Habert Marie-Odile, Bertin Hugo, Labit Mickael, Diallo Mamadou, Marie Sullivan, Martineau Kelly, Kas Aurélie, Causse-Lemercier Valérie, Bakardjian Hovagim, Epelbaum Stéphane, Chételat Gael, Houot Marion, Hampel Harald, Dubois Bruno, Mangin Jean-François
Centre pour l'Acquisition et le Traitement des Images, Saclay, Paris, France.
Département de Médecine Nucléaire, Hôpital de la Pitié-Salpêtrière, AP-HP, Paris, France.
Ann Nucl Med. 2018 Feb;32(2):75-86. doi: 10.1007/s12149-017-1221-0. Epub 2017 Dec 7.
Our aim is to validate the process steps implemented by the French CATI platform to assess amyloid status, obtained from 18F-Florbetapir PET scans, in a cohort of 318 cognitively normal subjects participating in the INSIGHT-preAD study. Our objective was to develop a method with partial volume effect correction (PVEC) on untransformed PET images, using an automated pipeline ("RACHEL") adapted to large series of patients and including quality checks of results.
We compared RACHEL using different options (with and without PVEC, different sets of regions of interest), to two other methods validated in the literature, referred as the "AVID" and "CAEN" methods. A standard uptake value ratio (SUVR) was obtained with the different methods for participants to another French study, IMAP, including 26 normal elderly controls (NEC), 11 patients with mild cognitive impairment (MCI) and 16 patients with Alzheimer's disease (AD). We determined two cutoffs for RACHEL method by linear correlation with the other methods and applied them to the INSIGHT-preAD subjects.
RACHEL including PVEC and a combination of the whole cerebellum and the pons as a reference region allowed the best discrimination between NEC and AD participants. A strong linear correlation was found between RACHEL and the other two methods and yielded the two cutoffs of 0.79 and 0.88. According to the more conservative threshold, 19.8% of the INSIGHT-preAD subjects would be considered amyloid positive, and 27.7% according to the more liberal threshold.
With our method, we clearly discriminated between NEC with negative amyloid status and patients with clinical AD. Using a linear correlation with other validated cutoffs, we could infer our own positivity thresholds and apply them to an independent population. This method might be useful to the community, especially when the optimal cutoff could not be obtained from a population of healthy young adults or from correlation with post-mortem results.
我们的目标是验证法国计算机辅助电话调查（CATI）平台实施的用于评估淀粉样蛋白状态的流程步骤，该状态通过18F-氟代硼吡咯正电子发射断层扫描（PET）获得，研究对象为参与INSIGHT-preAD研究的318名认知正常的受试者。我们的目标是开发一种对未转换的PET图像进行部分容积效应校正（PVEC）的方法，使用适用于大量患者的自动化流程（“RACHEL”），并包括结果的质量检查。
我们将使用不同选项（有或无PVEC、不同的感兴趣区域集）的RACHEL与文献中验证的另外两种方法（称为“AVID”和“CAEN”方法）进行比较。对于参与另一项法国研究IMAP的参与者，用不同方法获得标准摄取值比率（SUVR），IMAP研究包括26名正常老年对照（NEC）、11名轻度认知障碍（MCI）患者和16名阿尔茨海默病（AD）患者。我们通过与其他方法的线性相关确定RACHEL方法的两个临界值，并将其应用于INSIGHT-preAD受试者。
包括PVEC以及将整个小脑和脑桥组合作为参考区域的RACHEL能够最好地区分NEC和AD参与者。在RACHEL与其他两种方法之间发现了很强的线性相关性，并得出0.79和0.88这两个临界值。根据更保守的阈值，19.8%的INSIGHT-preAD受试者将被视为淀粉样蛋白阳性，根据更宽松的阈值则为27.7%。
通过我们的方法，我们能够清楚地区分淀粉样蛋白状态为阴性的NEC和临床AD患者。通过与其他验证的临界值进行线性相关，我们可以推断出自己的阳性阈值并将其应用于独立人群。这种方法可能对该领域有用，特别是当无法从健康年轻成年人人群中获得最佳临界值或无法与尸检结果进行相关性分析时。
