Hett Kilian, Ta Vinh-Thong, Oguz Ipek, Manjón José V, Coupé Pierrick
CNRS, Univ. Bordeaux, Bordeaux INP, LABRI, UMR5800, PICTURA, Talence F-33400, France; Vanderbilt University, Department of Electrical Engineering and Computer Science, Nashville, TN, USA.
CNRS, Univ. Bordeaux, Bordeaux INP, LABRI, UMR5800, PICTURA, Talence F-33400, France.
Med Image Anal. 2021 Jan;67:101850. doi: 10.1016/j.media.2020.101850. Epub 2020 Oct 6.
The prediction of subjects with mild cognitive impairment (MCI) who will progress to Alzheimer's disease (AD) is clinically relevant, and may above all have a significant impact on accelerating the development of new treatments. In this paper, we present a new MRI-based biomarker that enables us to accurately predict conversion of MCI subjects to AD. In order to better capture the AD signature, we introduce two main contributions. First, we present a new graph-based grading framework to combine inter-subject similarity features and intra-subject variability features. This framework involves patch-based grading of anatomical structures and graph-based modeling of structure alteration relationships. Second, we propose an innovative multiscale brain analysis to capture alterations caused by AD at different anatomical levels. Based on a cascade of classifiers, this multiscale approach enables the analysis of alterations of whole brain structures and hippocampus subfields at the same time. During our experiments using the ADNI-1 dataset, the proposed multiscale graph-based grading method obtained an area under the curve (AUC) of 81% to predict conversion of MCI subjects to AD within three years. Moreover, when combined with cognitive scores, the proposed method obtained 85% of AUC. These results are competitive in comparison to state-of-the-art methods evaluated on the same dataset.
预测轻度认知障碍（MCI）患者是否会进展为阿尔茨海默病（AD）具有临床相关性，最重要的是可能对加速新疗法的研发产生重大影响。在本文中，我们提出了一种基于磁共振成像（MRI）的新生物标志物，它使我们能够准确预测MCI患者向AD的转化。为了更好地捕捉AD特征，我们做出了两项主要贡献。首先，我们提出了一种新的基于图的分级框架，将个体间相似性特征和个体内变异性特征相结合。该框架涉及基于补丁的解剖结构分级和基于图的结构改变关系建模。其次，我们提出了一种创新的多尺度脑分析方法，以捕捉AD在不同解剖水平上引起的改变。基于一系列分类器，这种多尺度方法能够同时分析全脑结构和海马亚区结构的改变。在使用阿尔茨海默病神经成像计划（ADNI-1）数据集进行实验时，所提出的基于多尺度图的分级方法在预测MCI患者在三年内转化为AD方面获得了81%的曲线下面积（AUC）。此外，当与认知评分相结合时，该方法获得了85%的AUC。与在同一数据集上评估的现有最先进方法相比，这些结果具有竞争力。
