首页> 美国卫生研究院文献>Journal of Cerebral Blood Flow Metabolism >Multivariate spatial covariance analysis of 99mTc-exametazime SPECT images in dementia with Lewy bodies and Alzheimers disease: utility in differential diagnosis
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Multivariate spatial covariance analysis of 99mTc-exametazime SPECT images in dementia with Lewy bodies and Alzheimers disease: utility in differential diagnosis

机译:患有路易体和阿尔茨海默氏病的痴呆症中99mTc-依美西汀SPECT图像的多元空间协方差分析:在鉴别诊断中的用途

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摘要

We examined 99mTc-exametazime brain blood flow single-photon emission computed tomography (SPECT) images using a spatial covariance analysis (SCA) approach to assess its diagnostic value in distinguishing dementia with Lewy bodies (DLB) from Alzheimer's disease (AD). Voxel SCA was simultaneously applied to a set of preprocessed images (AD, n=40; DLB, n=26), generating a series of eigenimages representing common intercorrelated voxels in AD and DLB. Linear regression derived a spatial covariance pattern (SCP) that discriminated DLB from AD. To investigate the diagnostic value of the model SCP, the SCP was validated by applying it to a second, independent, AD and DLB cohort (AD, n=34; DLB, n=29). Mean SCP expressions differed between AD and DLB (F1,64=36.2, P<0.001) with good diagnostic accuracy (receiver operating characteristic (ROC) curve area 0.87, sensitivity 81%, specificity 88%). Forward application of the model SCP to the independent cohort revealed similar differences between groups (F1,61=38.4, P<0.001), also with good diagnostic accuracy (ROC 0.86, sensitivity 80%, specificity 80%). Multivariate analysis of blood flow SPECT data appears to be robust and shows good diagnostic accuracy in two independent cohorts for distinguishing DLB from AD.
机译:我们使用空间协方差分析(SCA)方法检查了 99m Tc-Exametazime脑血流单光子发射计算机断层扫描(SPECT)图像,以评估其对区分路易体痴呆症(DLB)和阿尔茨海默氏病(AD)。同时将体素SCA应用于一组预处理图像(AD,n = 40; DLB,n = 26),生成表示本征图像的一系列本征图像,这些特征图像表示AD和DLB中常见的相互关联的体素。线性回归得出的空间协方差模式(SCP)可以将DLB与AD区别开来。为了调查模型SCP的诊断价值,通过将SCP应用于第二个独立的AD和DLB队列(AD,n = 34; DLB,n = 29)来验证SCP。 AD和DLB之间的平均SCP表达差异(F1,64 = 36.2,P <0.001),具有良好的诊断准确性(接收器工作特征(ROC)曲线面积0.87,灵敏度81%,特异性88%)。将模型SCP向前应用到独立队列中,发现各组之间存在相似的差异(F1,61 = 38.4,P <0.001),诊断准确性也很好(ROC 0.86,敏感性80%,特异性80%)。血流SPECT数据的多变量分析似乎很可靠,并且在两个独立的队列中显示了良好的诊断准确性,以区分DLB和AD。

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