Predicting conversion from MCI to AD with FDG-PET brain images at different prodromal stages
Predicting conversion from MCI to AD with FDG-PET brain images at different prodromal stages
复制标题
DOI：
10.1016/j.compbiomed.2015.01.003
复制
发表时间：
2015-03-01
影响因子：
7.7
通讯作者：
Silveira, Margarida
中科院分区：
文献类型：
--
作者：
Cabral, Carlos;Morgado, Pedro M.;Silveira, Margarida
阿尔茨海默病（AD）的早期诊断，虽然仍处于被称为轻度认知障碍（MCI）的阶段，但对于开发新的治疗方法非常重要。然而，MCI中的脑变性随着时间的推移而发展，并且因患者而异，使得早期诊断成为一项非常具有挑战性的任务。尽管存在这些困难，许多机器学习技术已经被用于MCI的诊断和预测MCI向AD的转化，但在以前的工作中使用的MCI组通常是非常异质的，包含不同阶段的受试者。本文的目标是研究疾病阶段如何影响机器学习方法预测转化的能力。在确定转换者并估计转换时间（TC）（使用神经心理学测试分数）后，我们根据转换瞬间（转换前0，6，12，18和24个月）的时间距离设计了MCI转换者（MCI-C）的5个亚组。接下来，我们使用这些亚组的FDG-PET图像和训练的分类器来区分不同阶段的MCI-C和稳定的非转换者（MCI-NC）。我们的研究结果表明，MCI到AD的转换早在转换前24个月就可以预测，并且机器学习方法的区分能力随着到TC的时间距离的增加而降低。这些发现是一致的所有测试分类。我们的研究结果还表明，这种减少是由于用于分类的区域中所包含的信息减少以及自动选择过程的稳定性降低。(C)2015爱思唯尔有限公司版权所有。
Early diagnosis of Alzheimer disease (AD), while still at the stage known as mild cognitive impairment (MCI), is important for the development of new treatments. However, brain degeneration in MCI evolves with time and differs from patient to patient, making early diagnosis a very challenging task. Despite these difficulties, many machine learning techniques have already been used for the diagnosis of MCI and for predicting MCI to AD conversion, but the MCI group used in previous works is usually very heterogeneous containing subjects at different stages. The goal of this paper is to investigate how the disease stage impacts on the ability of machine learning methodologies to predict conversion. After identifying the converters and estimating the time of conversion (TC) (using neuropsychological test scores), we devised 5 subgroups of MCI converters (MCI-C) based on their temporal distance to the conversion instant (0, 6, 12, 18 and 24 months before conversion). Next, we used the FDG-PET images of these subgroups and trained classifiers to distinguish between the MCI-C at different stages and stable non-converters (MCI-NC). Our results show that MCI to AD conversion can be predicted as early as 24 months prior to conversion and that the discriminative power of the machine learning methods decreases with the increasing temporal distance to the TC, as expected. These findings were consistent for all the tested classifiers. Our results also show that this decrease arises from a reduction in the information contained in the regions used for classification and by a decrease in the stability of the automatic selection procedure. (C) 2015 Elsevier Ltd. All rights reserved.