Invicro LLC, London, United Kingdom; and
Invicro LLC, London, United Kingdom; and.
J Nucl Med. 2021 Sep 1;62(9):1292-1300. doi: 10.2967/jnumed.120.258962. Epub 2021 Jan 30.
Recently, Amyloid was introduced as a new canonical image-based algorithm to quantify amyloid PET scans and demonstrated increased power over traditional SUV ratio (SUVR) approaches when assessed in cross-sectional and longitudinal analyses. We build further on this mathematical framework to develop a Tau algorithm for the quantitative analysis of the more complex spatial distribution displayed by tau PET radiotracers. Cross-sectional ( = 615) and longitudinal ( = 149) F-flortaucipir data were obtained from the Alzheimer's Disease Neuroimaging Initiative along with necessary adjunct amyloid PET and T1-weighted structural MRI data. A subset of these data were used to derive a chronological tau dataset, using Amyloid analysis of associated amyloid PET data to calculate the subject's temporal position in the canonical AD disease process, from which canonical images for the nonspecific and specific binding components of F-flortaucipir in AD were calculated. These 2 canonical images were incorporated into the Tau algorithm that enables the quantification of both global and local tau outcome measures using an image-based regression and statistical parametric analysis of the initial residual image. Performance of the Tau algorithm was compared with SUVR approaches for cross-sectional analyses, longitudinal analyses, and correlation with clinical measures (Alzheimer Disease Assessment Scale-Cognitive Subscale [ADAS-Cog], Clinical Dementia Rating scale-sum of boxes [CDR-SB], and Mini-Mental State Examination [MMSE]). Tau successfully calculated global tau load (Tau) in all 791 scans analyzed (range, -3.5% to 185.2%; mean ± SD, 23% ± 20.5%) with a nonzero additional local tau component being required in 31% of all scans (cognitively normal [CN], 22%; mild cognitive impairment [MCI], 35%; dementia, 72%). Tau was compared with the best SUVR approach in the cross-sectional analysis (Tau increase in effect size: CN- vs. CN+, +45%; CN- vs. MCI+, -5.6%; CN- vs. dementia+, +2.3%) (+/- indicates amyloid-positive or -negative) and correlation with clinical scores (Tau increase in : CDR-SB+, 7%; MMSE+, 38%; ADAS-Cog+, 0%). Tau substantially outperformed SUVR approaches in the longitudinal analysis (Tau increase in power: CN+, >3.2-fold; MCI+, >2.2-fold; dementia+, >2.9-fold). Tau as calculated by Tau provides a superior approach for the quantification of tau PET data. In particular, it provides a substantial improvement in power for longitudinal analyses and the early detection of tau deposition and thus should have significant value for clinical imaging trials in AD that are investigating the attenuation of tau deposition with novel therapies.
最近，淀粉样蛋白被引入作为一种新的基于图像的算法，用于定量分析淀粉样蛋白 PET 扫描，并在横断面和纵向分析中显示出比传统 SUV 比（SUVR）方法更高的效能。我们在这个数学框架的基础上进一步发展了一种用于定量分析 tau PET 示踪剂显示的更复杂空间分布的 tau 算法。从阿尔茨海默病神经影像学倡议中获得了横断面（=615）和纵向（=149）F-氟替卡培尔的 Flortaucipir 数据，以及必要的辅助淀粉样蛋白 PET 和 T1 加权结构 MRI 数据。这些数据的一部分被用来推导出一个时间tau 数据集，使用相关的淀粉样蛋白 PET 数据的淀粉样蛋白分析来计算受试者在经典 AD 疾病过程中的时间位置，从而计算出 AD 中 F-氟替卡培尔的非特异性和特异性结合成分的标准图像。这 2 个标准图像被纳入 Tau 算法中，该算法可以使用基于图像的回归和初始残差图像的统计参数分析来定量测量 tau 的全局和局部结果。Tau 算法的性能与 SUVR 方法在横断面分析、纵向分析和与临床指标的相关性（阿尔茨海默病评估量表-认知子量表[ADAS-Cog]、临床痴呆评定量表-总评分[CDR-SB]和简易精神状态检查[MMSE]）方面进行了比较。Tau 成功地计算了所有 791 次分析扫描中的全局 tau 负荷（Tau）（范围为-3.5%至 185.2%；平均值±标准差为 23%±20.5%），所有扫描中有 31%需要额外的局部 tau 成分（认知正常[CN]，22%；轻度认知障碍[MCI]，35%；痴呆，72%）。Tau 与最佳 SUVR 方法在横断面分析中进行了比较（Tau 的效应量增加：CN-vs.CN+，+45%；CN-vs.MCI+，-5.6%；CN-vs.痴呆+，+2.3%）（+/-表示淀粉样蛋白阳性或阴性），并与临床评分相关（Tau 的增加：CDR-SB+，7%；MMSE+，38%；ADAS-Cog+，0%）。Tau 在纵向分析中表现明显优于 SUVR 方法（Tau 的效能增加：CN+，>3.2 倍；MCI+，>2.2 倍；痴呆+，>2.9 倍）。Tau 作为由 Tau 计算得出的方法，为 tau PET 数据的定量提供了一种优越的方法。特别是，它为纵向分析和 tau 沉积的早期检测提供了显著的效能提高，因此对于正在研究新型疗法对 tau 沉积衰减的 AD 临床成像试验应该具有重要价值。
