Shen Ting, Jiang Jiehui, Lu Jiaying, Wang Min, Zuo Chuantao, Yu Zhihua, Yan Zhuangzhi
Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China.
PET Center, Huashan Hospital, Fudan University, Shanghai, China.
Mol Imaging. 2019 Jan-Dec;18:1536012119877285. doi: 10.1177/1536012119877285.
Accurate diagnosis of early Alzheimer disease (AD) plays a critical role in preventing the progression of memory impairment. We aimed to develop a new deep belief network (DBN) framework using 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) metabolic imaging to identify patients at the mild cognitive impairment (MCI) stage with presymptomatic AD and to discriminate them from other patients with MCI.
18F-fluorodeoxyglucose-PET images of 109 patients recruited in the ongoing longitudinal Alzheimer's Disease Neuroimaging Initiative study were included in this analysis. Patients were grouped into 2 classes: (1) stable mild cognitive impairment (n = 62) or (2) progressive mild cognitive impairment (n = 47). Our framework is composed of 4 steps: (1) image preprocessing: normalization and smoothing; (2) identification of regions of interest (ROIs); (3) feature learning using deep neural networks; and (4) classification by support vector machine with 3 kernels. All classification experiments were performed with a 5-fold cross-validation. Accuracy, sensitivity, and specificity were used to validate the results.
A total of 1103 ROIs were obtained. One hundred features were learned from ROIs using the DBN. The classification accuracy using linear, polynomial, and RBF kernels was 83.9%, 79.2%, and 86.6%, respectively. This method may be a powerful tool for personalized precision medicine in the population with prediction of early AD progression.
早期阿尔茨海默病（AD）的准确诊断对预防记忆障碍进展起着关键作用。我们旨在开发一种新的深度信念网络（DBN）框架，利用18F-氟脱氧葡萄糖（FDG）正电子发射断层扫描（PET）代谢成像来识别处于轻度认知障碍（MCI）阶段且有症状前AD的患者，并将他们与其他MCI患者区分开来。
本分析纳入了正在进行的纵向阿尔茨海默病神经影像学倡议研究中招募的109例患者的18F-氟脱氧葡萄糖-PET图像。患者被分为两类：（1）稳定型轻度认知障碍（n = 62）或（2）进展型轻度认知障碍（n = 47）。我们的框架由4个步骤组成：（1）图像预处理：归一化和平滑；（2）感兴趣区域（ROI）识别；（3）使用深度神经网络进行特征学习；（4）采用具有3种核的支持向量机进行分类。所有分类实验均采用5折交叉验证。使用准确率、敏感性和特异性来验证结果。
共获得1103个ROI。使用DBN从ROI中学习了100个特征。使用线性、多项式和径向基函数（RBF）核的分类准确率分别为83.9%、79.2%和86.6%。该方法可能是预测早期AD进展人群中个性化精准医学的有力工具。
