Li Yupeng, Jiang Jiehui, Lu Jiaying, Jiang Juanjuan, Zhang Huiwei, Zuo Chuantao
Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China.
Shanghai Institute for Advanced Communication and Data Science, Shanghai University, T Building, 99 ShangDa Road, BaoShan District, Shanghai, 200444, China.
Ther Adv Neurol Disord. 2019 Mar 29;12:1756286419838682. doi: 10.1177/1756286419838682. eCollection 2019.
Alzheimer's disease (AD) is the most common form of progressive and irreversible dementia, and accurate diagnosis of AD at its prodromal stage is clinically important. Currently, computer-aided diagnosis of AD and mild cognitive impairment (MCI) using F-fluorodeoxy-glucose positron emission tomography (F-FDG PET) imaging is usually based on low-level imaging features or deep learning methods, which have difficulties in achieving sufficient classification accuracy or lack clinical significance. This research therefore aimed to implement a new feature extraction method known as radiomics, to improve the classification accuracy and discover high-order features that can reveal pathological information.
In this study, F-FDG PET and clinical assessments were collected in a cohort of 422 individuals [including 130 with AD, 130 with MCI, and 162 healthy controls (HCs)] from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and 44 individuals (including 22 with AD, and 22 HCs) from Huashan Hospital, Shanghai, China. First, we performed a group comparison using a two-sample Student's test to determine the regions of interest (ROIs) based on 30 AD patients and 30 HCs from ADNI cohorts. Second, based on two time scans of 32 HCs from ADNI cohorts, we used Cronbach's alpha coefficient for radiomic feature stability analyses. Pearson's correlation coefficients were regarded as a feature selection criterion, to select effective features associated with the clinical cognitive scale [clinical dementia rating scale in its sum of boxes (CDRSB); Alzheimer's disease assessment scale (ADAS)] with 500-times cross-validation. Finally, a support vector machine (SVM) was used to test the ability of the radiomic features to classify HCs, MCI and AD patients.
As a result, we identified brain regions which were mainly distributed in the temporal, occipital and frontal areas as ROIs. A total of 168 radiomic features of AD were stable (alpha > 0.8). The classification experiment led to maximal accuracies of 91.5%, 83.1% and 85.9% for classifying AD HC, MCI HCs and AD MCI.
The research in this paper proved that the novel approach based on high-order radiomic features extracted from F-FDG PET brain images that can be used for AD and MCI computer-aided diagnosis.
阿尔茨海默病（AD）是进行性且不可逆痴呆最常见的形式，在其前驱期准确诊断AD具有重要临床意义。目前，利用氟代脱氧葡萄糖正电子发射断层扫描（F-FDG PET）成像对AD和轻度认知障碍（MCI）进行计算机辅助诊断通常基于低层次成像特征或深度学习方法，这些方法在实现足够的分类准确性方面存在困难或缺乏临床意义。因此，本研究旨在实施一种称为放射组学的新特征提取方法，以提高分类准确性并发现能够揭示病理信息的高阶特征。
在本研究中，从阿尔茨海默病神经成像计划（ADNI）的422名个体队列[包括130名AD患者、130名MCI患者和162名健康对照（HC）]以及中国上海华山医院的44名个体（包括22名AD患者和22名HC）中收集了F-FDG PET和临床评估数据。首先，我们使用双样本t检验进行组间比较，基于ADNI队列中的30名AD患者和30名HC确定感兴趣区域（ROI）。其次，基于ADNI队列中32名HC的两次时间扫描，我们使用克朗巴哈系数进行放射组学特征稳定性分析。皮尔逊相关系数被视为特征选择标准，通过500次交叉验证选择与临床认知量表[临床痴呆评定量表总分（CDRSB）；阿尔茨海默病评估量表（ADAS）]相关的有效特征。最后，使用支持向量机（SVM）测试放射组学特征对HC、MCI和AD患者进行分类的能力。
结果，我们确定主要分布在颞叶、枕叶和额叶区域的脑区为ROI。AD的168个放射组学特征是稳定的（α>0.8）。分类实验中，AD与HC、MCI与HC以及AD与MCI分类的最大准确率分别为91.5%、83.1%和85.9%。
本文的研究证明，基于从F-FDG PET脑图像中提取的高阶放射组学特征的新方法可用于AD和MCI的计算机辅助诊断。
