Yang Mingguang, Meng Shan, Wu Faqi, Shi Feng, Xia Yuwei, Feng Junbang, Zhang Jinrui, Li Chuanming
Medical Imaging Department, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China.
Department of Radiology, Chongqing Western Hospital, Chongqing, China.
Front Med (Lausanne). 2024 Jan 12;11:1305565. doi: 10.3389/fmed.2024.1305565. eCollection 2024.
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).
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.
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.
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.
轻度认知障碍（MCI）的早期快速诊断对改善阿尔茨海默病（AD）的预后具有重要临床价值。海马体和海马旁回在认知功能衰退的发生中起关键作用。在本研究中，运用深度学习和放射组学技术从健康对照（HC）中自动检测MCI。
本研究纳入了115例MCI患者和133名正常个体，其3D-T1加权MR结构图像来自ADNI数据库。使用VB-net自动进行海马体和海马旁回的识别与分割，并提取放射组学特征。采用Relief、最小冗余最大相关、递归特征消除以及最小绝对收缩和选择算子（LASSO）进行降维和选择最优特征。包括支持向量机（SVM）、随机森林（RF）、逻辑回归（LR）、袋装决策树（BDT）和高斯过程（GP）在内的五个独立机器学习分类器在训练集上进行训练，并在测试集上进行验证以检测MCI。使用德龙检验评估不同模型的性能。
我们的VB-net能够自动识别和分割双侧海马体和海马旁回。经过四步特征降维后，基于组合特征（海马体的11个特征和海马旁回的4个特征）的GP模型在区分MCI和正常对照受试者方面表现最佳。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）。决策曲线分析表明线图模型的临床获益较高。
基于双侧海马体和海马旁回的15个放射组学特征的GP分类器能够基于传统MR图像从正常对照中高精度地检测MCI。我们的全自动模型能够快速处理MRI数据并在1分钟内给出结果，这在辅助诊断中具有重要临床价值。
