al. Association between younger age and targetable genomic alterations and prognosis in non-small-cell lung cancer[J]. JAMA Oncol, 2016, 2 (3): 313-320.
42
Zhang L, Chen B, Liu X, et al. Quantitative biomarkers for prediction of epidermal growth factor receptor mutation in non-small cell lung cancer[J]. Transl Oncol, 2018, 11 (1): 94-101.
43
Lustberg T, van Soest J, Gooding M, et al. Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer[J]. Radiother Oncol, 2018, 126 (2): 312-317.
44
Wu G, Yang P, Xie Y, et al. Development of a clinical decision support system for severity risk prediction and triage of COVID-19 patients at hospital admission: an international multicentre study[J]. Eur Respir J, 2020, 56 (2): 2001104.
45
Furin J, Cox H, Pai M. Tuberculosis[J]. Lancet, 2019, 393 (10181): 1642-1656.
46
Jaeger S, Juarez-Espinosa OH, Candemir S, et al. Detecting drug-resistant tuberculosis in chest radiographs[J]. Int J Comput Assist Radiol Surg, 2018, 13 (12): 1915-1925.
47
Hosny A, Parmar C, Coroller TP, et al. Deep learning for lung cancer prognostication: a retrospective multi-cohort radiomics study[J]. PLoS Med, 2018, 15 (11): e1002711.
48
González G, Ash SY, Vegas-Sánchez-Ferrero G, et al. Disease staging and prognosis in smokers using deep learning in chest computed tomography[J]. Am J Respir Crit Care Med, 2018, 197 (2): 193-203.
49
Yang X, Yu Y, Xu J, et al. Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study[J]. Lancet Respir Med, 2020, 8 (5): 475-481.
50
Chen Y, Wang Y, Zhang Y, et al. A quantitative and radiomics approach to monitoring ARDS in COVID-19 patients based on chest CT: a retrospective cohort study[J]. Int J Med Sci, 2020, 17 (12): 1773-1782.
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