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首页> 外文会议>Annual International Conference of the IEEE Engineering in Medicine and Biology Society >Radiomics features as predictors to distinguish fast and slow progression of Mild Cognitive Impairment to Alzheimer's disease
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Radiomics features as predictors to distinguish fast and slow progression of Mild Cognitive Impairment to Alzheimer's disease

机译：辐射瘤功能作为预测因子，以区分快速和缓慢进展对阿尔茨海默病的轻微认知障碍

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Prediction of Alzheimer's disease (AD) from Mild Cognitive Impairment (MCI) by analyzing Magnetic Resonance Imaging (MRI) image features has become popular in recent years. However, defining effective predictive biomarkers is still challengeable. The 'radiomics' is an established method to identify advanced and high order quantitative imaging features for computer-aided diagnosis and has been applied into oncology study. However, it has not been applied into brain disorder disease study. Therefore, the purpose of this study is to identify whether the features from radiomics could be the predictors of the conversion from MCI to AD. We analyzed 197 samples with MRI scans from the ADNI database, which contained 32 healthy subjects and 165 MCI patients. Firstly, we extracted 215 radiomics features from hippocampus. Then we used Cronbach's alpha coefficient, the intra-class correlation coefficient, Kaplan-Meier model and cox regression to select 44 radiomics features as effective features. Finally, we used SVM classification to validate these features. The results showed that the classification accuracy using linear, polynomial and sigmoid kernel could achieve 80.0%, 93.3% and 86.6% to distinguish MCI-to-AD fast and slow converter. As a result, this study indicated that the radiomics features are potential to be applied into predicting AD from MCI.
机译：阿尔茨海默通过分析磁共振成像（MRI）图像特征从轻度认知障碍（MCI）病（AD）的预测在近几年变得流行。然而，定义有效的预测生物标志物仍然是有挑战性的。在“radiomics”是一个既定的方法，以确定用于计算机辅助诊断先进和高阶定量成像的特征和已经施加到肿瘤学研究。然而，它并没有被应用到脑功能障碍疾病的研究。因此，本研究的目的是确定从radiomics特征是否可能是从MCI到AD转换的预测因子。我们分析了197个样本MRI来自ADNI数据库，其中载有32名健康受试者和165名MCI患者进行扫描。首先，我们从提取海马215种radiomics功能。然后我们使用Cronbach的α系数时，组内相关系数，卡普兰 - 迈耶模型和Cox回归来选择44个radiomics设有作为有效特征。最后，我们使用SVM分类，以验证这些功能。结果表明，使用线性，多项式和乙状结肠内核可以达到80.0％，93.3％和86.6％的分类准确度来区分MCI到AD快速和慢速转换器。其结果是，本研究表明，radiomics特征可能被应用到从MCI预测AD。

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