非结核分枝杆菌肺病影像学表现及应用人工智能新技术的研究进展

doi:10.19982/j.issn.1000-6621.20230404

中国防痨杂志 ›› 2024, Vol. 46 ›› Issue (3): 362-366.doi: 10.19982/j.issn.1000-6621.20230404

• 综述 • 上一篇

非结核分枝杆菌肺病影像学表现及应用人工智能新技术的研究进展

高珊¹, 聂文娟¹, 侯代伦²(), 初乃惠¹()

¹北京市结核病胸部肿瘤研究所/首都医科大学附属北京胸科医院结核一科,北京 101149
²北京市结核病胸部肿瘤研究所/首都医科大学附属北京胸科医院影像科,北京 101149

收稿日期:2023-11-11 出版日期:2024-03-10 发布日期:2024-03-05
通信作者: 初乃惠,Email: dongchu1994@sina.com;侯代伦,Email: hou.dl@mail.ccmu.edu.cn
基金资助:
国家自然科学基金(82100002);北京滚动项目(2023-3)

Research progress on imaging manifestations of nontuberculosis mycobacterial pulmonary and application of new artificial intelligence technology

Gao Shan¹, Nie Wenjuan¹, Hou Dailun²(), Chu Naihui¹()

¹The First Department of Tuberculosis, Beijing Tuberculosis and Thoracic Tumor Research Institute/Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
²Department of Imaging, Beijing Tuberculosis and Thoracic Tumor Research Institute/Beijing Chest Hospital, Capital Medical University, Beijing 101149,China

Received:2023-11-11 Online:2024-03-10 Published:2024-03-05
Contact: Chu Naihui, Email: dongchu1994@sina.com; Hou Dailun, Email: hou.dl@mail.ccmu.edu.cn
Supported by:
National Natural Science Foundation of China(82100002);Beijing Rolling Project(2023-3)

摘要/Abstract

摘要：

非结核分枝杆菌病在全球的发病率逐渐上升,并且已经成为我国结核病防治领域重点关注的问题之一。非结核分枝杆菌肺病在影像学上有特征性表现,因此影像学成为诊断非结核分枝杆菌肺病的重要辅助诊断方式。近年来,人工智能在医学领域中飞速发展,基于影像学的人工智能技术在非结核分枝杆菌肺病方向有了新的研究尝试。因此,笔者整理了非结核分枝杆菌肺病的影像学表现,介绍了人工智能新技术在影像学诊断、预后和治疗评价中的应用,期望能够更好地了解非结核分枝杆菌肺病在人工智能领域的发展趋势,以及为疾病诊断、治疗评估方式提供新思路。

关键词: 分枝杆菌,非典型性, 体层摄影术,X线计算机, 人工智能, 诊断

Abstract:

The incidence of diseases caused by nontuberculosis mycobacterial is gradually increasing in the world, and it has become one of the key issues of tuberculosis prevention and control in China. Medical imaging has become an essential auxiliary diagnostic method due to the characteristic imaging manifestations of nontuberculosis mycobacteria pulmonary disease. Artificial intelligence has developed rapidly in the field of medicine, and imaging-based artificial intelligence technology has made new research attempts in the direction of nontuberculosis mycobacterial pulmonary disease in recent years. Therefore, the authors collates the imaging manifestations of non-tuberculosis mycobacteria pulmonary disease and introduces the application of new artificial intelligence technologies in imaging diagnosis, prognosis, and treatment evaluation, with the expectation of better understanding the development of disease in the field of artificial intelligence as well as providing new thoughts on the way of diagnosis and treatment evaluation.

Key words: Mycobacteria, atypical, Tomography, X-ray computed, Artificial intelligence, Diagnosis

中图分类号:

高珊, 聂文娟, 侯代伦, 初乃惠. 非结核分枝杆菌肺病影像学表现及应用人工智能新技术的研究进展[J]. 中国防痨杂志, 2024, 46(3): 362-366. doi: 10.19982/j.issn.1000-6621.20230404

Gao Shan, Nie Wenjuan, Hou Dailun, Chu Naihui. Research progress on imaging manifestations of nontuberculosis mycobacterial pulmonary and application of new artificial intelligence technology[J]. Chinese Journal of Antituberculosis, 2024, 46(3): 362-366. doi: 10.19982/j.issn.1000-6621.20230404

参考文献 31

[1]	中华医学会结核病学分会. 非结核分枝杆菌病诊断与治疗指南(2020年版). 中华结核和呼吸杂志, 2020, 43(11):918-946. doi:10.3760/cma.j.cn112147-20200508-00570.
[2]	Cowman S, van Ingen J, Griffith DE, et al. Non-tuberculous mycobacterial pulmonary disease. Eur Respir J, 2019, 54(1):1900250. doi:10.1183/13993003.00250-2019.
[3]	全国结核病流行病学抽样调查技术指导组. 2000年全国结核病流行病学抽样调查报告. 中国防痨杂志, 2002, 24(2):65-66.
[4]	全国第五次结核病流行病学抽样调查技术指导组, 全国第五次结核病流行病学抽样调查办公室. 2010年全国第五次结核病流行病学抽样调查报告. 中国防痨杂志, 2012, 34(8):485-508.
[5]	Ying C, Li X, Lv S, et al. T-SPOT with CT image analysis based on deep learning for early differential diagnosis of nontuberculous mycobacteria pulmonary disease and pulmonary tuberculosis. Int J Infect Dis, 2022, 125:42-50. doi:10.1016/j.ijid.2022.09.031.
[6]	Kulkarni S, Jha S. Artificial Intelligence, Radiology, and Tuberculosis: A Review. Acad Radiol, 2020, 27(1):71-75. doi:10.1016/j.acra.2019.10.003. pmid: 31759796
[7]	Tavaziva G, Majidulla A, Nazish A, et al. Diagnostic accuracy of a commercially available, deep learning-based chest X-ray interpretation software for detecting culture-confirmed pulmonary tuberculosis. Int J Infect Dis, 2022, 122:15-20. doi:10.1016/j.ijid.2022.05.037. pmid: 35597555
[8]	Li Y, Xu Z, Lv X, et al. Radiomics analysis of lung CT for multidrug resistance prediction in active tuberculosis: a multicentre study. Eur Radiol, 2023, 33(9):6308-6317. doi:10.1007/s00330-023-09589-x. pmid: 37004571
[9]	Li Y, Wang B, Wen L, et al. Machine learning and radiomics for the prediction of multidrug resistance in cavitary pulmonary tuberculosis: a multicentre study. Eur Radiol, 2023, 33(1):391-400. doi:10.1007/s00330-022-08997-9.
[10]	Nguyen I, Green O, Modahl L. Nontuberculous Mycobacterial Pulmonary Disease: A Clinical and Radiologic Update. Semin Roentgenol, 2022, 57(1):75-89. doi:10.1053/j.ro.2021.10.008. pmid: 35090712
[11]	Eisenberg I, Yasin A, Fuks L, et al. Radiologic Characteristics of Non-tuberculous Mycobacteria Infection in Patients with Bronchiectasis. Lung, 2020, 198(4):715-722. doi:10.1007/s00408-020-00371-0. pmid: 32591896
[12]	Aksamit TR, O’Donnell AE, Barker A, et al. Adult Patients With Bronchiectasis: A First Look at the US Bronchiectasis Research Registry. Chest, 2017, 151(5):982-992. doi:10.1016/j.chest.2016.10.055. pmid: 27889361
[13]	Anjos LRBD, Parreira PL, Torres PPTS, et al. Non-tuberculous mycobacterial lung disease: a brief review focusing on radiological findings. Rev Soc Bras Med Trop, 2020, 53:e20200241. doi:10.1590/0037-8682-0241-2020.
[14]	Dettmer S, Ringshausen FC, Fuge J, et al. Computed Tomography in Adults with Bronchiectasis and Nontuberculous Mycobacterial Pulmonary Disease: Typical Imaging Findings. J Clin Med, 2021, 10(12):2736. doi:10.3390/jcm10122736.
[15]	中国研究型医院学会感染与炎症放射专业委员会, 中华医学会放射学分会传染病学组, 中国医师协会放射医师分会感染影像专业委员会, 等. 非结核分枝杆菌肺病影像诊断专家共识. 中国研究型医院, 2021, 8(3):1-6. doi:10.19450/j.cnki.jcrh.2021.03.001.
[16]	Marušic'A, Kuhtic'I, Mažuranic'I, et al. Nodular distribution pattern on chest computed tomography (CT) in patients diagnosed with nontuberculous mycobacteria (NTM) infections. Wien Klin Wochenschr, 2021, 133(9-10):470-477. doi:10.1007/s00508-020-01701-1.
[17]	Yin H, Gu X, Wang Y, et al. Clinical characteristics of patients with bronchiectasis with nontuberculous mycobacterial disease in Mainland China: a single center cross-sectional study. BMC Infect Dis, 2021, 21(1):1216. doi:10.1186/s12879-021-06917-8. pmid: 34872515
[18]	Gundacker ND, Gonzalez JA, Sheinin YM, et al. Hot Tub Lung: Case Report and Review of the Literature. WMJ, 2022, 121(2):E31-E33.
[19]	Jamal F, Hammer MM. Nontuberculous Mycobacterial Infections. Radiol Clin North Am, 2022, 60(3):399-408. doi:10.1016/j.rcl.2022.01.012.
[20]	Wang D, Lin W, Cheng H, et al. Clinical Characteristics and Antimicrobial Susceptibility of Mycobacterium intracellulare and Mycobacterium abscessus Pulmonary Diseases: A Retrospective Study. Can J Infect Dis Med Microbiol, 2022, 2022:2642200. doi:10.1155/2022/2642200.
[21]	Chu HQ, Li B, Zhao L, et al. Chest imaging comparison between non-tuberculous and tuberculosis mycobacteria in sputum acid fast bacilli smear-positive patients. Eur Rev Med Pharmacol Sci, 2015, 19(13):2429-2439. doi:10.1183/13993003.congress-2015.pa2674.
[22]	BaHammam A, Kambal A, Sharif Y, et al. Comparison of clinico-radiological features of patients with positive cultures of nontuberculous mycobacteria and patients with tuberculosis. Saudi Med J, 2005, 26(5):754-758. pmid: 15951864
[23]	Hosny A, Parmar C, Quackenbush J, et al. Artificial intelligence in radiology. Nat Rev Cancer, 2018, 18(8):500-510. doi:10.1038/s41568-018-0016-5. pmid: 29777175
[24]	Avanzo M, Wei L, Stancanello J, et al. Machine and deep learning methods for radiomics. Med Phys, 2020, 47(5):e185-e202. doi:10.1002/mp.13678.
[25]	Liu CJ, Tsai CC, Kuo LC, et al. A deep learning model using chest X-ray for identifying TB and NTM-LD patients: a cross-sectional study. Insights Imaging, 2023, 14(1):67. doi:10.1186/s13244-023-01395-9.
[26]	Park M, Lee Y, Kim S, et al. Distinguishing nontuberculous mycobacterial lung disease and Mycobacterium tuberculosis lung disease on X-ray images using deep transfer learning. BMC Infect Dis, 2023, 23(1):32. doi:10.1186/s12879-023-07996-5.
[27]	Yan Q, Wang W, Zhao W, et al. Differentiating nontuberculous mycobacterium pulmonary disease from pulmonary tuberculosis through the analysis of the cavity features in CT images using radiomics. BMC Pulm Med, 2022, 22(1):4. doi:10.1186/s12890-021-01766-2. pmid: 34991543
[28]	Xing Z, Ding W, Zhang S, et al. Machine Learning-Based Differentiation of Nontuberculous Mycobacteria Lung Disease and Pulmonary Tuberculosis Using CT Images. Biomed Res Int, 2020, 2020:6287545. doi:10.1155/2020/6287545.
[29]	Wang L, Ding W, Mo Y, et al. Distinguishing nontuberculous mycobacteria from Mycobacterium tuberculosis lung disease from CT images using a deep learning framework. Eur J Nucl Med Mol Imaging, 2021, 48(13):4293-4306. doi:10.1007/s00259-021-05432-x.
[30]	Lee S, Lee HW, Kim HJ, et al. Deep Learning-Based Prediction Model Using Radiography in Nontuberculous Mycobacterial Pulmonary Disease. Chest, 2022, 162(5):995-1005. doi:10.1016/j.chest.2022.06.018.
[31]	Yoon I, Hong JH, Witanto JN, et al. Mycobacterial cavity on chest computed tomography: clinical implications and deep learning-based automatic detection with quantification. Quant Imaging Med Surg, 2023, 13(2):747-762. doi:10.21037/qims-22-620.

非结核分枝杆菌肺病影像学表现及应用人工智能新技术的研究进展

Research progress on imaging manifestations of nontuberculosis mycobacterial pulmonary and application of new artificial intelligence technology

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献 31

相关文章 15

编辑推荐

Metrics

本文评价

友情链接

[1]	赵艳丽, 车南颖. 《结核病病理学诊断规范》团体标准解读[J]. 中国防痨杂志, 2024, 46(4): 371-374.
[2]	宋瑞雪, 姚明旭, 潘丽萍. 干扰素诱导蛋白10在呼吸道病原微生物感染诊断和疾病监测中的研究进展[J]. 中国防痨杂志, 2024, 46(4): 473-478.
[3]	李多, 吕平欣. 推动人工智能技术在肺结核影像领域的发展和运用[J]. 中国防痨杂志, 2024, 46(3): 253-259.
[4]	秦李祎, 吕平欣, 郭琳, 钱令军, 肖谦, 杨阳, 尚园园, 贾俊楠, 初乃惠, 刘远明, 李卫民. 基于CT图像的肺结核病灶治愈状态判定深度学习模型的建立[J]. 中国防痨杂志, 2024, 46(3): 272-278.
[5]	刘雪艳, 王芳, 李春华, 唐光孝, 郑娇凤, 王惠秋, 李玉蕊, 王佳男, 舒伟强, 吕圣秀. 基于深度学习的继发性肺结核CT辅助诊断模型构建及验证[J]. 中国防痨杂志, 2024, 46(3): 279-287.
[6]	易婉晴, 郑雪怡, 张状, 孙维荣, 袁小东. 基于多期相CT图像的深度学习ResNet18和ResNet50模型诊断肾结核的效能比较[J]. 中国防痨杂志, 2024, 46(3): 288-293.
[7]	姚阳阳, 梁长华, 韩东明, 崔俊伟, 潘犇, 王慧慧, 魏正琦, 甄思雨, 危涵羽. 基于CT影像组学结合临床特征鉴别肺结核与非结核分枝杆菌肺病的研究[J]. 中国防痨杂志, 2024, 46(3): 302-310.
[8]	汪晨媛, 王珊珊, 王赛楠, 邵戈, 曹佳颐, 熊海燕, 胡屹. 脑脊液特征指标辅助结核性脑膜炎快速筛查的效果评估[J]. 中国防痨杂志, 2024, 46(3): 311-317.
[9]	国家呼吸内科医疗质量控制中心, 中华医学会结核病学分会, 中国防痨协会结核病控制专业分会, 中日友好医院. 综合医疗机构肺结核早期发现临床实践指南[J]. 中国防痨杂志, 2024, 46(2): 127-140.
[10]	宋瑞雪, 董静, 姚明旭, 贾红彦, 李自慧, 孙琦, 朱传智, 杜博平, 邢爱英, 潘丽萍. γ-干扰素诱导蛋白-10 mRNA检测试验对不同年龄人群结核病的辅助诊断价值[J]. 中国防痨杂志, 2024, 46(2): 221-227.
[11]	李晓迎, 方玉莲, 宁静, 徐勇胜. GeneXpert MTB/RIF粪便检测在儿童结核病中的研究进展[J]. 中国防痨杂志, 2024, 46(1): 119-123.
[12]	张国钦, 魏文亮, 张志, 张玉华, 钟达, 张帆. 监狱羁押人群中两种结核分枝杆菌潜伏感染检测结果不一致原因及预防性治疗效果分析[J]. 中国防痨杂志, 2024, 46(1): 45-53.
[13]	刘梅, 吴霞, 古旭, 李娜娜, 张万敏, 张小可, 兰远波. InnowaveDX MTB/RIF对结核分枝杆菌复合群及利福平耐药检测性能的评估[J]. 中国防痨杂志, 2024, 46(1): 70-74.
[14]	贝呈, 李蒙, 高谦. 基于血液样本的转录标识物在结核病诊断中的研究进展[J]. 中国防痨杂志, 2023, 45(8): 801-807.
[15]	辛赫男, 高磊. 《世界卫生组织结核病整合指南模块3:诊断——结核感染检测》解读[J]. 中国防痨杂志, 2023, 45(7): 639-643.