人工智能在结缔组织相关间质性肺疾病的应用及研究进展_Chinese Journal of Respiratory and Critical Care Medicine

Authors：

林庄恒 ,  柴燕玲

昆明医科大学第二附属医院呼吸与危重症医学科二病区（云南昆明 650000）;

Corresponding author：

柴燕玲, Email: chai_yanlin@126.com

Keywords：

DOI：

10.7507/1671-6205.202310045

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Citation： 林庄恒, 柴燕玲. 人工智能在结缔组织相关间质性肺疾病的应用及研究进展. Chinese Journal of Respiratory and Critical Care Medicine, 2024, 23(7): 529-532. doi: 10.7507/1671-6205.202310045 Copy

1.	Gao Q, Yang L, Lu M, et al. The artificial intelligence and machine learning in lung cancer immunotherapy. J Hematol Oncol, 2023, 16(1): 55.
2.	何婷, 任贤, 季佳丽, 等. 人工智能在呼吸疾病诊治中的应用进展. 中华结核和呼吸杂志, 2021, 44(7): 4.
3.	中国医师协会风湿免疫科医师分会风湿病相关肺血管/间质病学组, 国家风湿病数据中心. 2018中国结缔组织病相关间质性肺病诊断和治疗专家共识. 中华内科杂志, 2018, 57(8): 558-565.
4.	Zhang Y, Lian X, Huang S, et al. A study of the diagnostic value of a modified transthoracic lung ultrasound scoring method in interstitial lung disease. Quant Imaging Med Surg, 2023, 13(2): 946-956.
5.	Palmucci S, Galioto F, Fazio G, et al. Clinical and radiological features of lung disorders related to connective-tissue diseases: a pictorial essay. Insights Imaging, 2022, 13(1): 108.
6.	Glenn LM, Pugashetti JV, Oldham J, et al. Interstitial pneumonia with autoimmune features: from research classification to diagnosis. Curr Opin Pulm Med, 2021, 27(5): 374-387.
7.	Xu W, Wu W, Zheng Y, et al. A computed tomography radiomics-based prediction model on interstitial lung disease in anti-MDA5-positive dermatomyositis. Front Med, 2021, 8: 768052.
8.	Zhang S, Yu M, Chen D, et al. Role of MRI-based radiomics in locally advanced rectal cancer (Review). Oncol Rep, 2022, 47(2): 34.
9.	Yasaka K, Akai H, Kunimatsu A, et al. Deep learning with convolutional neural network in radiology. Jpn J Radiol, 2018, 36(4): 257-272.
10.	Guiot J, Vaidyanathan A, Deprez L, et al. A review in radiomics: Making personalized medicine a reality via routine imaging. Med Res Rev, 2022, 42(1): 426-440.
11.	Dianat B, La Torraca P, Manfredi A, et al. Classification of pulmonary sounds through deep learning for the diagnosis of interstitial lung diseases secondary to connective tissue diseases. Comput Biol Med, 2023, 160: 106928.
12.	Ohno Y, Aoyagi K, Takenaka D, et al. Machine learning for lung CT texture analysis: Improvement of inter-observer agreement for radiological finding classification in patients with pulmonary diseases. Eur J Radiol, 2021, 134: 109410.
13.	Le Gall A, Hoang-Thi TN, Porcher R, et al. Prognostic value of automated assessment of interstitial lung disease on CT in systemic sclerosis. Rheumatology (Oxford), 2024, 63(1): 103-110.
14.	Verschakelen J A. Lung shrinkage: an additional CT marker in the follow-up of fibrotic interstitial lung disease. Radiology, 2021, 298(1): 199-200.
15.	Feng DY, Zhou YQ, Xing YF, et al. Selection of glucocorticoid-sensitive patients in interstitial lung disease secondary to connective tissue diseases population by radiomics. Ther Clin Risk Manag, 2018, 14: 1975-1986.
16.	Li Y, Zhou Y, Wang Q. Multiple values of (18)F-FDG PET/CT in idiopathic inflammatory myopathy. Clin Rheumatol, 2017, 36(10): 2297-2305.
17.	Ley B, Ryerson CJ, Vittinghoff E, et al. A multidimensional index and staging system for idiopathic pulmonary fibrosis. Ann Intern Med, 2012, 156(10): 684-691.
18.	Jiang X, Su N, Quan S, et al. Computed tomography radiomics-based prediction model for gender-age-physiology staging of connective tissue disease-associated interstitial lung disease. Acad Radiol, 2023, S1076-6332(23): 00054-5.
19.	Martini K, Baessler B, Bogowicz M, et al. Applicability of radiomics in interstitial lung disease associated with systemic sclerosis: proof of concept. Eur Radiol, 2021, 31(4): 1987-1998.
20.	Schniering J, Maciukiewicz M, Gabrys H S, et al. Computed tomography-based radiomics decodes prognostic and molecular differences in interstitial lung disease related to systemic sclerosis. Eur Respir J, 2022, 59(5).
21.	李允, 刘舒怡, 郑劲平. 胸部影像人工智能在新型冠状病毒肺炎诊断中的应用及展望. 中华结核和呼吸杂志, 2022, 45(12): 1255-1260.
22.	Lambin P, Leijenaar R TH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nature reviews. Clin Oncol, 2017, 14(12): 749-762.
23.	Spadarella G, Stanzione A, Akinci D’Antonoli T, et al. Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative. Eur Radiol, 2023, 33(3): 1884-1894.
24.	Akinci D’Antonoli T, Cuocolo R, Baessler B, et al. Towards reproducible radiomics research: introduction of a database for radiomics studies. Eur Radiol, 2023, 34(1): 436-443.
25.	吴玉超, 林岚, 王婧璇, 等. 基于卷积神经网络的语义分割在医学图像中的应用. 生物医学工程学杂志, 2020, 37(3): 533-540.
26.	Chassagnon G, Vakalopoulou M, Régent A, et al. Elastic registration-driven deep learning for longitudinal assessment of systemic sclerosis interstitial lung disease at CT. Radiology, 2021, 298(1): 189-198.
27.	Chassagnon G, Martin C, Marini R, et al. Use of elastic registration in pulmonary MRI for the assessment of pulmonary fibrosis in patients with systemic sclerosis. Radiology, 2019, 291(2): 487-492.
28.	Bruni C, Occhipinti M, Pienn M, et al. Lung vascular changes as biomarkers of severity in systemic sclerosis-associated interstitial lung disease. Rheumatology (Oxford), 2023, 62(2): 696-706.
29.	Qu H, Zhai H, Zhang S, et al. Dynamic radiomics for predicting the efficacy of antiangiogenic therapy in colorectal liver metastases. Front Oncol, 2023, 13: 992096.
30.	Zhang S, Wang Y, Zheng Q, et al. Artificial intelligence in melanoma: a systematic review. J Cosmet Dermatol, 2022, 21(11): 5993-6004.

1. Gao Q, Yang L, Lu M, et al. The artificial intelligence and machine learning in lung cancer immunotherapy. J Hematol Oncol, 2023, 16(1): 55.
2. 何婷, 任贤, 季佳丽, 等. 人工智能在呼吸疾病诊治中的应用进展. 中华结核和呼吸杂志, 2021, 44(7): 4.
3. 中国医师协会风湿免疫科医师分会风湿病相关肺血管/间质病学组, 国家风湿病数据中心. 2018中国结缔组织病相关间质性肺病诊断和治疗专家共识. 中华内科杂志, 2018, 57(8): 558-565.
4. Zhang Y, Lian X, Huang S, et al. A study of the diagnostic value of a modified transthoracic lung ultrasound scoring method in interstitial lung disease. Quant Imaging Med Surg, 2023, 13(2): 946-956.
5. Palmucci S, Galioto F, Fazio G, et al. Clinical and radiological features of lung disorders related to connective-tissue diseases: a pictorial essay. Insights Imaging, 2022, 13(1): 108.
6. Glenn LM, Pugashetti JV, Oldham J, et al. Interstitial pneumonia with autoimmune features: from research classification to diagnosis. Curr Opin Pulm Med, 2021, 27(5): 374-387.
7. Xu W, Wu W, Zheng Y, et al. A computed tomography radiomics-based prediction model on interstitial lung disease in anti-MDA5-positive dermatomyositis. Front Med, 2021, 8: 768052.
8. Zhang S, Yu M, Chen D, et al. Role of MRI-based radiomics in locally advanced rectal cancer (Review). Oncol Rep, 2022, 47(2): 34.
9. Yasaka K, Akai H, Kunimatsu A, et al. Deep learning with convolutional neural network in radiology. Jpn J Radiol, 2018, 36(4): 257-272.
10. Guiot J, Vaidyanathan A, Deprez L, et al. A review in radiomics: Making personalized medicine a reality via routine imaging. Med Res Rev, 2022, 42(1): 426-440.
11. Dianat B, La Torraca P, Manfredi A, et al. Classification of pulmonary sounds through deep learning for the diagnosis of interstitial lung diseases secondary to connective tissue diseases. Comput Biol Med, 2023, 160: 106928.
12. Ohno Y, Aoyagi K, Takenaka D, et al. Machine learning for lung CT texture analysis: Improvement of inter-observer agreement for radiological finding classification in patients with pulmonary diseases. Eur J Radiol, 2021, 134: 109410.
13. Le Gall A, Hoang-Thi TN, Porcher R, et al. Prognostic value of automated assessment of interstitial lung disease on CT in systemic sclerosis. Rheumatology (Oxford), 2024, 63(1): 103-110.
14. Verschakelen J A. Lung shrinkage: an additional CT marker in the follow-up of fibrotic interstitial lung disease. Radiology, 2021, 298(1): 199-200.
15. Feng DY, Zhou YQ, Xing YF, et al. Selection of glucocorticoid-sensitive patients in interstitial lung disease secondary to connective tissue diseases population by radiomics. Ther Clin Risk Manag, 2018, 14: 1975-1986.
16. Li Y, Zhou Y, Wang Q. Multiple values of (18)F-FDG PET/CT in idiopathic inflammatory myopathy. Clin Rheumatol, 2017, 36(10): 2297-2305.
17. Ley B, Ryerson CJ, Vittinghoff E, et al. A multidimensional index and staging system for idiopathic pulmonary fibrosis. Ann Intern Med, 2012, 156(10): 684-691.
18. Jiang X, Su N, Quan S, et al. Computed tomography radiomics-based prediction model for gender-age-physiology staging of connective tissue disease-associated interstitial lung disease. Acad Radiol, 2023, S1076-6332(23): 00054-5.
19. Martini K, Baessler B, Bogowicz M, et al. Applicability of radiomics in interstitial lung disease associated with systemic sclerosis: proof of concept. Eur Radiol, 2021, 31(4): 1987-1998.
20. Schniering J, Maciukiewicz M, Gabrys H S, et al. Computed tomography-based radiomics decodes prognostic and molecular differences in interstitial lung disease related to systemic sclerosis. Eur Respir J, 2022, 59(5).
21. 李允, 刘舒怡, 郑劲平. 胸部影像人工智能在新型冠状病毒肺炎诊断中的应用及展望. 中华结核和呼吸杂志, 2022, 45(12): 1255-1260.
22. Lambin P, Leijenaar R TH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nature reviews. Clin Oncol, 2017, 14(12): 749-762.
23. Spadarella G, Stanzione A, Akinci D’Antonoli T, et al. Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative. Eur Radiol, 2023, 33(3): 1884-1894.
24. Akinci D’Antonoli T, Cuocolo R, Baessler B, et al. Towards reproducible radiomics research: introduction of a database for radiomics studies. Eur Radiol, 2023, 34(1): 436-443.
25. 吴玉超, 林岚, 王婧璇, 等. 基于卷积神经网络的语义分割在医学图像中的应用. 生物医学工程学杂志, 2020, 37(3): 533-540.
26. Chassagnon G, Vakalopoulou M, Régent A, et al. Elastic registration-driven deep learning for longitudinal assessment of systemic sclerosis interstitial lung disease at CT. Radiology, 2021, 298(1): 189-198.
27. Chassagnon G, Martin C, Marini R, et al. Use of elastic registration in pulmonary MRI for the assessment of pulmonary fibrosis in patients with systemic sclerosis. Radiology, 2019, 291(2): 487-492.
28. Bruni C, Occhipinti M, Pienn M, et al. Lung vascular changes as biomarkers of severity in systemic sclerosis-associated interstitial lung disease. Rheumatology (Oxford), 2023, 62(2): 696-706.
29. Qu H, Zhai H, Zhang S, et al. Dynamic radiomics for predicting the efficacy of antiangiogenic therapy in colorectal liver metastases. Front Oncol, 2023, 13: 992096.
30. Zhang S, Wang Y, Zheng Q, et al. Artificial intelligence in melanoma: a systematic review. J Cosmet Dermatol, 2022, 21(11): 5993-6004.

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人工智能在结缔组织相关间质性肺疾病的应用及研究进展

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