Medical Imaging Department, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China; Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Department of Radiology, Chongqing Western Hospital, Chongqing, China.
Comput Biol Med. 2024 Aug;178:108684. doi: 10.1016/j.compbiomed.2024.108684. Epub 2024 Jun 4.
White matter hyperintensity (WMH) is a common feature of brain aging, often linked with cognitive decline and dementia. This study aimed to employ deep learning and radiomics to develop models for detecting cognitive impairment in WMH patients and to analyze the causal relationships among cognitive impairment and related factors.
A total of 79 WMH patients from hospital 1 were randomly divided into a training set (62 patients) and a testing set (17 patients). Additionally, 29 patients from hospital 2 were included as an independent testing set. All participants underwent formal neuropsychological assessments to determine cognitive status. Automated identification and segmentation of WMH were conducted using VB-net, with extraction of radiomics features from cortex, white matter, and nuclei. Four machine learning classifiers were trained on the training set and validated on the testing set to detect cognitive impairment. Model performances were evaluated and compared. Causal analyses were conducted among cortex, white matter, nuclei alterations, and cognitive impairment.
Among the models, the logistic regression (LR) model based on white matter features demonstrated the highest performance, achieving an AUC of 0.819 in the external test dataset. Causal analyses indicated that age, education level, alterations in cortex, white matter, and nuclei were causal factors of cognitive impairment.
The LR model based on white matter features exhibited high accuracy in detecting cognitive impairment in WMH patients. Furthermore, the possible causal relationships among alterations in cortex, white matter, nuclei, and cognitive impairment were elucidated.
脑白质高信号（WMH）是脑老化的常见特征，常与认知能力下降和痴呆有关。本研究旨在利用深度学习和放射组学开发用于检测 WMH 患者认知障碍的模型，并分析认知障碍与相关因素之间的因果关系。
共 79 例来自医院 1 的 WMH 患者被随机分为训练集（62 例）和测试集（17 例）。此外，还纳入了来自医院 2 的 29 例患者作为独立测试集。所有参与者均接受了正式的神经心理学评估以确定认知状态。使用 VB-net 自动识别和分割 WMH，并从皮质、白质和核中提取放射组学特征。在训练集上训练了四个机器学习分类器，并在测试集上进行验证以检测认知障碍。评估和比较了模型性能。在皮质、白质、核改变与认知障碍之间进行了因果分析。
在这些模型中，基于白质特征的逻辑回归（LR）模型表现最佳，在外部测试数据集的 AUC 为 0.819。因果分析表明，年龄、教育程度、皮质、白质和核的改变是认知障碍的因果因素。
基于白质特征的 LR 模型在检测 WMH 患者认知障碍方面具有较高的准确性。此外，还阐明了皮质、白质、核改变与认知障碍之间可能的因果关系。
