融合影像、神经认知评价和生物标志等多模态数据预测阿尔兹海默症进展阶段及转化
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安徽省自然科学基金面上项目(2008085MA09);安徽高校自然科学研究重点项目(KJ2019A0238)


Improving prediction accuracy of early diagnosis of mild cognitive impairment and conversion to dementia by integration of cognitive assessment, biomarkers and brain image data
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    目的:研究如何整合并优化影像、神经认知评价和生物标志测量等多来源多模态数据以提高阿尔兹海默症(Alzheimer disease,AD)发展阶段和转化的分类预测准确率。方法:基于阿尔兹海默症影像计划(Alzheimer’s disease neuroimaging initiative,ADNI)2004—2018年4个阶段的样本数据,包括从核磁共振成像(magnetic resonance imaging,MRI)影像数据提取的脑图像特征数据、神经认知量表(简易精神状态测量量表和ADAS-Cog13量表)数据、生物标志测量数据(Abeta、Tau和p-Tau蛋白及ApoE4基因型)。基于783个样本的基线数据建立二分类和多分类Logistic回归模型用于疾病发展阶段的两两和同时分类预测。基于具有轻度认知障碍(mild cognitive impairment,MCI)状态的352个样本的纵向数据建立二分类Logistic回归模型并用于转化状态的分类预测。将脑图像特征变量、量表数据和生物标志加入到基准模型中,通过交叉验证方法随机划分数据集,并计算准确率、查准率、召回率、F1得分和ROC曲线下面积(area under curve,AUC)等指标进行综合比较,得到最优多模态组合的分类预测模型。结果:对于AD发展阶段的分类,结合脑图像特征数据、量表数据和生物标志数据建立二分类Logistic模型表现最佳,区分AD组和正常组、MCI组和正常组以及AD组和MCI组的准确率分别达到了100.00%、77.18%和89.58%;AUC值分别为100.00%、85.52%和96.39%,比仅用脑图像数据进行进展阶段的分类预测有显著提高。对于MCI是否转化的分类预测,脑图像特征数据结合量表数据和生物标志能最大限度地提高准确率,从86.69%提高到90%以上;相应的AUC值从89.21%提高到94.06%。结论:结合多来源数据能提高AD疾病进展阶段和转化的分类预测准确率,为临床诊断AD所处的发展阶段和转化提供理论上的支持。

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    Objective:To determine whether the combination of neurocognitive assessment and key biomarker data can improve the accuracy of using MRI image data to predict Alzheimer disease(AD) status and conversion. Methods:Data were collected from the Alzheimer’s Disease Neuroimaging Initiative(ADNI) during 2004—2018. Samples with complete MRI image data,cognitive assessment data and biological measures were screened from the raw data. Seven brain volumetric features including ventricular,hippocampus,whole brain,entorhinal cortex,fusiform gyrus,middle temporal gyrus,and intracerebral volumes were extracted from MRI by toolbox FreeSurfer. Cognitive assessment scale included the MMSE and ADAS-Cog13 scale. Biological measurement data included four biomarkers,i.e.,Abeta peptide,Tau protein,p-Tau protein and ApoE4 genotype. Based on the baseline data of 783 samples,logistic regression model was established for classification of disease development stages. Based on the longitudinal data of 352 samples with MCI status,a binary logistic regression was established for distinguishing converted patients from non-converted patients. We integrated cognitive data,and biomarkers with the brain image data,randomly divided the data set through a cross-validation method,and calculated accuracy,recall,precision,F1 score,and the area under the ROC curve. Results:For the classification of AD development stage,combining brain image data,cognitive data and biological measures achieved accuracy rates of 100.00%(AD vs. Normal),77.18%(MCI vs. Normal) and 89.58%(AD vs. MCI); the areas under the ROC curves are 100.00%(AD vs. Normal),85.52%(MCI vs. Normal) and 96.39%(AD vs. MCI) respectively;the AUCs for distinguishing Normal,MCI,and AD from the other two categories are 88.30%,81.00% and 97.26% respectively,which are significantly higher than the classification performance using only brain image data. For classification of MCI conversion,the brain image data combined with the cognitive data can maximize the accuracy rate,from 86.69% to more than 90%;the corresponding AUC increased from 89.21%,which only use the brain image data to 94.06%. Conclusion:Combining data from multiple sources can improve the classification and prediction accuracy of AD status and conversion,thus provide theoretical support for clinical practice in early diagnosis of the AD.

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智丽萍,祝 昭,袁 敏.融合影像、神经认知评价和生物标志等多模态数据预测阿尔兹海默症进展阶段及转化[J].南京医科大学学报(自然科学版),2022,42(4):522-528

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  • 收稿日期:2021-08-12
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  • 在线发布日期: 2022-04-25
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