Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
Sci Rep. 2017 Jan 12;7:39880. doi: 10.1038/srep39880.
Accurate prediction of Alzheimer's disease (AD) is important for the early diagnosis and treatment of this condition. Mild cognitive impairment (MCI) is an early stage of AD. Therefore, patients with MCI who are at high risk of fully developing AD should be identified to accurately predict AD. However, the relationship between brain images and AD is difficult to construct because of the complex characteristics of neuroimaging data. To address this problem, we present a longitudinal measurement of MCI brain images and a hierarchical classification method for AD prediction. Longitudinal images obtained from individuals with MCI were investigated to acquire important information on the longitudinal changes, which can be used to classify MCI subjects as either MCI conversion (MCIc) or MCI non-conversion (MCInc) individuals. Moreover, a hierarchical framework was introduced to the classifier to manage high feature dimensionality issues and incorporate spatial information for improving the prediction accuracy. The proposed method was evaluated using 131 patients with MCI (70 MCIc and 61 MCInc) based on MRI scans taken at different time points. Results showed that the proposed method achieved 79.4% accuracy for the classification of MCIc versus MCInc, thereby demonstrating very promising performance for AD prediction.
准确预测阿尔茨海默病（AD）对于该病的早期诊断和治疗非常重要。轻度认知障碍（MCI）是 AD 的早期阶段。因此，应识别出处于 AD 完全发展高风险的 MCI 患者，以准确预测 AD。然而，由于神经影像学数据的复杂特征，脑图像与 AD 之间的关系难以构建。为了解决这个问题，我们提出了一种针对 MCI 脑图像的纵向测量和用于 AD 预测的分层分类方法。研究了从 MCI 个体中获得的纵向图像，以获取有关纵向变化的重要信息，这些信息可用于将 MCI 受试者分类为 MCI 转化（MCIc）或 MCI 非转化（MCInc）个体。此外，引入了分层框架到分类器中，以管理高特征维度问题，并整合空间信息以提高预测准确性。该方法基于在不同时间点采集的 MRI 扫描，使用 131 名 MCI 患者（70 名 MCIc 和 61 名 MCInc）进行了评估。结果表明，该方法在 MCIc 与 MCInc 的分类中达到了 79.4%的准确性，从而为 AD 预测提供了非常有前途的性能。
