Jiang Shaofan, Yang Siyu, Deng Kaiji, Jiang Rifeng, Xue Yunjing
Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China.
Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumors, Fujian Medical University, Fuzhou, China.
Front Aging Neurosci. 2024 Oct 9;16:1434589. doi: 10.3389/fnagi.2024.1434589. eCollection 2024.
This study aimed to develop and validate machine learning models (MLMs) to diagnose Alzheimer's disease (AD) using cortical complexity indicated by fractal dimension (FD).
A total of 296 participants with normal cognitive (NC) function and 182 with AD from the AD Neuroimaging Initiative database were randomly divided into training and internal validation cohorts. Then, FDs, demographic characteristics, baseline global cognitive function scales [Montreal Cognitive Assessment (MoCA), Functional Activities Questionnaire (FAQ), Global Deterioration Scale (GDS), Neuropsychiatric Inventory (NPI)], phospho-tau (p-tau 181), amyloidβ-42/40, apolipoprotein E (APOE) and polygenic hazard score (PHS) were collected to establish multiple MLMs. Receiver operating characteristic curves were used to evaluate model performance. Participants from our institution ( = 66; 33 with NC and 33 with AD) served as external validation cohorts to validate the MLMs. Decision curve analysis was used to estimate the models' clinical values.
The FDs from 30 out of 69 regions showed significant alteration. All MLMs were conducted based on the 30 significantly different FDs. The FD model had good accuracy in predicting AD in three cohorts [area under the receiver operating characteristic (ROC) curve (AUC) = 0.842, 0.808, and 0.803]. There were no statistically significant differences in AUC values between the FD model and the other combined models in the training and internal validation cohorts except MoCA + FD and FAQ + FD models. Among MLMs, the MoCA + FD model showed the best predictive efficiency in three cohorts (AUC = 0.951, 0.931, and 0.955) and had the highest clinical net benefit.
The FD model showed favorable diagnostic performance for AD. Among MLMs, the MoCA + FD model can predict AD with the highest efficiency and could be used as a non-invasive diagnostic method.
本研究旨在开发并验证机器学习模型（MLMs），以利用分形维数（FD）表示的皮质复杂性来诊断阿尔茨海默病（AD）。
从AD神经影像倡议数据库中选取296名认知功能正常（NC）的参与者和182名AD患者，随机分为训练队列和内部验证队列。然后，收集FDs、人口统计学特征、基线整体认知功能量表[蒙特利尔认知评估（MoCA）、功能活动问卷（FAQ）、总体衰退量表（GDS）、神经精神量表（NPI）]、磷酸化tau蛋白（p-tau 181）、淀粉样β蛋白42/40、载脂蛋白E（APOE）和多基因风险评分（PHS），以建立多个MLMs。采用受试者工作特征曲线评估模型性能。来自本机构的参与者（n = 66；33名NC参与者和33名AD患者）作为外部验证队列来验证MLMs。采用决策曲线分析评估模型的临床价值。
69个区域中的30个区域的FDs显示出显著变化。所有MLMs均基于这30个显著不同的FDs构建。FD模型在三个队列中预测AD具有良好的准确性[受试者工作特征（ROC）曲线下面积（AUC）= 0.842、0.808和0.803]。在训练队列和内部验证队列中，除MoCA + FD模型和FAQ + FD模型外，FD模型与其他联合模型的AUC值无统计学显著差异。在MLMs中，MoCA + FD模型在三个队列中显示出最佳预测效率（AUC = 0.951、0.931和0.955），且临床净效益最高。
FD模型对AD显示出良好的诊断性能。在MLMs中，MoCA + FD模型能够以最高效率预测AD，可作为一种非侵入性诊断方法。
