一种用于区分阿尔茨海默病（AD）、轻度认知障碍（MCI）和正常对照（NC）的新型深度学习放射组学模型：基于阿尔茨海默病神经影像学倡议（ADNI）的tau正电子发射断层扫描（PET）的探索性研究。
A Novel Deep Learning Radiomics Model to Discriminate AD, MCI and NC: An Exploratory Study Based on Tau PET Scans from ADNI.
作者信息
Zhao Yan, Zhang Jieming, Chen Yue, Jiang Jiehui
机构信息
Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou 646000, China.
Department of Nuclear Medicine, Affiliated Hospital of Southwest Medical University, Luzhou 646000, China.
出版信息
Brain Sci. 2022 Aug 12;12(8):1067. doi: 10.3390/brainsci12081067.
OBJECTIVE
We explored a novel model based on deep learning radiomics (DLR) to differentiate Alzheimer's disease (AD) patients, mild cognitive impairment (MCI) patients and normal control (NC) subjects. This model was validated in an exploratory study using tau positron emission tomography (tau-PET) scans.
METHODS
In this study, we selected tau-PET scans from the Alzheimer's Disease Neuroimaging Initiative database (ADNI), which included a total of 211 NC, 197 MCI, and 117 AD subjects. The dataset was divided into one training/validation group and one separate external group for testing. The proposed DLR model contained the following three steps: (1) pre-training of candidate deep learning models; (2) extraction and selection of DLR features; (3) classification based on support vector machine (SVM). In the comparative experiments, we compared the DLR model with three traditional models, including the SUVR model, traditional radiomics model, and a clinical model. Ten-fold cross-validation was carried out 200 times in the experiments.
RESULTS
Compared with other models, the DLR model achieved the best classification performance, with an accuracy of 90.76% ± 2.15% in NC vs. MCI, 88.43% ± 2.32% in MCI vs. AD, and 99.92% ± 0.51% in NC vs. AD.
CONCLUSIONS
Our proposed DLR model had the potential clinical value to discriminate AD, MCI and NC.
目的
我们探索了一种基于深度学习放射组学（DLR）的新型模型，以区分阿尔茨海默病（AD）患者、轻度认知障碍（MCI）患者和正常对照（NC）受试者。该模型在一项使用tau正电子发射断层扫描（tau-PET）的探索性研究中得到了验证。
方法
在本研究中，我们从阿尔茨海默病神经影像学倡议数据库（ADNI）中选择了tau-PET扫描数据，其中包括总共211名NC受试者、197名MCI受试者和117名AD受试者。数据集被分为一个训练/验证组和一个单独的外部测试组。所提出的DLR模型包含以下三个步骤：（1）候选深度学习模型的预训练；（2）DLR特征的提取和选择；（3）基于支持向量机（SVM）的分类。在比较实验中，我们将DLR模型与三种传统模型进行了比较，包括标准化摄取值比（SUVR）模型、传统放射组学模型和临床模型。在实验中进行了200次十折交叉验证。
结果
与其他模型相比，DLR模型取得了最佳的分类性能，在NC与MCI之间的准确率为90.76%±2.15%，在MCI与AD之间的准确率为88.43%±2.32%，在NC与AD之间的准确率为99.92%±0.51%。
结论
我们提出的DLR模型在区分AD、MCI和NC方面具有潜在的临床价值。
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