Quantitative Radiomic Features as New Biomarkers for Alzheimer's Disease: An Amyloid PET Study | |
Ding, Yanhui1; Zhao, Kun2,3,4; Che, Tongtong2; Du, Kai3,4,5,6; Sun, Hongzan7; Liu, Shu3,4,6; Zheng, Yuanjie1; Li, Shuyu2; Liu, Bing3,4,5,6; Liu, Yong3,4,5,6,8,9 | |
发表期刊 | CEREBRAL CORTEX |
ISSN | 1047-3211 |
2021-08-01 | |
卷号 | 31期号:8页码:3950-3961 |
通讯作者 | Liu, Yong(yongliu@bupt.edu.cn) |
摘要 | Growing evidence indicates that amyloid-beta (A beta) accumulation is one of the most common neurobiological biomarkers in Alzheimer's disease (AD). The primary aim of this study was to explore whether the radiomic features of A beta positron emission tomography (PET) images are used as predictors and provide a neurobiological foundation for AD. The radiomics features of A beta PET imaging of each brain region of the Brainnetome Atlas were computed for classification and prediction using a support vector machine model. The results showed that the area under the receiver operating characteristic curve (AUC) was 0.93 for distinguishing AD (N=291) from normal control (NC; N= 334). Additionally, the AUC was 0.83 for the prediction of mild cognitive impairment (MCI) converting (N=88) (vs. no conversion, N=100) to AD. In the MCI and AD groups, the systemic analysis demonstrated that the classification outputs were significantly associated with clinical measures (apolipoprotein E genotype, polygenic risk scores, polygenic hazard scores, cerebrospinal fluid A beta, and Tau, cognitive ability score, the conversion time for progressive MCI subjects and cognitive changes). These findings provide evidence that the radiomic features of A beta PET images can serve as new biomarkers for clinical applications in AD/MCI, further providing evidence for predicting whether MCI subjects will convert to AD. |
关键词 | Alzheimer's disease biomarker machine learning prediction quantitative radiomic features |
DOI | 10.1093/cercor/bhab061 |
关键词[WOS] | MILD COGNITIVE IMPAIRMENT ; POLYGENIC HAZARD SCORE ; CLASSIFICATION ; RISK ; METAANALYSIS ; IMAGES ; ATLAS |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Beijing Natural Science Funds for Distinguished Young Scholars[JQ200036] ; National Natural Science Foundation of China[81871438] ; National Natural Science Foundation of China[81901101] ; Primary Research & Development Plan of Shandong Province[2017GGX10112] ; Natural Science Foundation of Shandong Province[ZR2020MF051] |
项目资助者 | Beijing Natural Science Funds for Distinguished Young Scholars ; National Natural Science Foundation of China ; Primary Research & Development Plan of Shandong Province ; Natural Science Foundation of Shandong Province |
WOS研究方向 | Neurosciences & Neurology |
WOS类目 | Neurosciences |
WOS记录号 | WOS:000674446400027 |
出版者 | OXFORD UNIV PRESS INC |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/45515 |
专题 | 脑网络组研究 |
通讯作者 | Liu, Yong |
作者单位 | 1.Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250014, Peoples R China 2.Beihang Univ, Sch Biol Sci & Med Engn, Beijing 100191, Peoples R China 3.Chinese Acad Sci, Brainnetome Ctr, Inst Automat, Beijing 100190, Peoples R China 4.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China 5.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Inst Automat, Beijing 100190, Peoples R China 6.Chinese Acad Sci, Univ Chinese Acad Sci, Beijing 100049, Peoples R China 7.China Med Univ, Dept Radiol, Shengjing Hosp, Shenyang 110004, Peoples R China 8.Pazhou Lab, Guangzhou 510330, Peoples R China 9.Beijing Univ Posts & Telecommun, Sch Artificial Intelligence, Beijing 100876, Peoples R China |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Ding, Yanhui,Zhao, Kun,Che, Tongtong,et al. Quantitative Radiomic Features as New Biomarkers for Alzheimer's Disease: An Amyloid PET Study[J]. CEREBRAL CORTEX,2021,31(8):3950-3961. |
APA | Ding, Yanhui.,Zhao, Kun.,Che, Tongtong.,Du, Kai.,Sun, Hongzan.,...&Liu, Yong.(2021).Quantitative Radiomic Features as New Biomarkers for Alzheimer's Disease: An Amyloid PET Study.CEREBRAL CORTEX,31(8),3950-3961. |
MLA | Ding, Yanhui,et al."Quantitative Radiomic Features as New Biomarkers for Alzheimer's Disease: An Amyloid PET Study".CEREBRAL CORTEX 31.8(2021):3950-3961. |
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