MR Image Analytics to Characterize the Upper Airway Structure in Obese Children with Obstructive Sleep Apnea Syndrome.
MR Image Analytics to Characterize the Upper Airway Structure in Obese Children with Obstructive Sleep Apnea Syndrome.
复制标题
DOI：
10.1371/journal.pone.0159327
复制
发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Arens R
中科院分区：
文献类型：
--
作者：
Tong Y;Udupa JK;Sin S;Liu Z;Wileyto EP;Torigian DA;Arens R
在阻塞性睡眠呼吸暂停综合征（OSAS）的早期研究中，定量图像分析主要集中在上气道或其附近的几个物体以及物体大小的测量。在本文中，我们采取了一个更一般的方法，考虑所有主要的对象在上气道区域和措施有关的个人形态学特性，其组织特征显示的图像强度，和3D架构的对象组件。我们提出了一种新的方法来选择一个小集合的显着特征，从这个大集合的措施，并证明了这些功能的能力，区分肥胖OSAS和肥胖非OSAS组之间的预测精度非常高。30名儿童参与了这项研究，其中15名为肥胖OSAS组，呼吸暂停低通气指数（AHI）= 14.4 ± 10.7），15名为肥胖非OSAS组，AHI = 1.0 ± 1.0（p <0.001）。受试者年龄在8 - 17岁之间，在清醒状态下接受了上呼吸道T1和T2加权磁共振成像（MRI）。在这些图像中分割上气道附近的14个对象，并且从每个受试者图像中获得总共159个测量值，包括对象大小、表面积、体积、球形度、标准化T2加权图像强度值和对象间距离。一个小的一组区别性的功能，确定从这个集合中的几个步骤。首先，确定在测量之间具有低水平相关性的测量的子集。为此，使用了一种热图可视化技术，该技术允许根据参数之间的相关性对参数进行分组。然后，通过T检验，确定了能够区分这两组的另一个子集的措施。这些子集的交集产生了最终的特征集。通过使用逻辑回归和多重交叉验证来测试这些特征将看不见的图像分类到两个患者组中的准确性。以低特征间相关性（<0.36）识别的一组16个特征产生了96%的高分类准确性，灵敏度和特异性分别为97.8%和94.4%。除了之前观察到的OSAS中腺样体、扁桃体和脂肪垫的线性尺寸、表面积和体积增加外，还发现了以下新的标志物：两组之间整个颈体区域、咽部和鼻咽部的标准化T2加权图像强度不同，可能表明对象组织特征的变化。OSAS患者的脂肪垫和口咽部变圆或变复杂。在阻塞性睡眠呼吸暂停综合征中，脂肪垫和舌头靠近，口咽和扁桃体、脂肪垫和扁桃体也是如此。相比之下，脂肪垫和口咽远离皮肤对象移动。本研究发现了几种新的OSAS的解剖学生物标志物。对象中标准化T2加权图像强度的变化可能意味着OSAS中内在组织成分发生变化。物体间距离的结果意味着处理方法应该尊重物体之间存在的关系，而不仅仅是它们的大小。所提出的分析方法可能会导致更好地了解阻塞性睡眠呼吸暂停综合征的机制。
Quantitative image analysis in previous research in obstructive sleep apnea syndrome (OSAS) has focused on the upper airway or several objects in its immediate vicinity and measures of object size. In this paper, we take a more general approach of considering all major objects in the upper airway region and measures pertaining to their individual morphological properties, their tissue characteristics revealed by image intensities, and the 3D architecture of the object assembly. We propose a novel methodology to select a small set of salient features from this large collection of measures and demonstrate the ability of these features to discriminate with very high prediction accuracy between obese OSAS and obese non-OSAS groups. Thirty children were involved in this study with 15 in the obese OSAS group with an apnea-hypopnea index (AHI) = 14.4 ± 10.7) and 15 in the obese non-OSAS group with an AHI = 1.0 ± 1.0 (p<0.001). Subjects were between 8–17 years and underwent T1- and T2-weighted magnetic resonance imaging (MRI) of the upper airway during wakefulness. Fourteen objects in the vicinity of the upper airways were segmented in these images and a total of 159 measurements were derived from each subject image which included object size, surface area, volume, sphericity, standardized T2-weighted image intensity value, and inter-object distances. A small set of discriminating features was identified from this set in several steps. First, a subset of measures that have a low level of correlation among the measures was determined. A heat map visualization technique that allows grouping of parameters based on correlations among them was used for this purpose. Then, through T-tests, another subset of measures which are capable of separating the two groups was identified. The intersection of these subsets yielded the final feature set. The accuracy of these features to perform classification of unseen images into the two patient groups was tested by using logistic regression and multi-fold cross validation. A set of 16 features identified with low inter-feature correlation (< 0.36) yielded a high classification accuracy of 96% with sensitivity and specificity of 97.8% and 94.4%, respectively. In addition to the previously observed increase in linear size, surface area, and volume of adenoid, tonsils, and fat pad in OSAS, the following new markers have been found. Standardized T2-weighted image intensities differed between the two groups for the entire neck body region, pharynx, and nasopharynx, possibly indicating changes in object tissue characteristics. Fat pad and oropharynx become less round or more complex in shape in OSAS. Fat pad and tongue move closer in OSAS, and so also oropharynx and tonsils and fat pad and tonsils. In contrast, fat pad and oropharynx move farther apart from the skin object. The study has found several new anatomic bio-markers of OSAS. Changes in standardized T2-weighted image intensities in objects may imply that intrinsic tissue composition undergoes changes in OSAS. The results on inter-object distances imply that treatment methods should respect the relationships that exist among objects and not just their size. The proposed method of analysis may lead to an improved understanding of the mechanisms underlying OSAS.