基于体素的COPD表型研究
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摘要
目的基于体素的定量CT筛选慢性阻塞性肺疾病(chronic obstructive pulmonary disease, COPD)影像学表型。方法连续纳入"数字肺"多中心研究中行双气相扫描的COPD患者,定量测定小气道病变(functional small-airway disease, fSAD)和肺气肿(emphysema, Emph)。采用k-means聚类法分析肺气肿和小气道病变,得到不同表型;采用Fisher判别分析得到分类函数,并进行初始验证和交叉验证。结果共纳入COPD患者50例。在所有患者及肺功能近似相同的慢性阻塞性肺疾病全球倡议(The Global Initiative for Chronic Obstructive Lung Disease, GOLD)2级患者中均得到3种不同表型:肺气肿为主型、小气道病变为主型及混合型,以及3种表型的Fisher线性分类函数;对所得函数进行初始验证和交叉验证,所有COPD患者预测分类结果的正确率分别为92.0%及90.0%,GOLD 2级患者预测分类结果的正确率分别为100.0%及95.2%。结论基于体素的定量CT可以将COPD患者分为肺气肿为主型、小气道病变为主型及混合型。
Objective To screen phenotypes of chronic obstructive pulmonary disease(COPD)using the voxel-based quantitative CT.Methods COPD patients who had received inspiratory and expiratory CT scanning were consecutively recruited from the multi-center study named Digital Lung.Quantitative parametres of functional small-airway disease(fSAD)and emphysema(Emph)were measured.The k-means clustering method was used to analyze the different phenotypes using the paramenters of emphysema and small-airway disease.Fisher discriminatory analysis was used to get the classification function,and then make initial and cross validation.Results Totally 50 COPD patients were recuited.We obtained three phenotypes in the whole cohort and the The Global Initiative for Chronic Obstructive Lung Disease(GOLD)II patients with the similar lung function,namely,emphysema-dominant type,small airway disease-dominant type,and the mixed type.Fisher linear classification function of the three phenotypes were obtained.In the initial verification and cross validation,the correct rate of the whole COPD subjects were 92.0% and 90.0%,the correct rate of GOLD2 grade patients were 100.0% and 95.2%,respectively.Conclusion The voxel-based quantitative CT can be used to divide COPD patients into three imaging phenotypes,namely,emphysema-dominant type,small airway disease-dominant type,and mixed type.
Objective To screen phenotypes of chronic obstructive pulmonary disease(COPD)using the voxel-based quantitative CT.Methods COPD patients who had received inspiratory and expiratory CT scanning were consecutively recruited from the multi-center study named Digital Lung.Quantitative parametres of functional small-airway disease(fSAD)and emphysema(Emph)were measured.The k-means clustering method was used to analyze the different phenotypes using the paramenters of emphysema and small-airway disease.Fisher discriminatory analysis was used to get the classification function,and then make initial and cross validation.Results Totally 50 COPD patients were recuited.We obtained three phenotypes in the whole cohort and the The Global Initiative for Chronic Obstructive Lung Disease(GOLD)II patients with the similar lung function,namely,emphysema-dominant type,small airway disease-dominant type,and the mixed type.Fisher linear classification function of the three phenotypes were obtained.In the initial verification and cross validation,the correct rate of the whole COPD subjects were 92.0% and 90.0%,the correct rate of GOLD2 grade patients were 100.0% and 95.2%,respectively.Conclusion The voxel-based quantitative CT can be used to divide COPD patients into three imaging phenotypes,namely,emphysema-dominant type,small airway disease-dominant type,and mixed type.
引文
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[11] LEE YK, OH YM, LEE JH, et al. Quantitative assessment of emphysema, air trapping, and airway thickening on computed tomography[J]. Lung, 2008, 186(3):157-165.
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[13] 黄宇婷,刘翱. HRCT定量测量与慢性阻塞性肺疾病表型的关系[J]. 中华肺部疾病杂志(电子版), 2015, 8(2): 251-253.
[14] 闫剑锋,冯国活,陈必桂,等. 高分辨率CT诊断早期慢性阻塞性肺疾病的临床研究[J]. 实用医技杂志, 2010, 17(3):208-209.
[15] VESTBO J, HURD SS, AGUSTI AG, et al. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary[J]. Am J Respir Crit Care Med, 2013, 187(4):347-365.
[16] MORTANI BARBOSA EJ JR. Quantitative imaging of chronic obstructive pulmonary disease-moving towards clinical application[J]. J Thorac Dis, 2016, 8(1):1-5.
[17] WEST JB. GOLD Executive Summary[J]. Am J Respir Crit Care Med, 2013, 188(11):1366-1367.
[18] FERNANDES L, FERNANDES Y, MESQUITA AM. Quantitative computed tomography imaging in chronic obstructive pulmonary disease[J]. Lung India, 2016, 33(6):646-652.
[19] MAKITA H, NASUHARA Y, NAGAI K, et al. Characterisation of phenotypes based on severity of emphysema in chronic obstructive pulmonary disease[J]. Thorax, 2007, 62(11):932-937.
[20] DA SILVA SM, PASCHOAL IA, DE CAPITANI EM, et al. COPD phenotypes on computed tomography and its correlation with selected lung function variables in severe patients[J]. Int J Chron Obstruct Pulmon Dis, 2016, 11:503-513.
[21] GALBAN CJ, HAN MK, BOES JL, et al. Computed tomography-based biomarker provides unique signature for diagnosis of COPD phenotypes and disease progression[J]. Nat Med , 2012, 18(11):1711-1715.
[22] BOES JL, HOFF BA, BULE M, et al. Parametric response mapping monitors temporal changes on lung CT scans in the subpopulations and intermediate outcome measures in COPD Study (SPIROMICS)[J]. Acad Radiol, 2015, 22(2):186-194.
[23] LYNCH DA, AL-QAISI MA. Quantitative computed tomography in chronic obstructive pulmonary disease[J]. J Thorac Imaging, 2013, 28(5):284-290.
[24] METS OM, DE JONG PA, VAN GINNEKEN B, et al. Quantitative computed tomography in COPD: Possibilities and limitations [J]. Lung, 2012, 190(2):133-145.
[25] BOES JL, BULE M, HOFF BA, et al. The impact of sources of variability on parametric response mapping of lung CT scans[J]. Tomogr, 2015, 1(1):69-77.
[2] BOES JL, HOFF BA, BULE M, et al. Parametric response mapping monitors temporal changes on lung CT scans in the subpopulations and intermediate outcome measures in COPD Study (SPIROMICS)[J]. Acad Radiol , 2015, 22(2):186-194.
[3] HOLLANDER Z, DEMARCO ML, SADATSAFAVI M, et al. Biomarker development in COPD: Moving from P values to products to impact patient care[J]. Chest, 2017, 151(2):455-467.
[4] SIN DD, HOLLANDER Z, DEMARCO ML, et al. Biomarker development for chronic obstructive pulmonary disease. From discovery to clinical implementation[J]. Am J Respir Crit Care Med, 2015, 192(10):1162-1170.
[5] HAN MK, AGUSTI A, CALVERLEY PM, et al. Chronic obstructive pulmonary disease phenotypes: The future of COPD[J]. Am J Respir Crit Care Med, 2010, 182(5):598-604.
[6] 吴饶仙,况九龙. 慢性阻塞性肺疾病临床表型的研究进展[J]. 解放军医学杂志, 2013, 38(6):519-523.
[7] 金晨望,梁志冉,郭佑民,等. 基于体素的空气潴留定量测量方法的建立及初步临床应用[J]. 中华放射学杂志, 2019, 53(1):22-26.
[8] HOGG JC, CHU F, UTOKAPARCH S, et al. The nature of small-airway obstruction in chronic obstructive pulmonary disease[J]. New Engl J Med, 2004, 350(26):2645-2653.
[9] MATSUOKA S, KURIHARA Y, YAGIHASHI K, et al. Quantitative assessment of air trapping in chronic obstructive pulmonary disease using inspiratory and expiratory volumetric MDCT[J]. AJR Am J Roentgenol, 2008, 190(3):762-769.
[10] YAMASHIRO T, MATSUOKA S, BARTHOLMAI BJ, et al. Collapsibility of lung volume by paired inspiratory and expiratory CT scans: Correlations with lung function and mean lung density[J]. Acad Radiol, 2010, 17(4):489-495.
[11] LEE YK, OH YM, LEE JH, et al. Quantitative assessment of emphysema, air trapping, and airway thickening on computed tomography[J]. Lung, 2008, 186(3):157-165.
[12] ZHONG N, WANG C, YAO W, et al. Prevalence of chronic obstructive pulmonary disease in China: A large, population-based survey[J]. Am J Respir Crit Care Med, 2007, 176(8):753-760.
[13] 黄宇婷,刘翱. HRCT定量测量与慢性阻塞性肺疾病表型的关系[J]. 中华肺部疾病杂志(电子版), 2015, 8(2): 251-253.
[14] 闫剑锋,冯国活,陈必桂,等. 高分辨率CT诊断早期慢性阻塞性肺疾病的临床研究[J]. 实用医技杂志, 2010, 17(3):208-209.
[15] VESTBO J, HURD SS, AGUSTI AG, et al. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary[J]. Am J Respir Crit Care Med, 2013, 187(4):347-365.
[16] MORTANI BARBOSA EJ JR. Quantitative imaging of chronic obstructive pulmonary disease-moving towards clinical application[J]. J Thorac Dis, 2016, 8(1):1-5.
[17] WEST JB. GOLD Executive Summary[J]. Am J Respir Crit Care Med, 2013, 188(11):1366-1367.
[18] FERNANDES L, FERNANDES Y, MESQUITA AM. Quantitative computed tomography imaging in chronic obstructive pulmonary disease[J]. Lung India, 2016, 33(6):646-652.
[19] MAKITA H, NASUHARA Y, NAGAI K, et al. Characterisation of phenotypes based on severity of emphysema in chronic obstructive pulmonary disease[J]. Thorax, 2007, 62(11):932-937.
[20] DA SILVA SM, PASCHOAL IA, DE CAPITANI EM, et al. COPD phenotypes on computed tomography and its correlation with selected lung function variables in severe patients[J]. Int J Chron Obstruct Pulmon Dis, 2016, 11:503-513.
[21] GALBAN CJ, HAN MK, BOES JL, et al. Computed tomography-based biomarker provides unique signature for diagnosis of COPD phenotypes and disease progression[J]. Nat Med , 2012, 18(11):1711-1715.
[22] BOES JL, HOFF BA, BULE M, et al. Parametric response mapping monitors temporal changes on lung CT scans in the subpopulations and intermediate outcome measures in COPD Study (SPIROMICS)[J]. Acad Radiol, 2015, 22(2):186-194.
[23] LYNCH DA, AL-QAISI MA. Quantitative computed tomography in chronic obstructive pulmonary disease[J]. J Thorac Imaging, 2013, 28(5):284-290.
[24] METS OM, DE JONG PA, VAN GINNEKEN B, et al. Quantitative computed tomography in COPD: Possibilities and limitations [J]. Lung, 2012, 190(2):133-145.
[25] BOES JL, BULE M, HOFF BA, et al. The impact of sources of variability on parametric response mapping of lung CT scans[J]. Tomogr, 2015, 1(1):69-77.
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