人工智能在結締組織相關間質性肺疾病的應用及研究進展_Chinese Journal of Respiratory and Critical Care Medicine

Authors：

林莊恒 ,  柴燕玲

昆明醫科大學第二附屬醫院呼吸與危重癥醫學科二病區（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： 林莊恒, 柴燕玲. 人工智能在結締組織相關間質性肺疾病的應用及研究進展. 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, 任賢, 季佳麗, 等. 人工智能在呼吸疾病診治中的應用進展. 中華結核和呼吸雜志, 2021, 44(7): 4.
3.	中國醫師協會風濕免疫科醫師分會風濕病相關肺血管/間質病學組, 國家風濕病數據中心. 2018中國結締組織病相關間質性肺病診斷和治療專家共識. 中華內科雜志, 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, 劉舒怡, 鄭勁平. 胸部影像人工智能在新型冠狀病毒肺炎診斷中的應用及展望. 中華結核和呼吸雜志, 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.	吳玉超, 林嵐, 王婧璇, 等. 基于卷積神經網絡的語義分割在醫學圖像中的應用. 生物醫學工程學雜志, 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, 任賢, 季佳麗, 等. 人工智能在呼吸疾病診治中的應用進展. 中華結核和呼吸雜志, 2021, 44(7): 4.
3. 中國醫師協會風濕免疫科醫師分會風濕病相關肺血管/間質病學組, 國家風濕病數據中心. 2018中國結締組織病相關間質性肺病診斷和治療專家共識. 中華內科雜志, 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, 劉舒怡, 鄭勁平. 胸部影像人工智能在新型冠狀病毒肺炎診斷中的應用及展望. 中華結核和呼吸雜志, 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. 吳玉超, 林嵐, 王婧璇, 等. 基于卷積神經網絡的語義分割在醫學圖像中的應用. 生物醫學工程學雜志, 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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人工智能在結締組織相關間質性肺疾病的應用及研究進展

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