MRI衍生的MCI向AD转化的定义用于MCI患者的长期自动预后
An MRI-Derived Definition of MCI-to-AD Conversion for Long-Term, Automati c Prognosis of MCI Patients
摘要：
阿尔茨海默病（AD）和轻度认知障碍（MCI）继续被广泛研究。虽然对MCIs是否实际上“转化”为AD没有共识，但更重要的问题不是MCIs是否转化，而是什么是最好的定义。我们专注于自动诊断，名义上只使用基线图像脑扫描，MCI个体是否会在首次临床访视后的多年内转换为AD。这实际上不是一个传统的监督学习问题，因为在ADNI中，没有明确的MCI转换标记示例。先前的工作已经基于临床/认知评分如CDR是否从基线显著变化来定义MCI亚类。然而，这些定义存在问题，因为例如，即使可能发生生理变化，大多数MCI（和AD）也不会从基线CDR=0.5发生变化。这些工作忽略了MCI患者的大脑扫描中丰富的表型信息，并标记了AD和对照实例，以定义转换。我们提出了一个创新的转换定义，其中MCI患者被宣布为转换器，如果任何患者的脑部扫描（在后续访问）被分类为“AD”的（精确设计的）控制AD分类器。这个新的定义引导了第二个分类器的设计，专门训练来预测MCI是否会转换。因此，该第二分类器预测AD-对照分类器是否将预测患者患有AD。我们的研究结果表明，这种新的定义不仅导致比通过CDR转换更高的预后准确性，而且亚群与已知的AD脑区生物标志物更一致。我们还确定了关键的预后区域生物标志物，这对于准确区分转换者和非转换者组至关重要。
英文摘要：
Alzheimer's disease (AD) and mild cognitive impairment (MCI), continue to be widely studied. While there is no consensus on whether MCIs actually "convert" to AD, 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 image brain scan, of whether an MCI individual will convert to AD within a multi-year period following the initial clinical visit. This is in fact not a traditional supervised learning problem since, in ADNI, there are no definitive labeled examples of MCI conversion. Prior works have defined MCI subclasses based on whether or not clinical/cognitive scores such as CDR 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, 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 conversion definition, wherein an MCI patient is declared to be a converter if any of the patient's brain scans (at follow-up visits) are classified "AD" by an (accurately-designed) Control-AD classifier. This novel definition bootstraps the design of a second classifier, specifically trained to predict whether or not MCIs will convert. This second classifier thus predicts whether an AD-Control classifier will predict that a patient has AD. Our results demonstrate this new definition leads not only to much higher prognostic accuracy than by-CDR conversion, but also to subpopulations much more consistent with known AD brain region biomarkers. We also identify key prognostic region biomarkers, essential for accurately discriminating the converter and nonconverter groups.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
