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Abstract：
This study aimed to explore the utility of hippocampal radiomics using multiparametric simultaneous positron emission tomography (PET)/magnetic resonance imaging (MRI) for early diagnosis of Alzheimer\'s disease (AD).
A total of 53 healthy control (HC) participants, 55 patients with amnestic mild cognitive impairment (aMCI), and 51 patients with AD were included in this study. All participants accepted simultaneous PET/MRI scans, including 18F-fluorodeoxyglucose (18F-FDG) PET, 3D arterial spin labeling (ASL), and high-resolution T1-weighted imaging (3D T1WI). Radiomics features were extracted from the hippocampus region on those three modal images. Logistic regression models were trained to classify AD and HC, AD and aMCI, aMCI and HC respectively. The diagnostic performance and radiomics score (Rad-Score) of logistic regression models were evaluated from 5-fold cross-validation.
The hippocampal radiomics features demonstrated favorable diagnostic performance, with the multimodal classifier outperforming the single-modal classifier in the binary classification of HC, aMCI, and AD. Using the multimodal classifier, we achieved an area under the receiver operating characteristic curve (AUC) of 0.98 and accuracy of 96.7% for classifying AD from HC, and an AUC of 0.86 and accuracy of 80.6% for classifying aMCI from HC. The value of Rad-Score differed significantly between the AD and HC (p < 0.001), aMCI and HC (p < 0.001) groups. Decision curve analysis showed superior clinical benefits of multimodal classifiers compared to neuropsychological tests.
Multiparametric hippocampal radiomics using PET/MRI aids in the identification of early AD, and may provide a potential biomarker for clinical applications.
A total of 53 healthy control (HC) participants, 55 patients with amnestic mild cognitive impairment (aMCI), and 51 patients with AD were included in this study. All participants accepted simultaneous PET/MRI scans, including 18F-fluorodeoxyglucose (18F-FDG) PET, 3D arterial spin labeling (ASL), and high-resolution T1-weighted imaging (3D T1WI). Radiomics features were extracted from the hippocampus region on those three modal images. Logistic regression models were trained to classify AD and HC, AD and aMCI, aMCI and HC respectively. The diagnostic performance and radiomics score (Rad-Score) of logistic regression models were evaluated from 5-fold cross-validation.
The hippocampal radiomics features demonstrated favorable diagnostic performance, with the multimodal classifier outperforming the single-modal classifier in the binary classification of HC, aMCI, and AD. Using the multimodal classifier, we achieved an area under the receiver operating characteristic curve (AUC) of 0.98 and accuracy of 96.7% for classifying AD from HC, and an AUC of 0.86 and accuracy of 80.6% for classifying aMCI from HC. The value of Rad-Score differed significantly between the AD and HC (p < 0.001), aMCI and HC (p < 0.001) groups. Decision curve analysis showed superior clinical benefits of multimodal classifiers compared to neuropsychological tests.
Multiparametric hippocampal radiomics using PET/MRI aids in the identification of early AD, and may provide a potential biomarker for clinical applications.
摘要：
目的：本研究旨在探讨使用多参数同步正电子发射断层扫描（PET）/磁共振成像（MRI）的海马影像组学在阿尔茨海默病（AD）早期诊断中的应用。
方法：共有53名健康对照（HC）参与者，55例遗忘型轻度认知障碍（aMCI）患者，51例AD患者纳入本研究。所有参与者同时接受PET/MRI扫描,包括18F-氟脱氧葡萄糖（18F-FDG）PET，3D动脉自旋标记（ASL），和高分辨率T1加权成像（3DT1WI）。在这三个模态图像上从海马区域提取影像组学特征。对Logistic回归模型进行训练以对AD和HC进行分类，AD和aMCI,aMCI和HC分别。通过5倍交叉验证评估逻辑回归模型的诊断性能和放射组学评分(Rad-Score)。
结果：海马影像组学特征表现出良好的诊断性能，在HC的二元分类中，多模态分类器优于单模态分类器，aMCI和AD。使用多模态分类器，我们实现了0.98的接收器工作特征曲线下面积（AUC）和96.7%的准确度分类AD从HC，将aMCI与HC分类的AUC为0.86，准确率为80.6%。AD和HC之间的Rad评分值差异显着（p<0.001），aMCI和HC(p<0.001)组。决策曲线分析显示，与神经心理学测试相比，多模式分类器具有更高的临床优势。
结论：使用PET/MRI辅助识别早期AD的多参数海马影像组学，并且可以为临床应用提供潜在的生物标志物。
方法：共有53名健康对照（HC）参与者，55例遗忘型轻度认知障碍（aMCI）患者，51例AD患者纳入本研究。所有参与者同时接受PET/MRI扫描,包括18F-氟脱氧葡萄糖（18F-FDG）PET，3D动脉自旋标记（ASL），和高分辨率T1加权成像（3DT1WI）。在这三个模态图像上从海马区域提取影像组学特征。对Logistic回归模型进行训练以对AD和HC进行分类，AD和aMCI,aMCI和HC分别。通过5倍交叉验证评估逻辑回归模型的诊断性能和放射组学评分(Rad-Score)。
结果：海马影像组学特征表现出良好的诊断性能，在HC的二元分类中，多模态分类器优于单模态分类器，aMCI和AD。使用多模态分类器，我们实现了0.98的接收器工作特征曲线下面积（AUC）和96.7%的准确度分类AD从HC，将aMCI与HC分类的AUC为0.86，准确率为80.6%。AD和HC之间的Rad评分值差异显着（p<0.001），aMCI和HC(p<0.001)组。决策曲线分析显示，与神经心理学测试相比，多模式分类器具有更高的临床优势。
结论：使用PET/MRI辅助识别早期AD的多参数海马影像组学，并且可以为临床应用提供潜在的生物标志物。
