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Prediction of MCI to AD Risk of Conversion Survival Models: qMRI vs CSF Measures and Cognitive Assessments

机译:MCI到AD转换生存模型的风险预测:qMRI与CSF量度和认知评估

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Several studies have found that different quantitative MRI (qMRI) measurements are associated with the presence of Alzheimer's disease. Cognitive Assessment scores, Apolipoprotein E4 (ApoE4) and cerebrospinal fluid (CSF) biomarkcrs are important factors associated with the risk of conversion from MCI to AD. Despite this awareness, the relationship of the qMRI measurements with the conversion rate and their effect in multivariate survival models that combine Radiomics, CSF, ApoE4 and Cognitive assessment is not known. The objective of this work was to evaluate the importance of each data source using several machine learnirig(ML) approaches that build Cox Survival models that combine cognitive assessments, CSF, ApoE4 and qMRI features. 321 features from 442 subjects from the ADNI study that converted from the MCI status to AD were used. ML methods were explored in a Cross-validation framework. Test results indicated that cognitive assessments plus qMRI data produce Cox survival models that are 92% concordant with the conversion time from MCI to AD, while CSF biomarkcrs did not have a mayor contribution on the final survival Model.
机译:几项研究发现,不同的定量MRI(qMRI)测量与阿尔茨海默氏病的存在有关。认知评估评分,载脂蛋白E4(ApoE4)和脑脊液(CSF)生物标志物是与从MCI转化为AD的风险相关的重要因素。尽管有这种认识,qMRI测量值与转换率的关系及其在结合Radiomics,CSF,ApoE4和认知评估的多元生存模型中的作用尚不清楚。这项工作的目的是使用几种机器学习方法(ML)来评估每个数据源的重要性,这些方法构建结合了认知评估,CSF,ApoE4和qMRI功能的Cox生存模型。使用了来自ADNI研究的442个受试者的321个特征,这些特征从MCI状态转换为AD。在交叉验证框架中探索了机器学习方法。测试结果表明,认知评估和qMRI数据可产生Cox生存模型,与从MCI到AD的转换时间一致,达92%,而CSF生物标志物对最终生存模型没有贡献。

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