An MRI-derived definition of MCI-to-AD conversion for long-term, automatic prognosis of MCI patients.
An MRI-derived definition of MCI-to-AD conversion for long-term, automatic prognosis of MCI patients.
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DOI：
10.1371/journal.pone.0025074
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发表时间：
2011
期刊：
影响因子：
3.7
通讯作者：
Yang QX
中科院分区：
文献类型：
--
作者：
Aksu Y;Miller DJ;Kesidis G;Bigler DC;Yang QX
阿尔茨海默病（AD）和轻度认知障碍（MCI）是当前研究的热点。虽然对于 MCI 是否真的“转变”为 AD 尚未达成共识，但这一概念已被广泛应用。因此，更重要的问题不是 MCI 是否会转化，而是最佳的定义是什么。我们专注于自动预测，名义上仅使用基线大脑图像来预测 MCI 是否会在初次临床就诊后的多年内发生转变。这不是传统的监督学习问题，因为在 ADNI 中，没有明确的标记转换示例。它也不是不受监督的，因为有（标记的）AD 和对照，以及 MCI 的认知分数。先前的工作根据临床评分是否较基线发生显着变化来定义 MCI 子类。然而，人们对这些定义存在担忧，因为例如，即使可能发生生理变化，大多数 MCI（和 AD）在 ADNI 的任何后续访视中都不会从基线 CDR = 0.5 发生变化。这些工作在定义转换时忽略了 MCI 患者脑部扫描中丰富的表型信息以及标记的 AD 和对照示例。我们提出了一个创新的定义，其中如果患者的任何脑部扫描被 Control-AD 分类器分类为“AD”，则 MCI 是转换器。该定义引导了第二个分类器的设计，该分类器经过专门训练来预测 MCI 是否会转换。因此，我们预测 AD 控制分类器是否会预测患者患有 AD。我们的结果表明，这一定义不仅比 CDR 转换具有更高的预后准确性，而且使亚群与已知的 AD 生物标志物（包括 CSF 标志物）更加一致。我们还确定了关键的预后大脑区域生物标志物。
Alzheimer's disease (AD) and mild cognitive impairment (MCI) are of great current research interest. While there is no consensus on whether MCIs actually “convert” to AD, this concept is widely applied. Thus, the more important question is not whether MCIs convert, but what is the best such definition. We focus on automatic prognostication, nominally using only a baseline brain image, of whether an MCI will convert within a multi-year period following the initial clinical visit. This is not a traditional supervised learning problem since, in ADNI, there are no definitive labeled conversion examples. It is not unsupervised, either, since there are (labeled) ADs and Controls, as well as cognitive scores for MCIs. Prior works have defined MCI subclasses based on whether or not clinical scores significantly change from baseline. There are concerns with these definitions, however, since, e.g., most MCIs (and ADs) do not change from a baseline CDR = 0.5 at any subsequent visit in ADNI, even while physiological changes may be occurring. These works ignore rich phenotypical information in an MCI patient's brain scan and labeled AD and Control examples, in defining conversion. We propose an innovative definition, wherein an MCI is a converter if any of the patient's brain scans are classified “AD” by a Control-AD classifier. This definition bootstraps design of a second classifier, specifically trained to predict whether or not MCIs will convert. We thus predict whether an AD-Control classifier will predict that a patient has AD. Our results demonstrate that this definition leads not only to much higher prognostic accuracy than by-CDR conversion, but also to subpopulations more consistent with known AD biomarkers (including CSF markers). We also identify key prognostic brain region biomarkers.
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影响因子：
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作者：
De Meyer, Geert;Shapiro, Fred;Vanderstichele, Hugo;Vanmechelen, Eugeen;Engelborghs, Sebastiaan;De Deyn, Peter Paul;Coart, Els;Hansson, Oskar;Minthon, Lennart;Zetterberg, Henrik;Blennow, Kaj;Shaw, Leslie;Trojanowski, John Q.
通讯作者：
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作者：
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通讯作者：
Davatzikos, Christos
影响因子：
5.7
作者：
Davatzikos, C;Genc, A;Resnick, SM
通讯作者：
Resnick, SM