Early prediction of the Alzheimer’s disease risk using Tau-PET and machine learning
Early prediction of the Alzheimer’s disease risk using Tau-PET and machine learning
复制标题
使用 Tau-PET 和机器学习早期预测阿尔茨海默病风险
DOI：
10.1117/12.2607990
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Li, Jing
中科院分区：
文献类型：
--
作者：
Wang, Lujia;Zheng, Zhiyang;SU, Yi;Chen, Kewei;Weidman, David A.;Wu, Teresa;Lo, Ben;Lure, Fleming;Li, Jing
阿尔茨海默病（AD）是一种毁灭性的神经退行性疾病。最近在tau-正电子发射断层扫描（PET）成像的进展允许定量和绘制出AD在整个大脑中的一个重要标志的区域分布。有必要开发机器学习（ML）算法来询问这种新的成像模式的实用性。虽然最近有一些研究显示，基于tau-PET图像，使用ML来区分AD患者和正常对照（NC）有希望，但在ML的帮助下，tau-PET是否可以在个体仍处于早期轻度认知障碍（MCI）阶段时促进预测转化为AD的风险，这方面的研究工作有限。我们利用机器学习（ML）开发了一种基于tau-PET的MCI受试者早期AD风险预测器。我们的机器学习算法在预测给定MCI受试者转换为AD的风险方面取得了很好的准确性。有助于预测的重要特征与文献报道的tau易感区域一致。这项工作证明了基于tau-PET和ML为MCI患者开发早期AD风险预测器的可行性。
Alzheimer’s Disease (AD) is a devastating neurodegenerative disease. Recent advances in tau-positron emission tomography (PET) imaging allow quantitating and mapping out the regional distribution of one important hallmark of AD across the brain. There is a need to develop machine learning (ML) algorithms to interrogate the utility of this new imaging modality. While there are some recent studies showing promise of using ML to differentiate AD patients from normal controls (NC) based on tau-PET images, there is limited work to investigate if tau-PET, with the help of ML, can facilitate predicting the risk of converting to AD while an individual is still at the early Mild Cognitive Impairment (MCI) stage. We developed an early AD risk predictor for subjects with MCI based on tau-PET using Machine Learning (ML). Our ML algorithms achieved good accuracy in predicting the risk of conversion to AD for a given MCI subject. Important features contributing to the prediction are consistent with literature reports of tau susceptible regions. This work demonstrated the feasibility of developing an early AD risk predictor for subjects with MCI based on tau-PET and ML.
影响因子：
11.2
作者：
Cho, Hanna;Choi, Jae Yong;Lyoo, Chul Hyoung
通讯作者：
Lyoo, Chul Hyoung