Yuan Zengbei, Qi Na, Chen Xing, Luo Yingying, Zhou Zirong, Wang Jie, Wu Junhao, Zhao Jun
Department of Nuclear Medicine, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China.
Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China.
Digit Health. 2025 Apr 22;11:20552076251337183. doi: 10.1177/20552076251337183. eCollection 2025 Jan-Dec.
The progression of Alzheimer's disease (AD) has been shown to significantly correlate with changes in brain tissue structure and leads to cognitive decline and dementia. Using radiomic features derived from brain magnetic resonance imaging (MRI) scan, we can get the help of deep learning (DL) model for diagnosing AD.
This study proposes the use of the DL model under the framework of MR radiomics for AD diagnosis. Two cross-racial independent cohorts from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (141 AD, 166 Mild Cognitive Impairment (MCI), and 231 normal control (NC) subjects) and Huashan hospital (45 AD, 35 MCI, and 31 NC subjects) were enrolled. We first performed preprocessing of MRI using methods such as spatial normalization and denoizing filtering. Next, we conducted Statistical Parametric Mapping analysis based on a two-sample t-test to identify regions of interest and extracted radiomic features using Radiomics tools. Subsequently, feature selection was carried out using the Least Absolute Shrinkage and Selection Operator model. Finally, the selected radiomic features were used to implement the AD diagnosis task with the TabNet model.
The model was quantitatively evaluated using the average values obtained from five-fold cross-validation. In the three-way classification task, the model achieved classification average area under the curve (AUC) of 0.8728 and average accuracy (ACC) of 0.7111 for AD versus MCI versus NC. For the binary classification task, the average AUC values were 0.8778, 0.8864, and 0.9506 for AD versus MCI, MCI versus NC, and AD versus NC, respectively, with average ACC of 0.8667, 0.8556, and 0.9222 for these comparisons.
The proposed model exhibited excellent performance in the AD diagnosis task, accurately distinguishing different stages of AD. This confirms the value of MR DL radiomic model for AD diagnosis.
阿尔茨海默病（AD）的进展已被证明与脑组织结构的变化显著相关，并导致认知衰退和痴呆。利用从脑磁共振成像（MRI）扫描中提取的放射组学特征，我们可以借助深度学习（DL）模型来诊断AD。
本研究提出在MR放射组学框架下使用DL模型进行AD诊断。纳入了来自阿尔茨海默病神经影像倡议（ADNI）数据库（141例AD、166例轻度认知障碍（MCI）和231例正常对照（NC）受试者）以及华山医院（45例AD、35例MCI和31例NC受试者）的两个跨种族独立队列。我们首先使用空间归一化和去噪滤波等方法对MRI进行预处理。接下来，基于双样本t检验进行统计参数映射分析以识别感兴趣区域，并使用放射组学工具提取放射组学特征。随后，使用最小绝对收缩和选择算子模型进行特征选择。最后，使用TabNet模型将所选的放射组学特征用于执行AD诊断任务。
使用从五折交叉验证获得的平均值对模型进行定量评估。在三分类任务中，该模型对AD与MCI与NC的分类平均曲线下面积（AUC）为0.8728，平均准确率（ACC）为0.7111。对于二分类任务，AD与MCI、MCI与NC以及AD与NC的平均AUC值分别为0.8778、0.8864和0.9506，这些比较的平均ACC分别为0.8667、0.8556和0.9222。
所提出的模型在AD诊断任务中表现出优异的性能，能够准确区分AD的不同阶段。这证实了MR DL放射组学模型在AD诊断中的价值。
