Gómez-Sancho Marta, Tohka Jussi, Gómez-Verdejo Vanessa
Department of Signal Processing and Communications, Universidad Carlos III de Madrid, Leganes, Spain.
University of Eastern Finland, AI Virtanen Institute for Molecular Sciences, Kuopio, Finland.
Magn Reson Imaging. 2018 Jul;50:84-95. doi: 10.1016/j.mri.2018.03.003. Epub 2018 Mar 10.
Alzheimer's disease (AD) is a progressive neurological disorder in which the death of brain cells causes memory loss and cognitive decline. The identification of at-risk subjects yet showing no dementia symptoms but who will later convert to AD can be crucial for the effective treatment of AD. For this, Magnetic Resonance Imaging (MRI) is expected to play a crucial role. During recent years, several Machine Learning (ML) approaches to AD-conversion prediction have been proposed using different types of MRI features. However, few studies comparing these different feature representations exist, and the existing ones do not allow to make definite conclusions. We evaluated the performance of various types of MRI features for the conversion prediction: voxel-based features extracted based on voxel-based morphometry, hippocampus volumes, volumes of the entorhinal cortex, and a set of regional volumetric, surface area, and cortical thickness measures across the brain. Regional features consistently yielded the best performance over two classifiers (Support Vector Machines and Regularized Logistic Regression), and two datasets studied. However, the performance difference to other features was not statistically significant. There was a consistent trend of age correction improving the classification performance, but the improvement reached statistical significance only rarely.
阿尔茨海默病（AD）是一种进行性神经疾病，其中脑细胞死亡会导致记忆丧失和认知衰退。识别尚无痴呆症状但日后会转变为AD的高危个体对于AD的有效治疗可能至关重要。为此，磁共振成像（MRI）有望发挥关键作用。近年来，已经提出了几种使用不同类型MRI特征的机器学习（ML）方法来进行AD转变预测。然而，比较这些不同特征表示的研究很少，现有的研究也无法得出明确结论。我们评估了各种类型的MRI特征用于转变预测的性能：基于体素形态学提取的基于体素的特征、海马体积、内嗅皮质体积以及一组全脑的区域体积、表面积和皮质厚度测量值。在两个分类器（支持向量机和正则化逻辑回归）以及两个研究数据集上，区域特征始终表现出最佳性能。然而，与其他特征的性能差异没有统计学意义。年龄校正存在一致的趋势可提高分类性能，但这种提高仅在极少数情况下达到统计学意义。
