PDF(Pubmed)
Abstract：
UNASSIGNED: Early and rapid diagnosis of mild cognitive impairment (MCI) has important clinical value in improving the prognosis of Alzheimer\'s disease (AD). The hippocampus and parahippocampal gyrus play crucial roles in the occurrence of cognitive function decline. In this study, deep learning and radiomics techniques were used to automatically detect MCI from healthy controls (HCs).
UNASSIGNED: This study included 115 MCI patients and 133 normal individuals with 3D-T1 weighted MR structural images from the ADNI database. The identification and segmentation of the hippocampus and parahippocampal gyrus were automatically performed with a VB-net, and radiomics features were extracted. Relief, Minimum Redundancy Maximum Correlation, Recursive Feature Elimination and the minimum absolute shrinkage and selection operator (LASSO) were used to reduce the dimensionality and select the optimal features. Five independent machine learning classifiers including Support Vector Machine (SVM), Random forest (RF), Logistic Regression (LR), Bagging Decision Tree (BDT), and Gaussian Process (GP) were trained on the training set, and validated on the testing set to detect the MCI. The Delong test was used to assess the performance of different models.
UNASSIGNED: Our VB-net could automatically identify and segment the bilateral hippocampus and parahippocampal gyrus. After four steps of feature dimensionality reduction, the GP models based on combined features (11 features from the hippocampus, and 4 features from the parahippocampal gyrus) showed the best performance for the MCI and normal control subject discrimination. The AUC of the training set and test set were 0.954 (95% CI: 0.929-0.979) and 0.866 (95% CI: 0.757-0.976), respectively. Decision curve analysis showed that the clinical benefit of the line graph model was high.
UNASSIGNED: The GP classifier based on 15 radiomics features of bilateral hippocampal and parahippocampal gyrus could detect MCI from normal controls with high accuracy based on conventional MR images. Our fully automatic model could rapidly process the MRI data and give results in 1 minute, which provided important clinical value in assisted diagnosis.
UNASSIGNED: This study included 115 MCI patients and 133 normal individuals with 3D-T1 weighted MR structural images from the ADNI database. The identification and segmentation of the hippocampus and parahippocampal gyrus were automatically performed with a VB-net, and radiomics features were extracted. Relief, Minimum Redundancy Maximum Correlation, Recursive Feature Elimination and the minimum absolute shrinkage and selection operator (LASSO) were used to reduce the dimensionality and select the optimal features. Five independent machine learning classifiers including Support Vector Machine (SVM), Random forest (RF), Logistic Regression (LR), Bagging Decision Tree (BDT), and Gaussian Process (GP) were trained on the training set, and validated on the testing set to detect the MCI. The Delong test was used to assess the performance of different models.
UNASSIGNED: Our VB-net could automatically identify and segment the bilateral hippocampus and parahippocampal gyrus. After four steps of feature dimensionality reduction, the GP models based on combined features (11 features from the hippocampus, and 4 features from the parahippocampal gyrus) showed the best performance for the MCI and normal control subject discrimination. The AUC of the training set and test set were 0.954 (95% CI: 0.929-0.979) and 0.866 (95% CI: 0.757-0.976), respectively. Decision curve analysis showed that the clinical benefit of the line graph model was high.
UNASSIGNED: The GP classifier based on 15 radiomics features of bilateral hippocampal and parahippocampal gyrus could detect MCI from normal controls with high accuracy based on conventional MR images. Our fully automatic model could rapidly process the MRI data and give results in 1 minute, which provided important clinical value in assisted diagnosis.
摘要：
■早期、快速诊断轻度认知障碍(MCI)对改善阿尔茨海默病(AD)的预后具有重要的临床价值。海马和海马旁回在认知功能下降的发生中起着至关重要的作用。在这项研究中,使用深度学习和影像组学技术自动检测健康对照(HCs)的MCI.
■这项研究纳入了115名MCI患者和133名正常个体，并获得了来自ADNI数据库的3D-T1加权MR结构图像。用VB-net自动进行海马和海马旁回的识别和分割,并提取了影像组学特征。救济,最小冗余最大相关性，使用递归特征消除和最小绝对收缩和选择运算符（LASSO）来减少维度并选择最佳特征。五个独立的机器学习分类器，包括支持向量机(SVM)，随机森林(RF)，逻辑回归(LR)，Bagging决策树(BDT),和高斯过程(GP)在训练集上进行训练，并在测试集上验证以检测MCI。Delong检验用于评估不同模型的性能。
■我们的VB-net可以自动识别和分割双侧海马和海马旁回。经过四个步骤的特征降维，基于组合特征的GP模型(来自海马的11个特征，海马旁回的4个特征）对MCI和正常对照受试者的辨别表现最佳。训练集和测试集的AUC分别为0.954（95％CI：0.929-0.979）和0.866（95％CI：0.757-0.976），分别。决策曲线分析表明，折线图模型的临床获益较高。
■基于双侧海马和海马旁回的15个影像组学特征的GP分类器可以基于常规MR图像高精度地从正常对照中检测MCI。我们的全自动模型可以快速处理MRI数据并在1分钟内给出结果，为辅助诊断提供了重要的临床价值。
■这项研究纳入了115名MCI患者和133名正常个体，并获得了来自ADNI数据库的3D-T1加权MR结构图像。用VB-net自动进行海马和海马旁回的识别和分割,并提取了影像组学特征。救济,最小冗余最大相关性，使用递归特征消除和最小绝对收缩和选择运算符（LASSO）来减少维度并选择最佳特征。五个独立的机器学习分类器，包括支持向量机(SVM)，随机森林(RF)，逻辑回归(LR)，Bagging决策树(BDT),和高斯过程(GP)在训练集上进行训练，并在测试集上验证以检测MCI。Delong检验用于评估不同模型的性能。
■我们的VB-net可以自动识别和分割双侧海马和海马旁回。经过四个步骤的特征降维，基于组合特征的GP模型(来自海马的11个特征，海马旁回的4个特征）对MCI和正常对照受试者的辨别表现最佳。训练集和测试集的AUC分别为0.954（95％CI：0.929-0.979）和0.866（95％CI：0.757-0.976），分别。决策曲线分析表明，折线图模型的临床获益较高。
■基于双侧海马和海马旁回的15个影像组学特征的GP分类器可以基于常规MR图像高精度地从正常对照中检测MCI。我们的全自动模型可以快速处理MRI数据并在1分钟内给出结果，为辅助诊断提供了重要的临床价值。
