Quantitative computed tomographic imaging-based clustering differentiates asthmatic subgroups with distinctive clinical phenotypes.
Quantitative computed tomographic imaging-based clustering differentiates asthmatic subgroups with distinctive clinical phenotypes.
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DOI：
10.1016/j.jaci.2016.11.053
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发表时间：
2017-09
期刊：
影响因子：
--
通讯作者：
National Heart, Lung and Blood Institute's Severe Asthma Research Program
中科院分区：
文献类型：
--
作者：
Choi S;Hoffman EA;Wenzel SE;Castro M;Fain S;Jarjour N;Schiebler ML;Chen K;Lin CL;National Heart, Lung and Blood Institute's Severe Asthma Research Program
人们发现，包括气道直径、壁厚度和空气滞留在内的成像变量是区分严重哮喘患者与非严重哮喘患者和健康受试者的重要指标。本研究的目的是识别基于成像的聚类并探索聚类与现有临床指标的关联。我们使用从 248 名哮喘患者各自的吸气和呼气扫描中提取的基于定量计算机断层扫描的结构和功能变量，进行了基于成像的聚类分析。基于成像的指标包括更广泛的多尺度变量，例如吸气气道尺寸、呼气滞留空气和基于记录的肺变形（吸气与呼气）。通过聚类方法得出的哮喘亚组与受试者人口统计、调查问卷、用药史和生物标志物变量相关。第1组患者是早发的年轻非严重哮喘患者，伴有可逆性气流阻塞，气道结构正常；第 2 组患者混合了具有边缘炎症的非严重和严重哮喘患者，他们表现出气道管腔狭窄，但没有壁增厚。第 3 组和第 4 组患者以严重哮喘患者为主。第 3 组患者是患有可逆性气流阻塞的肥胖女性，她们表现出气道壁增厚，但没有气道狭窄。第 4 组患者是晚发老年男性，伴有持续气流阻塞，表现出明显的空气滞留和区域变形减少。第 3 组和第 4 组患者还分别表现出淋巴细胞减少和中性粒细胞增加。确定了四个基于图像的聚类，并显示它们与临床特征相关。这种聚类有助于区分哮喘亚组，这可以用作开发新疗法的基础。我们使用由影像变量组成的聚类分析确定了四个哮喘亚组，这些变量与临床指标相关。识别基于成像的集群可以实现基于集群的实用治疗干预。
Imaging variables including airway diameter, wall thickness and air-trapping have been found to be important metrics when differentiating severe asthmatics from nonsevere asthmatics and healthy subjects. The objective of this study was to identify imaging-based clusters and to explore the association of the clusters with existing clinical metrics. We performed an imaging-based cluster analysis using quantitative computed tomography-based structural and functional variables extracted from the respective inspiration and expiration scans of 248 asthmatics. The imaging-based metrics included a broader-set of multiscale variables such as inspiratory airway dimension, expiratory air-trapping and registration-based lung deformation (inspiration vs. expiration). Asthma subgroups derived from a clustering method were associated with subject demography, questionnaire, medication history, and biomarker variables. Cluster 1 patients were early-onset younger nonsevere asthmatics with reversible airflow obstruction, who showed normal airway structure; Cluster 2 patients were a mix of nonsevere and severe asthmatics with marginal inflammation, who exhibited airway luminal narrowing without wall thickening. Cluster 3 and 4 patients were dominated by severe asthmatics. Cluster 3 patients were obese females with reversible airflow obstruction who exhibited airway wall thickening without airway narrowing. Cluster 4 patients were late-onset older males with persistent airflow obstruction, exhibiting significant air-trapping and reduced regional deformation. Clusters 3 and 4 patients also showed decreased lymphocyte and increased neutrophils, respectively. Four image-based clusters were identified and shown to be correlated with clinical characteristics. Such clustering serves to differentiate asthma subgroups which may be used as a basis for the development of new therapies. We identified four asthma subgroups using a cluster analysis composed of imaging variables, which were associated with clinical metrics. Identifying imaging-based clusters could enable practical cluster-based therapeutic interventions.
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影响因子：
3.3
作者：
Choi, Sanghun;Hoffman, Eric A.;Lin, Ching-Long
通讯作者：
Lin, Ching-Long
影响因子：
24.3
作者：
Chung, Kian Fan;Wenzel, Sally E.;Teague, W. Gerald
通讯作者：
Teague, W. Gerald
影响因子：
9.6
作者：
Busacker A;Newell JD Jr;Keefe T;Hoffman EA;Granroth JC;Castro M;Fain S;Wenzel S
通讯作者：
Wenzel S
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y
DOI：
10.1164/ajrccm/147.2.405
发表时间：
1993-02-01
期刊：
AMERICAN REVIEW OF RESPIRATORY DISEASE
影响因子：
--
作者：
CARROLL, N;ELLIOT, J;JAMES, A
通讯作者：
JAMES, A