School of Biomedical Engineering, Capital Medical University, Beijing, China.
Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China.
J Alzheimers Dis. 2020;73(3):991-1002. doi: 10.3233/JAD-190715.
Mild cognitive impairment (MCI) exhibits a high risk of progression to Alzheimer's disease (AD), and it is commonly deemed as the precursor of AD. It is important to find effective and robust ways for the early diagnosis of MCI. In this paper, a random forest-based method combining multiple morphological metrics was proposed to identify MCI from normal controls (NC). Voxel-based morphometry, deformation-based morphometry, and surface-based morphometry were utilized to extract morphological metrics such as gray matter volume, Jacobian determinant value, cortical thickness, gyrification index, sulcus depth, and fractal dimension. An initial discovery dataset (56 MCI/55 NC) from the ADNI were used to construct classification models and the performances were testified with 10-fold cross validation. To test the generalization of the proposed method, two extra validation datasets including longitudinal ADNI data (30 MCI/16 NC) and collected data from Xuanwu Hospital (27 MCI/32 NC) were employed respectively to evaluate the performance. No matter whether testing was done on the discovery dataset or the extra validation datasets, the accuracies were about 80% with the combined morphological metrics, which were significantly superior to single metric (accuracy: 45% ∼76%) and also displayed good generalization across datasets. Additionally, gyrification index and cortical thickness derived from surface-based morphometry outperformed other features in MCI identification, suggesting they were some key morphological biomarkers for early MCI diagnosis. Combining the multiple morphological metrics together resulted in a significantly better and reliable identification model, which may be helpful to assist in the clinical diagnosis of MCI.
轻度认知障碍 (MCI) 表现出向阿尔茨海默病 (AD) 进展的高风险，通常被认为是 AD 的前兆。寻找有效的、稳健的方法对 MCI 进行早期诊断非常重要。在本文中，提出了一种基于随机森林的方法，该方法结合了多种形态学指标，用于从正常对照组 (NC) 中识别 MCI。利用基于体素的形态学、基于变形的形态学和基于表面的形态学来提取形态学指标，如灰质体积、雅可比行列式值、皮质厚度、脑回指数、脑沟深度和分形维数。使用 ADNI 的初始发现数据集 (56 例 MCI/55 例 NC) 构建分类模型，并通过 10 折交叉验证验证性能。为了测试所提出方法的泛化能力，分别使用另外两个验证数据集，包括纵向 ADNI 数据 (30 例 MCI/16 例 NC) 和宣武医院收集的数据 (27 例 MCI/32 例 NC) 来评估性能。无论在发现数据集还是额外的验证数据集上进行测试，使用组合形态学指标的准确率均约为 80%，明显优于单一指标 (准确率：45%∼76%)，并且在数据集之间也具有良好的泛化能力。此外，基于表面的形态学中得出的脑回指数和皮质厚度在 MCI 识别中优于其他特征，表明它们是早期 MCI 诊断的一些关键形态学生物标志物。将多种形态学指标结合在一起，可以得到一个显著更好、更可靠的识别模型，这可能有助于辅助 MCI 的临床诊断。
