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Registration-based lung mechanical analysis of chronic obstructive pulmonary disease (COPD) using a supervised machine learning framework

机译：利用监督机学习框架注册基于慢性阻塞性肺病（COPD）的肺部力学分析

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摘要

Rationale and Objectives: This study evaluated the performance of computed tomography (CT)-derived biomechanical based features of lung function and the presence and severity of chronic obstructive pulmonary disease (COPD). It performed well when compared to CT-derived density and textural features of lung function and the presence and severity of COPD. Materials and Methods: A total of 162 subjects (Global Initiative for Chronic Obstructive Lung Disease [GOLD] stages 0-4 and nonsmokers) subjects with CT scan performed at total lung capacity or expiration to functional residual capacity were evaluated. CT-derived biomechanical, density, and textural feature sets were compared to forced expiratory volume in 1 second (FEV1)%, FEV1/forced vital capacity, and total St. George's respiratory questionnaire scores. The ability of these feature sets to assess the presence and severity of COPD was also evaluated. Optimal features are selected by linear forward feature selection and the classification is done using k nearest neighbor learning algorithm. Results: The proposed biomechanical features showed good correlations with the pulmonary function tests and health status metrics. In COPD versus non-COPD classification, biomechanical feature set achieved an area under the curve (AUC) of 0.85 performing well in comparison to density (AUC = 0.83) and texture (AUC = 0.89) feature sets. Classifying the subjects into the severity of GOLD stage using biomechanical features (AUC = 0.81) performed better than the density- and texture-based feature sets, AUC = 0.76 and 0.73, respectively. The biomechanical features performed better alone than in combination with the other two feature sets. Conclusion: This study shows the effectiveness of CT-derived biomechanical measures in the assessment of airflow obstruction and quality of life in subjects with COPD. CT-derived biomechanical features performed well in assessing the presence and severity of COPD. ? 2013 AUR.
机译：理由和目标：本研究评估了计算断层扫描（CT）的肺功能生物力学特征的性能及慢性阻塞性肺病（COPD）的存在和严重程度。与肺功能的CT衍生的密度和纹理特征相比，它表现良好，以及COPD的存在和严重程度。材料和方法：共评价162项受试者（慢性阻塞性肺病[金]阶段0-4阶段0-4阶段0-4阶段）的受试者在总肺部容量或到期到功能残留能力下进行CT扫描。将CT衍生的生物力学，密度和纹理特征集进行比较，以1秒（FEV1）％，FEV1 /强制生命能力和St. George的呼吸问卷分数。这些特征集评估了评估COPD的存在和严重程度的能力。通过线性前向特征选择选择最佳特征，并使用K最近邻学习算法进行分类。结果：拟议的生物力学特征与肺功能试验和健康状况指标显示出良好的相关性。在COPD与非COPD分类中，生物力学特征在于与密度（AUC = 0.83）和纹理（AUC = 0.89）特征集进行良好的0.85的曲线（AUC）下的区域。使用生物力学特征（AUC = 0.81）将受试者分类为使用生物力学特征（AUC = 0.81）的严重性，而不是基于密度和基于纹理的特征集，AUC = 0.76和0.73。生物力学特征比与其他两个特征集结合更好地执行。结论：本研究表明，CT衍生的生物力学措施在评估对受试者的气流障碍和生活质量方面的有效性。 CT衍生的生物力学特征在评估COPD的存在和严重程度时表现良好。还2013年AUR。

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  • 来源
    《Academic radiology》 |2013年第5期|共10页
  • 作者

    BodduluriS.; NewellJ.D.; HoffmanE.A.; ReinhardtJ.M.;

  • 作者单位

    Department of Biomedical Engineering The University of Iowa Iowa City IA United States;

    Department of Radiology Biomedical Engineering C751GH The University of Iowa Hospitals and;

    Department of Radiology Biomedical Engineering C751GH The University of Iowa Hospitals and;

    Department of Biomedical Engineering The University of Iowa Iowa City IA United States;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 放射医学;
  • 关键词

    CAD; COPD; Lung; Mechanics; Registration;

    机译：CAD;COPD;肺;力学;注册;

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