目的基于tau IQ算法，建立一种基于Braak分期纵向且不涉及β-淀粉样蛋白(Aβ)显像的体素级别量化方法，以实现特异性tau量化。
方法该横断面研究纳入2018年11月至2020年7月间复旦大学附属华山医院核医学/PET中心的92例受试者[男35例、女57例，年龄(62.9±10.4)岁]，其中认知正常(CN)者28名、轻度认知障碍(MCI)患者20例、阿尔茨海默病(AD)患者44例。所有受试者采集 18F-florzolotau PET图像及简易精神状态检查量表(MMSE)和临床痴呆评定量表(CDR)评分。通过Braak分期构建tau纵向数据集，采用logistic回归进行体素级别拟合得到基准矩阵，最后通过最小二乘法对矩阵进行分解，得到特异性沉淀系数Tau load。采用单因素方差分析(事后检验为Tukey法)比较组间Tau load、SUV比值(SUVR)差异，采用ROC曲线分析CN、MCI与AD两两组间的分类能力，采用Spearman秩相关分析Tau load、SUVR与MMSE评分、CDR评分之间的相关性。
结果CN组的Tau load接近于0，并显著低于MCI组与AD组( F＝55.03， P<0.001；事后检验均 P<0.001)，各ROI的SUVR差异也均有统计学意义( F值：36.46～55.38，均 P<0.001)；相较于SUVR，Tau load显示出更大的组间差异。在CN、MCI与AD两两组间的ROC曲线分析中，Tau load的AUC一直保持最高(0.754～1.000)。Tau load及各ROI的SUVR与MMSE评分呈负相关性( r s 值：－0.698～－0.583，均 P<0.05)，与CDR评分呈正相关性( r s 值：0.648～0.783，均 P<0.05)，其中Tau load的相关系数绝对值最高。
结论相对于传统半定量SUVR方法，Braak-tau IQ算法不需要特定参考脑区也可以实现特异性tau量化性能。
ObjectiveA voxel-level quantification method based on the tau IQ algorithm and Braak staging, excluding β-amyloid (Aβ) imaging, was developed to achieve specific tau quantification.
MethodsThis cross-sectional study included 92 subjects (35 males, 57 females; age (62.9±10.4) years) from the Nuclear Medicine/PET Center of Huashan Hospital, Fudan University between November 2018 and July 2020. The cohort comprised 28 cognitively normal (CN) individuals, 20 patients with mild cognitive impairment (MCI), and 44 patients with Alzheimer′s disease (AD). All participants underwent 18F-florzolotau PET imaging, Mini-Mental State Examination (MMSE), and Clinical Dementia Rating (CDR) scoring. A longitudinal tau dataset was constructed based on Braak staging. Voxel-level logistic regression fitting provided a baseline matrix, decomposed via least squares to yield the Tau load coefficient. One-way analysis of variance (with post hoc Tukey) was used to compare Tau load and SUV ratio (SUVR) among groups. ROC curve analysis was used to evaluate classification between CN, MCI and AD. Spearman rank correlation was used to assess the relationships between Tau load, SUVR, and MMSE scores or CDR scores.
ResultsThe Tau load in the CN group was close to 0 and significantly lower than that in the MCI and AD groups ( F=55.03, P<0.001; post hoc tests all P<0.001). Significant differences were also observed in the SUVR across all ROIs ( F values: 36.46-55.38, all P<0.001). Compared to SUVR, Tau load demonstrated greater intergroup differences. In ROC curve analyses between each pair of CN, MCI, and AD groups, Tau load consistently achieved the highest AUC (0.754-1.000). Both Tau load and SUVR for each ROI were negatively correlated with MMSE scores ( r s values: from -0.698 to -0.583, all P<0.05) and positively correlated with CDR scores ( r s values: 0.648-0.783, all P<0.05), with Tau load showing the highest absolute correlation coefficients.
ConclusionCompared to the traditional semi-quantitative SUVR method, the Braak-tau IQ algorithm does not require a specific reference brain region to achieve specific tau quantification.
门建炜,石蓉,王敏,等. Braak-tau IQ：一种适用于阿尔茨海默病tau PET图像的量化分解方法 [J]. 中华核医学与分子影像杂志,2024,44(12)：718-723.
DOI:10.3760/cma.j.cn321828-20240122-00030版权归中华医学会所有。
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门建炜：研究实施、统计学分析、论文撰写；石蓉：算法指导、论文撰写；王敏、张琪、鲁佳荧、张慧玮：研究实施、论文修改；赵倩华、蒋皆恢：研究指导、论文修改；左传涛、管一晖：研究指导、论文修改、经费支持
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