Department of Biomedical Engineering, The University of Iowa, Iowa City, IA, USA.
Acad Radiol. 2013 May;20(5):527-36. doi: 10.1016/j.acra.2013.01.019.
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.
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.
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.
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.
本研究评估了基于 CT 衍生的生物力学特征对肺功能及慢性阻塞性肺疾病（COPD）的存在和严重程度的预测能力。与 CT 衍生的密度和纹理特征及肺功能和 COPD 的存在和严重程度相比，这些生物力学特征的表现更好。
共纳入 162 名（GOLD 分期 0-4 期和不吸烟）在肺总量或呼气末进行 CT 扫描的患者。比较 CT 衍生的生物力学、密度和纹理特征与 1 秒用力呼气量（FEV1）%、FEV1/用力肺活量（FVC）和圣乔治呼吸问卷总评分。还评估了这些特征集评估 COPD 存在和严重程度的能力。采用线性前向特征选择选择最优特征，并使用 k 近邻学习算法进行分类。
所提出的生物力学特征与肺功能测试和健康状况指标具有良好的相关性。在 COPD 与非 COPD 分类中，生物力学特征集的曲线下面积（AUC）为 0.85，与密度（AUC=0.83）和纹理（AUC=0.89）特征集相比表现良好。使用生物力学特征将患者分为 GOLD 分期严重程度（AUC=0.81）的效果优于密度和纹理特征集，AUC 分别为 0.76 和 0.73。生物力学特征单独使用的效果优于与其他两个特征集联合使用。
本研究表明 CT 衍生的生物力学测量在评估 COPD 患者气流阻塞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 的存在和严重程度方面表现良好。
