European Journal of Nuclear Medicine and Molecular Imaging ( IF 8.6 ) Pub Date : 2022-07-14 , DOI: 10.1007/s00259-022-05879-6 E Doering 1, 2 , M C Hoenig 1, 3 , G N Bischof 1, 3 , K P Bohn 4 , L M Ellingsen 5, 6 , T van Eimeren 1, 7 , A Drzezga 1, 2, 3 ,
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Background
In patients with mild cognitive impairment (MCI), enhanced cerebral amyloid-β plaque burden is a high-risk factor to develop dementia with Alzheimer’s disease (AD). Not all patients have immediate access to the assessment of amyloid status (A-status) via gold standard methods. It may therefore be of interest to find suitable biomarkers to preselect patients benefitting most from additional workup of the A-status. In this study, we propose a machine learning–based gatekeeping system for the prediction of A-status on the grounds of pre-existing information on APOE-genotype 18F-FDG PET, age, and sex.
Methods
Three hundred and forty-two MCI patients were used to train different machine learning classifiers to predict A-status majority classes among APOE-ε4 non-carriers (APOE4-nc; majority class: amyloid negative (Aβ-)) and carriers (APOE4-c; majority class: amyloid positive (Aβ +)) from 18F-FDG-PET, age, and sex. Classifiers were tested on two different datasets. Finally, frequencies of progression to dementia were compared between gold standard and predicted A-status.
Results
Aβ- in APOE4-nc and Aβ + in APOE4-c were predicted with a precision of 87% and a recall of 79% and 51%, respectively. Predicted A-status and gold standard A-status were at least equally indicative of risk of progression to dementia.
Conclusion
We developed an algorithm allowing approximation of A-status in MCI with good reliability using APOE-genotype, 18F-FDG PET, age, and sex information. The algorithm could enable better estimation of individual risk for developing AD based on existing biomarker information, and support efficient selection of patients who would benefit most from further etiological clarification. Further potential utility in clinical routine and clinical trials is discussed.
中文翻译：
在轻度认知障碍中引入用于淀粉样蛋白状态评估的看门系统
背景
在轻度认知障碍 (MCI) 患者中，脑淀粉样蛋白-β 斑块负荷增加是发展为阿尔茨海默病 (AD) 痴呆的高危因素。并非所有患者都能通过金标准方法立即获得淀粉样蛋白状态（A 状态）的评估。因此，可能有兴趣找到合适的生物标志物来预选从额外的 A 状态检查中获益最多的患者。在这项研究中，我们基于 APOE 基因型18F -FDG PET、年龄和性别的预先存在信息，提出了一种基于机器学习的守门系统来预测 A 状态。
方法
三百四十二名 MCI 患者被用来训练不同的机器学习分类器来预测 APOE-ε4 非携带者（APOE4-nc；多数类：淀粉样蛋白阴性（Aβ-））和携带者（APOE4- c；多数类别：来自18 F-FDG-PET、年龄和性别的淀粉样蛋白阳性 (Aβ +)) 。分类器在两个不同的数据集上进行了测试。最后，在金标准和预测的 A 状态之间比较进展为痴呆的频率。
结果
APOE4-nc 中的 Aβ- 和 APOE4-c 中的 Aβ + 的预测精度分别为 87% 和召回率 79% 和 51%。预测的 A 状态和黄金标准 A 状态至少同样表明进展为痴呆症的风险。
结论
我们开发了一种算法，允许使用 APOE 基因型、 18 F-FDG PET、年龄和性别信息来近似 MCI 中的 A 状态，具有良好的可靠性。该算法可以根据现有的生物标志物信息更好地估计个体发展为 AD 的风险，并支持有效选择将从进一步的病因学澄清中获益最多的患者。讨论了在临床常规和临床试验中的进一步潜在效用。
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