Chen Kun, Hoffman Eric A, Seetharaman Indu, Jiao Feiran, Lin Ching-Long, Chan Kung-Sik
University of Connecticut.
University of Iowa.
Ann Appl Stat. 2016 Dec;10(4):1880-1906. doi: 10.1214/16-AOAS947. Epub 2017 Jan 5.
The human lung airway is a complex inverted tree-like structure. Detailed airway measurements can be extracted from MDCT-scanned lung images, such as segmental wall thickness, airway diameter, parent-child branch angles, etc. The wealth of lung airway data provides a unique opportunity for advancing our understanding of the fundamental structure-function relationships within the lung. An important problem is to construct and identify important lung airway features in normal subjects and connect these to standardized pulmonary function test results such as FEV1%. Among other things, the problem is complicated by the fact that a particular airway feature may be an important (relevant) predictor only when it pertains to segments of certain generations. Thus, the key is an efficient, consistent method for simultaneously conducting group selection (lung airway feature types) and within-group variable selection (airway generations), i.e., bi-level selection. Here we streamline a comprehensive procedure to process the lung airway data via imputation, normalization, transformation and groupwise principal component analysis, and then adopt a new composite penalized regression approach for conducting bi-level feature selection. As a prototype of composite penalization, the proposed composite bridge regression method is shown to admit an efficient algorithm, enjoy bi-level oracle properties, and outperform several existing methods. We analyze the MDCT lung image data from a cohort of 132 subjects with normal lung function. Our results show that, lung function in terms of FEV1% is promoted by having a less dense and more homogeneous lung comprising an airway whose segments enjoy more heterogeneity in wall thicknesses, larger mean diameters, lumen areas and branch angles. These data hold the potential of defining more accurately the "normal" subject population with borderline atypical lung functions that are clearly influenced by many genetic and environmental factors.
人类肺气道是一种复杂的倒置树状结构。可以从MDCT扫描的肺部图像中提取详细的气道测量数据，如节段壁厚度、气道直径、父子分支角度等。丰富的肺气道数据为增进我们对肺内基本结构-功能关系的理解提供了独特的机会。一个重要的问题是构建并识别正常受试者的重要肺气道特征，并将这些特征与标准化肺功能测试结果（如FEV1%）联系起来。除此之外，该问题因以下事实而变得复杂：特定的气道特征可能仅在涉及某些代的节段时才是重要的（相关的）预测指标。因此，关键是要有一种高效、一致的方法来同时进行组选择（肺气道特征类型）和组内变量选择（气道代），即双层选择。在这里，我们简化了一个综合程序，通过插补、归一化、变换和分组主成分分析来处理肺气道数据，然后采用一种新的复合惩罚回归方法进行双层特征选择。作为复合惩罚的一个原型，所提出的复合桥回归方法被证明允许一种高效算法，具有双层神谕性质，并且优于几种现有方法。我们分析了来自132名肺功能正常受试者队列的MDCT肺部图像数据。我们的结果表明，FEV1%所代表的肺功能通过拥有一个密度较低且更均匀的肺而得到提升，该肺包含一个气道，其节段在壁厚、平均直径、管腔面积和分支角度方面具有更多的异质性。这些数据有可能更准确地定义具有临界非典型肺功能的“正常”受试者群体，这些肺功能明显受到许多遗传和环境因素的影响。
