Department of Neurology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong-si, Gyeonggi-do, Republic of Korea.
Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
Sci Rep. 2021 Mar 11;11(1):5706. doi: 10.1038/s41598-021-85165-x.
We developed machine learning (ML) algorithms to predict abnormal tau accumulation among patients with prodromal AD. We recruited 64 patients with prodromal AD using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Supervised ML approaches based on the random forest (RF) and a gradient boosting machine (GBM) were used. The GBM resulted in an AUC of 0.61 (95% confidence interval [CI] 0.579-0.647) with clinical data (age, sex, years of education) and a higher AUC of 0.817 (95% CI 0.804-0.830) with clinical and neuropsychological data. The highest AUC was 0.86 (95% CI 0.839-0.885) achieved with additional information such as cortical thickness in clinical data and neuropsychological results. Through the analysis of the impact order of the variables in each ML classifier, cortical thickness of the parietal lobe and occipital lobe and neuropsychological tests of memory domain were found to be more important features for each classifier. Our ML algorithms predicting tau burden may provide important information for the recruitment of participants in potential clinical trials of tau targeting therapies.
我们开发了机器学习（ML）算法，以预测前驱期 AD 患者中异常 tau 积累。我们使用阿尔茨海默病神经影像学倡议（ADNI）数据集招募了 64 名前驱期 AD 患者。使用基于随机森林（RF）和梯度提升机（GBM）的监督 ML 方法。GBM 结合临床数据（年龄、性别、受教育年限）的 AUC 为 0.61（95%置信区间[CI] 0.579-0.647），结合临床和神经心理学数据的 AUC 更高为 0.817（95% CI 0.804-0.830）。在临床数据和神经心理学结果中增加皮质厚度等额外信息，AUC 最高可达 0.86（95% CI 0.839-0.885）。通过分析每个 ML 分类器中变量的影响顺序，发现顶叶和枕叶皮质厚度以及记忆域的神经心理学测试是每个分类器的更重要特征。我们预测 tau 负荷的 ML 算法可能为 tau 靶向治疗的潜在临床试验中的参与者招募提供重要信息。
