Eckert Thomas, Van Laere Koen, Tang Chengke, Lewis Daniel E, Edwards Christine, Santens Patrick, Eidelberg David
Center for Neurosciences, The Feinstein Institute for Medical Research, North Shore-LIJ Health System, 350 Community Drive, Manhasset, NY 11030, USA.
Eur J Nucl Med Mol Imaging. 2007 Apr;34(4):496-501. doi: 10.1007/s00259-006-0261-9. Epub 2006 Nov 10.
Spatial covariance analysis has been used with FDG PET to identify a specific metabolic network associated with Parkinson's disease (PD). In the current study, we utilized a new, fully automated voxel-based method to quantify network expression in ECD SPECT images from patients with classical PD, patients with multiple system atrophy (MSA), and healthy control subjects.
We applied a previously validated voxel-based PD-related covariance pattern (PDRP) to quantify network expression in the ECD SPECT scans of 35 PD patients, 15 age- and disease severity-matched MSA patients, and 35 age-matched healthy control subjects. PDRP scores were compared across groups using analysis of variance. The sensitivity and specificity of the prospectively computed PDRP scores in the differential diagnosis of individual subjects were assessed by receiver operating characteristic (ROC) analysis.
PDRP scores were significantly increased (p < 0.001) in the PD group relative to the MSA and control groups. ROC analysis indicated that the overall diagnostic accuracy of the PDRP measures was 0.91 (AUC). The optimal cutoff value was consistent with a sensitivity of 0.97 and a specificity of 0.80 and 0.71 for discriminating PD patients from MSA and normal controls, respectively.
Our findings suggest that fully automated voxel-based network assessment techniques can be used to quantify network expression in the ECD SPECT scans of parkinsonian patients.
空间协方差分析已与氟代脱氧葡萄糖正电子发射断层扫描（FDG PET）结合使用，以识别与帕金森病（PD）相关的特定代谢网络。在本研究中，我们采用了一种全新的、基于体素的全自动方法，来量化经典型PD患者、多系统萎缩（MSA）患者及健康对照者的ECD单光子发射计算机断层扫描（SPECT）图像中的网络表达。
我们应用先前验证过的基于体素的帕金森病相关协方差模式（PDRP），对35例PD患者、15例年龄及疾病严重程度匹配的MSA患者以及35例年龄匹配的健康对照者的ECD SPECT扫描图像进行网络表达量化。使用方差分析对各组的PDRP评分进行比较。通过受试者操作特征（ROC）分析评估前瞻性计算的PDRP评分在个体受试者鉴别诊断中的敏感性和特异性。
与MSA组和对照组相比，PD组的PDRP评分显著升高（p < 0.001）。ROC分析表明，PDRP测量的总体诊断准确性为0.91（曲线下面积）。最佳截断值对应的敏感性为0.97，分别鉴别PD患者与MSA患者及正常对照的特异性为0.80和0.71。
我们的研究结果表明，基于体素的全自动网络评估技术可用于量化帕金森病患者ECD SPECT扫描图像中的网络表达。
