Predicting behavioral variant frontotemporal dementia with pattern classification in multi-center structural MRI data.
Predicting behavioral variant frontotemporal dementia with pattern classification in multi-center structural MRI data.
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DOI：
10.1016/j.nicl.2017.02.001
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
FTLDc Study Group
中科院分区：
文献类型：
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作者：
Meyer S;Mueller K;Stuke K;Bisenius S;Diehl-Schmid J;Jessen F;Kassubek J;Kornhuber J;Ludolph AC;Prudlo J;Schneider A;Schuemberg K;Yakushev I;Otto M;Schroeter ML;FTLDc Study Group
额颞叶变性（FTLD）是早发性痴呆的常见原因。行为变异性额颞叶痴呆（bvFTD）是其最常见的亚型，其特征是行为和个性的深刻改变。2011年，提出了新的诊断标准，将成像标准纳入诊断算法。该研究旨在验证成像标准的潜力，以通过机器学习算法单独预测诊断。在来自德国FTLD联盟研究的52例bvFTD患者和52例健康对照受试者的多中心队列中，采用3 T结构磁共振成像（MRI）测量脑萎缩。除组间比较外，根据最近的荟萃分析，在全脑或额颞叶、岛叶区域和已知主要受影响的基底神经节的MRI数据中，使用支持向量机分类，对每例受试者中FTD与对照组的诊断进行了单独预测。多中心效应通过一种新的方法进行控制，即“排除一个中心”联合分析，即从分析中重复排除每个中心的受试者。组间比较显示bvFTD组额叶萎缩最为一致，除额叶、基底节和颞叶改变外。最值得注意的是，支持向量机分类能够以高达84.6%的高准确度预测单个患者的诊断，其中与全脑方法相比，关注额颞叶、岛叶区域和基底神经节的感兴趣区域方法的准确度最高。我们的研究表明，MRI是一种广泛使用的成像技术，可以在多中心成像数据中以高精度单独识别bvFTD，为未来的个性化诊断方法铺平道路。行为变异性额颞叶痴呆的诊断标准包括影像学检查。研究验证了MRI通过机器学习算法预测诊断的潜力。支持向量机分类器具有较高的分类精度。疾病特异性方法的准确性高于全脑方法。结构MRI可以单独识别行为变异型额颞叶痴呆。
Frontotemporal lobar degeneration (FTLD) is a common cause of early onset dementia. Behavioral variant frontotemporal dementia (bvFTD), its most common subtype, is characterized by deep alterations in behavior and personality. In 2011, new diagnostic criteria were suggested that incorporate imaging criteria into diagnostic algorithms. The study aimed at validating the potential of imaging criteria to individually predict diagnosis with machine learning algorithms. Brain atrophy was measured with structural magnetic resonance imaging (MRI) at 3 Tesla in a multi-centric cohort of 52 bvFTD patients and 52 healthy control subjects from the German FTLD Consortium's Study. Beside group comparisons, diagnosis bvFTD vs. controls was individually predicted in each subject with support vector machine classification in MRI data across the whole brain or in frontotemporal, insular regions, and basal ganglia known to be mainly affected based on recent meta-analyses. Multi-center effects were controlled for with a new method, “leave one center out” conjunction analyses, i.e. repeatedly excluding subjects from each center from the analysis. Group comparisons revealed atrophy in, most consistently, the frontal lobe in bvFTD beside alterations in the insula, basal ganglia and temporal lobe. Most remarkably, support vector machine classification enabled predicting diagnosis in single patients with a high accuracy of up to 84.6%, where accuracy was highest in a region-of-interest approach focusing on frontotemporal, insular regions, and basal ganglia in comparison with the whole brain approach. Our study demonstrates that MRI, a widespread imaging technology, can individually identify bvFTD with high accuracy in multi-center imaging data, paving the road to personalized diagnostic approaches in the future. Diagnostic criteria for behavioral variant frontotemporal dementia include imaging. Study validates MRI's potential to predict diagnosis with machine learning algorithms. Support vector machine classification enabled high classification accuracy. Accuracy was higher in disease-specific than whole-brain approaches. Structural MRI can individually identify behavioral variant frontotemporal dementia.