Objective: Automated voxel-based analysis methods are used to detect cortical hypometabolism typical of Alzheimer’s Disease (AD) on FDG-PET brain scans. We compared the accuracy of two clinically validated tools for their ability to identify those MCI subjects progressing to AD at followup, to evaluate the impact of the analysis method on FDG-PET diagnostic performance.

Methods: SPMGrid and BRASS (Hermes Medical Solutions, Stockholm, Sweden) were tested on 131 MCI and elderly healthy controls from the EADC PET dataset. The concordance between the tools was tested by correlating the quantitative parameters (z- and t-values), calculated by the two software tools, and by measuring the topographical overlap of the abnormal regions (Dice score). Three independent expert readers blindly assigned a diagnosis based on the two map sets. We used conversion to AD dementia as the gold standard.

Results: The t-map and z-map calculated with SPMGrid and BRASS, respectively, showed a good correlation (R > .50) for the majority of individual cases (128/131) and for the majority of selected regions of interest (ROIs) (98/116). The overlap of the hypometabolic patterns from the two tools was, however, poor (Dice score .36). The diagnostic performance was comparable, with BRASS showing significantly higher sensitivity (.82 versus .59) and SPMGrid showing higher specificity (.87 versus .52).

Conclusion: Despite similar diagnostic performance in predicting conversion to AD in MCI subjects, the two tools showed significant differences, and the maps provided by the tools showed limited overlap. These results underline the urgency for standardization across FDG-PET analysis methods for their use in clinical practice.

目标：基于自动体素的分析方法用于检测 FDG-PET 脑部扫描中阿尔茨海默病 (AD) 典型的皮质低代谢。我们比较了两种经过临床验证的工具在后续识别那些进展为 AD 的 MCI 受试者的能力的准确性，以评估分析方法对 FDG-PET 诊断性能的影响。

方法：SPMGrid 和 BRASS（Hermes Medical Solutions，斯德哥尔摩，瑞典）在来自 EADC PET 数据集的 131 MCI 和老年人健康对照中进行了测试。通过关联由两个软件工具计算的定量参数（z 和 t 值）以及测量异常区域的地形重叠（Dice 分数）来测试工具之间的一致性。三位独立的专家读者根据两个地图集盲目地分配了诊断。我们使用转化为 AD 痴呆作为金标准。

结果：分别使用 SPMGrid 和 BRASS 计算的 t-map 和 z-map 对大多数个体案例 (128/131) 和大多数选定的感兴趣区域 (ROIs) 显示出良好的相关性 (R > .50) ) (98/116)。然而，来自两种工具的低代谢模式的重叠很差（Dice 得分为 0.36）。诊断性能相当，BRASS 显示出更高的灵敏度（0.82 对 0.59），SPMGrid 显示出更高的特异性（0.87 对 0.52）。

结论：尽管在预测 MCI 受试者转换为 AD 方面的诊断性能相似，但两种工具显示出显着差异，并且工具提供的地图显示重叠有限。这些结果强调了将 FDG-PET 分析方法标准化以用于临床实践的紧迫性。