Identifying Mild Alzheimer's Disease With First 30-Min (11)C-PiB PET Scan.
Identifying Mild Alzheimer's Disease With First 30-Min (11)C-PiB PET Scan.
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通过首次 30 分钟 11C-PiB PET 扫描识别轻度阿尔茨海默病
DOI：
10.3389/fnagi.2022.785495
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发表时间：
2022
影响因子：
4.8
通讯作者：
中科院分区：
文献类型：
--
作者：
11 C标记匹兹堡化合物B（11 C-Pi B）PET成像可通过定量Pi B与脑内β-淀粉样蛋白沉积的结合，为阿尔茨海默病（AD）的诊断提供信息。定量指标，如标准摄取值比（SUVR）和分布容积比（DVR），已被用来有效区分健康和AD受试者。然而，这些措施需要长的等待/扫描时间，以及选择最佳参考区域。在这项研究中，我们提出了一种替代措施命名为淀粉样蛋白定量指数（AQI），它可以获得与第一个30分钟的扫描，而无需选择的参考区域。从公共数据集“OASIS-3”获得11 C标记的匹兹堡化合物B PET扫描数据。共纳入60名轻度AD受试者和60名健康对照者，每组50名用于训练，10名用于测试。所提出的测量AQI结合了来自第一个30分钟扫描的特征脑区域中的清除率和中期PIB保留的信息。对于训练集中的每个受试者，计算AQI、SUVR和DVR，并用于逻辑回归分类器的分类。进行受试者工作特征（ROC）分析，以评估这些措施的性能。报告了准确度、灵敏度和特异性。还对所有指标进行了Kruskal-Wallis检验和效应量评价。然后，使用相同的方法在测试集上进一步验证了三种度量的性能。分析这些指标与临床MMSE和CDR-MMSE评分之间的相关性。Kruskal-Wallis检验表明AQI、SUVR和DVR都可以区分健康和轻度AD受试者（p < 0.001）。对于训练集，ROC分析表明，AQI取得了最好的分类性能，准确率为0.93，高于SUVR的0.88和DVR的0.89。AQI、SUVR和DVR的效应量分别为2.35、2.12和2.06，表明AQI是这些措施中最有效的。对于测试集，所有三种测量都实现了不那么上级的性能，而AQI仍然表现最好，具有0.85的最高准确度。一些低于阈值SUVR和DVR值的假阴性病例使用AQI被正确识别。所有这三个指标均显示与临床评分具有显著和可比的相关性（p < 0.01）。淀粉样蛋白定量指数结合了早期动力学信息和一定程度的β-淀粉样蛋白沉积，并且可以使用来自第一个30分钟动态扫描的数据提供更好的鉴别性能。此外，它表明，临床上难以区分的AD病例有关PiB保留可能可以正确识别。
11C-labeled Pittsburgh compound B (11C-PiB) PET imaging can provide information for the diagnosis of Alzheimer's disease (AD) by quantifying the binding of PiB to β-amyloid deposition in the brain. Quantification index, such as standardized uptake value ratio (SUVR) and distribution volume ratio (DVR), has been exploited to effectively distinguish between healthy and subjects with AD. However, these measures require a long wait/scan time, as well as the selection of an optimal reference region. In this study, we propose an alternate measure named amyloid quantification index (AQI), which can be obtained with the first 30-min scan without the selection of the reference region. 11C-labeled Pittsburgh compound B PET scan data were obtained from the public dataset “OASIS-3”. A total of 60 mild subjects with AD and 60 healthy controls were included, with 50 used for training and 10 used for testing in each group. The proposed measure AQI combines information of clearance rate and mid-phase PIB retention in featured brain regions from the first 30-min scan. For each subject in the training set, AQI, SUVR, and DVR were calculated and used for classification by the logistic regression classifier. The receiver operating characteristic (ROC) analysis was performed to evaluate the performance of these measures. Accuracy, sensitivity, and specificity were reported. The Kruskal–Wallis test and effect size were also performed and evaluated for all measures. Then, the performance of three measures was further validated on the testing set using the same method. The correlations between these measures and clinical MMSE and CDR-SOB scores were analyzed. The Kruskal–Wallis test suggested that AQI, SUVR, and DVR can all differentiate between the healthy and subjects with mild AD (p < 0.001). For the training set, ROC analysis showed that AQI achieved the best classification performance with an accuracy rate of 0.93, higher than 0.88 for SUVR and 0.89 for DVR. The effect size of AQI, SUVR, and DVR were 2.35, 2.12, and 2.06, respectively, indicating that AQI was the most effective among these measures. For the testing set, all three measures achieved less superior performance, while AQI still performed the best with the highest accuracy of 0.85. Some false-negative cases with below-threshold SUVR and DVR values were correctly identified using AQI. All three measures showed significant and comparable correlations with clinical scores (p < 0.01). Amyloid quantification index combines early-phase kinetic information and a certain degree of β-amyloid deposition, and can provide a better differentiating performance using the data from the first 30-min dynamic scan. Moreover, it was shown that clinically indistinguishable AD cases regarding PiB retention potentially can be correctly identified.
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影响因子：
5.5
作者：
Patel，Kunal P.;Wymer，David T.;Rajadhyaksha，Chetan D.
通讯作者：
Rajadhyaksha，Chetan D.
影响因子：
2.4
作者：
Ponto, Laura L. Boles;Moser, David J.;Schultz, Susan K.
通讯作者：
Schultz, Susan K.
阿尔茨海默氏病从正常痴呆到轻度痴呆的投射反映在功能网络连通性中：一项纵向研究。
影响因子：
3.5
作者：
Sendi MSE;Zendehrouh E;Miller RL;Fu Z;Du Y;Liu J;Mormino EC;Salat DH;Calhoun VD
通讯作者：
Calhoun VD
影响因子：
5.7
作者：
Lammertsma, AA;Hume, SP
通讯作者：
Hume, SP
影响因子：
11.2
作者：
Klunk, WE;Engler, H;Långström, B
通讯作者：
Långström, B