Department of Neuroscience, Imaging, and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Italy.
Biomedical Unit, ASC27 s.r.l., Rome, Italy.
Neuroscience. 2023 Mar 15;514:143-152. doi: 10.1016/j.neuroscience.2023.01.029. Epub 2023 Feb 2.
In Mild Cognitive Impairment (MCI), identifying a high risk of conversion to Alzheimer's Disease Dementia (AD) is a primary goal for patient management. Machine Learning (ML) algorithms are widely employed to pursue data-driven diagnostic and prognostic goals. An agreement on the stability of these algorithms -when applied to different biomarkers and other conditions- is far from being reached. In this study, we compared the different prognostic performances of three supervised ML algorithms fed with multimodal biomarkers of MCI subjects obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Random Forest, Gradient Boosting, and eXtreme Gradient Boosting algorithms predict MCI conversion to AD. They can also be simultaneously employed -with the voting procedure- to improve predictivity. AD prediction accuracy is influenced by the nature of the data (i.e., neuropsychological test scores, cerebrospinal fluid AD-related proteins and APOE ε4, cerebral structural MRI (sMRI) data). In our study, independent of the applied ML algorithms, sMRI data showed the lowest accuracy (0.79) compared to other classes. Multimodal data were helpful in the algorithms' performances by combining clinical and biological measures. Accordingly, using the three ML algorithms, the highest accuracy (0.90) was reached by employing neuropsychological and AD-related biomarkers. Finally, the feature selection procedure indicated that the most critical variables in the respective classes were the ADAS-Cog-13 scale, the medial temporal lobe and hippocampus atrophy, and the ratio between phosphorylated Tau and Aβ42 proteins. In conclusion, our data support the notion that using multiple ML algorithms and multimodal biomarkers helps make more accurate and solid predictions.
在轻度认知障碍 (MCI) 中，确定向阿尔茨海默病痴呆 (AD) 转化的高风险是患者管理的主要目标。机器学习 (ML) 算法被广泛用于追求数据驱动的诊断和预后目标。然而，在应用于不同的生物标志物和其他情况时，这些算法的稳定性尚未达成共识。在这项研究中，我们比较了三种监督机器学习算法在使用从阿尔茨海默病神经影像学倡议 (ADNI) 数据库获得的 MCI 受试者的多模态生物标志物时的不同预后性能。随机森林、梯度提升和极端梯度提升算法预测 MCI 向 AD 的转化。它们也可以同时使用（投票程序）来提高预测能力。AD 预测准确性受数据性质的影响（即神经心理学测试分数、脑脊液 AD 相关蛋白和 APOE ε4、大脑结构 MRI（sMRI）数据）。在我们的研究中，无论应用哪种 ML 算法，sMRI 数据的准确性都最低（0.79），与其他类别相比。多模态数据通过结合临床和生物学测量，有助于提高算法的性能。因此，使用三种 ML 算法，通过使用神经心理学和 AD 相关生物标志物，达到了最高的准确性（0.90）。最后，特征选择过程表明，在各自类别中最重要的变量是 ADAS-Cog-13 量表、内侧颞叶和海马体萎缩以及磷酸化 Tau 与 Aβ42 蛋白的比值。总之，我们的数据支持这样一种观点，即使用多种 ML 算法和多模态生物标志物有助于做出更准确和可靠的预测。
