Validation of amyloid PET positivity thresholds in centiloids: a multisite PET study approach.
Validation of amyloid PET positivity thresholds in centiloids: a multisite PET study approach.
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蜈蚣样中淀粉样蛋白PET阳性阈值的验证：一项多点PET研究方法。
DOI：
10.1186/s13195-021-00836-1
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发表时间：
2021-05-10
期刊：
影响因子：
--
通讯作者：
Alzheimer’s Disease Neuroimaging Initiative
中科院分区：
文献类型：
--
作者：
Royse SK;Minhas DS;Lopresti BJ;Murphy A;Ward T;Koeppe RA;Bullich S;DeSanti S;Jagust WJ;Landau SM;Alzheimer’s Disease Neuroimaging Initiative
不一致的阳性阈值、图像分析流程和定量结果是在正电子发射断层扫描（PET）中使用一种以上β-淀粉样蛋白（Aβ）放射性示踪剂进行多部位研究的关键挑战。与这些因素相关的变异性导致研究和临床试验中的分歧和缺乏可复制性。为了解决这些问题并促进Aβ PET的协调，我们使用来自阿尔茨海默病神经影像学倡议（ADNI）的[18 F]florbetaben（FBB）和[18 F]florbetapir（FBP）数据来推导（1）标准化的Centiloid（CL）转换和（2）基于单独的年轻对照样本的内部一致的阳性阈值。我们使用本地空间自动化图像处理管道分析了Aβ PET数据，该管道用于许多大型多中心AD研究和试验中的PET定量，并可供研究界使用。有了这个管道，我们使用全球阿尔茨海默氏症协会交互网络数据推导出SUVR到CL的转换;我们使用参考区域进行横截面（整个小脑）和纵向（皮质下白色物质，脑干，整个小脑）分析。最后，我们使用一个独立的年轻对照样本（N=62）开发了一个FBB阳性阈值，其方法与我们现有的FBP阳性阈值平行，并使用数据驱动的方法在ADNI参与者（N=295）中验证了FBB阈值。基于年轻样本的FBB阈值（1.08; 18 CL）与数据驱动方法的阈值（1.10; 21 CL）一致，现有的FBP阈值通过衍生转换转换为CL（1.11; 20 CL）。下面的公式可以用来转换整个小脑-（横截面）和复合材料-（纵向）标准化FBB和FBP数据通过本地空间管道量化为CL单位：[18 F]FBB：CL整个小脑= 157.15 × SUVRFBB-151.87;阈值=1.08，18 CL [18 F]FBP：CL整个小脑= 188.22 × SUVRFBP-189.16;阈值=1.11，20 CL [18 F]FBB：CL复合= 244.20 × SUVRFBB − 170.80 [18 F]FBP：CL复合= 300.66 × SUVRFBP − 208.84 FBB和FBP阳性阈值来自独立的年轻对照样本，并使用自动化的自然空间方法进行定量，得出相似的CL值。这些发现适用于使用该管道分析并与科学界共享的数千个可用和预期结果。这项工作证明了在多部位PET研究中协调PET采集和分析的可行性以及标准化单位中阳性阈值的内部一致性。在线版本包含补充材料，可通过10.1186/s13195-021-00836-1获得。
Inconsistent positivity thresholds, image analysis pipelines, and quantitative outcomes are key challenges of multisite studies using more than one β-amyloid (Aβ) radiotracer in positron emission tomography (PET). Variability related to these factors contributes to disagreement and lack of replicability in research and clinical trials. To address these problems and promote Aβ PET harmonization, we used [18F]florbetaben (FBB) and [18F]florbetapir (FBP) data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to derive (1) standardized Centiloid (CL) transformations and (2) internally consistent positivity thresholds based on separate young control samples. We analyzed Aβ PET data using a native-space, automated image processing pipeline that is used for PET quantification in many large, multisite AD studies and trials and made available to the research community. With this pipeline, we derived SUVR-to-CL transformations using the Global Alzheimer’s Association Interactive Network data; we used reference regions for cross-sectional (whole cerebellum) and longitudinal (subcortical white matter, brain stem, whole cerebellum) analyses. Finally, we developed a FBB positivity threshold using an independent young control sample (N=62) with methods parallel to our existing FBP positivity threshold and validated the FBB threshold using a data-driven approach in ADNI participants (N=295). The FBB threshold based on the young sample (1.08; 18 CL) was consistent with that of the data-driven approach (1.10; 21 CL), and the existing FBP threshold converted to CL with the derived transformation (1.11; 20 CL). The following equations can be used to convert whole cerebellum- (cross-sectional) and composite- (longitudinal) normalized FBB and FBP data quantified with the native-space pipeline to CL units: [18F]FBB: CLwhole cerebellum = 157.15 × SUVRFBB − 151.87; threshold=1.08, 18 CL [18F]FBP: CLwhole cerebellum = 188.22 × SUVRFBP − 189.16; threshold=1.11, 20 CL [18F]FBB: CLcomposite = 244.20 × SUVRFBB − 170.80 [18F]FBP: CLcomposite = 300.66 × SUVRFBP − 208.84 FBB and FBP positivity thresholds derived from independent young control samples and quantified using an automated, native-space approach result in similar CL values. These findings are applicable to thousands of available and anticipated outcomes analyzed using this pipeline and shared with the scientific community. This work demonstrates the feasibility of harmonized PET acquisition and analysis in multisite PET studies and internal consistency of positivity thresholds in standardized units. The online version contains supplementary material available at 10.1186/s13195-021-00836-1.
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DOI：
10.1007/s00259-012-2088-x
发表时间：
2012-06-01
影响因子：
9.1
作者：
Villemagne, Victor L.;Mulligan, Rachel S.;Rowe, Christopher C.
通讯作者：
Rowe, Christopher C.
影响因子：
5.7
作者：
Schwarz CG;Senjem ML;Gunter JL;Tosakulwong N;Weigand SD;Kemp BJ;Spychalla AJ;Vemuri P;Petersen RC;Lowe VJ;Jack CR Jr
通讯作者：
Jack CR Jr
影响因子：
5.7
作者：
Fischl, Bruce
通讯作者：
Fischl, Bruce
影响因子：
11.2
作者：
Klunk, WE;Engler, H;Långström, B
通讯作者：
Långström, B
影响因子：
14
作者：
Carrillo, Maria C.;Rowe, Christopher C.;Head, Richard J.
通讯作者：
Head, Richard J.