ISSN 1004-4140
CN 11-3017/P
GAO H, LIU Y B, XIAO M, et al. The Correlation between Computed Tomography Severity Grade and Pulmonary Function in Interstitial Lung Disease[J]. CT Theory and Applications, 2025, 34(4): 598-603. DOI: 10.15953/j.ctta.2024.305.
Citation: GAO H, LIU Y B, XIAO M, et al. The Correlation between Computed Tomography Severity Grade and Pulmonary Function in Interstitial Lung Disease[J]. CT Theory and Applications, 2025, 34(4): 598-603. DOI: 10.15953/j.ctta.2024.305.

The Correlation between Computed Tomography Severity Grade and Pulmonary Function in Interstitial Lung Disease

More Information
  • Author Bio:

    GAO Hui: E-mail: dernini@163.com

  • Corresponding author:

    LIU Yongbin✉, E-mail: liuyongbinking@163.com.

  • Received Date: December 13, 2024
  • Revised Date: March 27, 2025
  • Accepted Date: April 17, 2025
  • Available Online: May 18, 2025
  • Objective: To assess whether there is a correlation between CT scan severity score and pulmonary function in Interstitial Lung Disease, and provide a more reliable basis for clinical diagnosis and treatment. Materials and Methods: Sixty patients with clinical diagnosis of Interstitial Lung Disease (ILD) were collected, and chest CT and pulmonary function tests were performed at the same time. Here DLCO% and FEV1% were used as the pulmonary function indexes. The severity of the patients was assessed by CT scores as mild (range, 1~10), moderate (range 11~20), and severe (range 21~30). Correlation analysis was carried out between CT score and pulmonary function index, and the lung function parameters of patients at three grades were compared between groups and pairwise among the means. Results: As per the CT severity classification, there were 13 mild cases, 31 moderate cases, and 16 severe cases. CT score was negatively correlated with DLCO% and FEV1%. R value was −0.814 and −0.797, respectively; The comparison of the mean value of DLCO% and FEV1% among the three groups and the pairings of the mean value of DLCO% and FEV1% were statistically significant. Conclusion: There was good correlation between CT score of ILD and the pulmonary function index. The pulmonary function index of patients with different CT severity grading was statistically significant, which can provide a new basis for the clinical evaluation and diagnosis of the disease.

  • [1]
    BOCCHINO M, BRUZZESE D, D’ALTO M, et al. Performance of a new quantitative computed tomography index for interstitial lung disease assessment in systemic sclerosis[J]. Scientific Reports, 2019, 9(1): 9468. DOI: 10.1038/s41598-019-45990-7.
    [2]
    JACOB J, BARTHOLMAR B J, RAJAGOPALAN S, et al. Evaluation of computer-based computer tomography stratification against outcome models in connective tissue disease-related interstitial lung disease: A patient outcome study[J]. BMC Medicine, 2016, 14(1): 190. DOI: 10.1186/s12916-016-0739-7.
    [3]
    SVERZELLATI N, CALABRÒ E, CHETTA A, et al. Visual score and quantitative CT indices in pulmonary fibrosis: Relationship with physiologic impairment[J]. Radiologia Medica, 2007, 112(8): 1160-1172. DOI: 10.1007/s11547-007-0213-x.
    [4]
    CAMICIOTTOLI G, ORLANDI I, BARTOLUCCI M, et al. Lung CT densitometry in systemic sclerosis: Correlation with lung function, exercise testing, and quality of life[J]. Chest, 2007, 131(3): 672-681. DOI: 10.1378/chest.06-1401.
    [5]
    FENG Y J. Levels of blood biochemical indices and CT image features in patients with rheumatoid arthritis associated interstitial lung disease[J]. Chinese Journal of CT and MRI, 2020, 18(02): 41-43. DOI: 10.3969/j.issn.1672-5131.2020.02.013.
    [6]
    KIM H G, TASHKIN D P, CLEMENTS P J, et al. A computer-aided diagnosis system for quantitative scoring of extent of lung fibrosis in scleroderma patients[J]. Clinical and Experimental Rheumatology, 2010, (5 Suppl 62): S26-35. DOI10.1093.
    [7]
    BIEDERER J, SCHNABEL A, MUHLE C, et al. Correlation between HRCT findings, pulmonary function tests and bronchoalveolar lavage cytology in interstitial lung disease associated with rheumatoid arthritis[J]. European Radiology, 2004, 14(2): 272-280. DOI: 10.1007/s00330-003-2026-1.
    [8]
    TASHKIN D P, VOLKMANN E R, TSENG C H, et al. Relationship between quantitative radiographic assessments of interstitial lung disease and physiological and clinical features of systemic sclerosis[J]. Annals of the Rheumatic Diseases, 2016, 75(2): 374-381. DOI: 10.1136/annrheumdis-2014-206076.
    [9]
    FORESTIER A, GOUELLEC N, BÉHAL H, et al. Evolution of high-resolution CT-scan in systemic sclerosis-associated interstitial lung disease: Description and prognosis factors[J]. Seminars in Arthritis and Rheumatism, 2020, S0049-0172(20): 30068-8. DOI: 10.1016/j.semarthrit.2020.02.015.
    [10]
    MARTINEZ C H, CHEN Y H, WESTGATE P M, et al. Relationship between quantitative CT metrics and health status and BODE in chronic obstructive pulmonary disease[J]. Thorax, 2012, 67(5): 399-406. DOI: 10.1136/thoraxjnl-2011-201185.
    [11]
    YABUUCHI H, MATSUO Y, TSUKAMOTO H, et al. Evaluation of the extent of ground-glass opacity on high-resolution CT in patients with interstitial pneumonia associated with systemic sclerosis: Comparison between quantitative and qualitative analysis[J]. Clinical Radiology, 2014, 69(7): 758-764. DOI: 10.1016/j.crad.2014.03.008.
    [12]
    ARIANI A, SILVA M, SELETTI V, et al. Quantitative chest computed tomography is associated with two prediction models of mortality in interstitial lung disease related to systemic sclerosis[J]. Cil Journal of the British Society for Rheumatology (Oxford), 2017, 56(6): 922-927. DOI: 10.1093/rheumatology/kew480.
    [13]
    UFUK F, DEMIRCI M, ALTINISIK G. Quantitative computed tomography assessment for systemic sclerosis-related interstitial lung disease: Comparison of different methods[J]. European Radiology, 2020, 10.1007/s00330-020-06772-2.
    [14]
    ZHANG C Q, LIU B L, SHU S J, et al. Study of 64-slice spiral CT in pulmonary function and application of HRCT in interstitial lung disease[J]. Journal of Clinical Pulmonary Medicine, 2012, 17(3): 394-396. DOI: 10.3969/j.issn.1009-6663.2012.03.003.
    [15]
    ROBBIE H, WELLS A U, FANG C, et al. Serial decline in lung volume parameters on computed tomography (CT) predicts outcome in idiopathic pulmonary fibrosis (IPF)[J]. European Radiology, 2022, 32(4): 2650-2660. DOI: 10.1007/s00330-021-08338-2.
    [16]
    PAN J, HOFMANNINGER J, NENNING K H, et al. Unsupervised machine learning identifies predictive progression markers of IPF[J]. European Radiology, 2023, 33(2): 925-935. DOI: 10.1007/s00330-022-09101-x.
  • Related Articles

    [1]GAO Yan, RONG Dongdong, AN Yanhong, DAN Yi, CHEN Xin, TIAN Geng, LU Jie. X-ray and CT Features of Novel Coronavirus Pneumonia[J]. CT Theory and Applications, 2020, 29(2): 147-154. DOI: 10.15953/j.1004-4140.2020.29.02.04
    [2]HUA Chenchen, LIU Yao, YIN Qihua. The Study of Accurate Liver Volume Measurement by IQQA-liver Auto Analysis Software[J]. CT Theory and Applications, 2019, 28(2): 229-235. DOI: 10.15953/j.1004-4140.2019.28.02.09
    [3]HAO Jia, ZHANG Li, CHEN Zhi-qiang, XING Yu-xiang, KANG Ke-jun. Multi-energy X-ray Imaging Technique and Its Application in Computed Tomography[J]. CT Theory and Applications, 2011, 20(1): 141-150.
    [4]LI Qing-liang, YAN Bin, SUN Hong-sheng, LI Lei, ZHANG Feng. Review of Metal Artifacts Correction Methods on X-ray Computed Tomography[J]. CT Theory and Applications, 2011, 20(1): 131-140.
    [5]ZHANG Feng, YAN Bin, LI Jian-xin, LI Lei, BAO Shang-lian. Review of Scatter Correction on X-Ray Industrial Computed Tomography[J]. CT Theory and Applications, 2009, 18(4): 34-43.
    [6]LI Cheng-quan, LI Zheng, LI Chen, YU Ai-min. Investigation of Image Reconstruction Algorithms for In-line X-ray Phase Contrast Tomography[J]. CT Theory and Applications, 2007, 16(2): 1-7.
    [7]SONG Fu-jing, TONG Jing, JI Zhi-min, ZHANG Bo, JIANG Guo-lin, ZHANG Guang-ping. To Analysis The Chest X-ray Character of SARS[J]. CT Theory and Applications, 2004, 13(2): 33-36.
    [8]WANG Xue-li. Spatial Resolution and Fourier Analysis for X-ray Medical Device[J]. CT Theory and Applications, 2003, 12(4): 36-41.
    [9]HUANG Shao-quan, TAN Jiang-cheng, CHEN Shu-ke, LONG Hai-dan. X-ray and CT study of esophageal cancer[J]. CT Theory and Applications, 2003, 12(3): 31-33.
    [10]Li Yubin, Li Xiangliang, Zhang Kuixiang, Cao Xulong. X-Ray Computed Micro-tomography(CMT) and Its Application to Petroleum Research[J]. CT Theory and Applications, 2000, 9(3): 35-40.

Catalog

    Article views (62) PDF downloads (14) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return