Innovative Multivariable Model Combining MRI Radiomics and Plasma Indexes Predicts Alzheimer’s Disease Conversion:Evidence from a 2-Cohort Longitudinal Study  

作　　者：Xianfeng Yu Xiaoming Sun Min Wei Shuqing Deng Qi Zhang Tengfei Guo Kai Shao Mingkai Zhang Jiehui Jiang Ying Han for the Alzheimer’s Disease Neuroimaging Initiative 

机构地区：[1]Department of Neurology,Xuanwu Hospital of Capital Medical University,Beijing 100053,China [2]Institute of Biomedical Engineering,School of Life Science,Shanghai University,Shanghai 200444,China [3]Institute of Biomedical Engineering,Shenzhen Bay Laboratory,Shenzhen 518132,China.4German Center for Neurodegenerative Diseases(DZNE),53127 Bonn,Germany [4]Center of Alzheimer’s Disease,Beijing Institute for Brain Disorders,Beijing 100069,China [5]National Clinical Research Center for Geriatric Disorders,Beijing 100053,China [6]不详

出　　处：《Research》2024年第4期651-661,共11页研究(英文)

基　　金：supported by the National Natural Science Foundation of China(grants 61633018 and 82020108013).

摘　　要：To explore the complementary relationship between magnetic resonance imaging(MRI)radiomic and plasma biomarkers in the early diagnosis and conversion prediction of Alzheimer’s disease(AD),our study aims to develop an innovative multivariable prediction model that integrates those two for predicting conversion results in AD.This longitudinal multicentric cohort study included 2 independent cohorts:the Sino Longitudinal Study on Cognitive Decline(SILCODE)project and the Alzheimer Disease Neuroimaging Initiative(ADNI).We collected comprehensive assessments,MRI,plasma samples,and amyloid positron emission tomography data.A multivariable logistic regression analysis was applied to combine plasma and MRI radiomics biomarkers and generate a new composite indicator.The optimal model’s performance and generalizability were assessed across populations in 2 cross-racial cohorts.A total of 897 subjects were included,including 635 from the SILCODE cohort(mean[SD]age,64.93[6.78]years;343[63%]female)and 262 from the ADNI cohort(mean[SD]age,73.96[7.06]years;140[53%]female).The area under the receiver operating characteristic curve of the optimal model was 0.9414 and 0.8979 in the training and validation dataset,respectively.A calibration analysis displayed excellent consistency between the prognosis and actual observation.The findings of the present study provide a valuable diagnostic tool for identifying at-risk individuals for AD and highlight the pivotal role of the radiomic biomarker.Importantly,built upon data-driven analyses commonly seen in previous radiomics studies,our research delves into AD pathology to further elucidate the underlying reasons behind the robust predictive performance of the MRI radiomic predictor.

关 键 词：ALZHEIMER prediction COMPLEMENTARY 

分 类 号：R74[医药卫生—神经病学与精神病学]