Lu Jianping, Cai Guoen, Xiao Naian, Zheng Kunmu, Ye Qinyong, Chen Xiaochun
Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, China.
Institute of Clinical Neurology, Fujian Medical University, Fuzhou, China.
Front Aging Neurosci. 2025 Jul 11;17:1566247. doi: 10.3389/fnagi.2025.1566247. eCollection 2025.
This study aimed to investigate the role of cerebellar magnetic resonance imaging (MRI) features in identifying mild cognitive impairment (MCI).
This retrospective multicenter study included patients with MCI, patients with Alzheimer's disease (AD), and healthy controls (HCs) from three tertiary hospitals in China (January 2022-December 2023). Cerebellar and hippocampal radiomics features were extracted from T1-, T2-, and T2-FLAIR-weighted MRI. A sparse representation classifier was developed using 10-fold cross-validation and was validated on independent datasets. Diagnostic performance was assessed through sensitivity, specificity, and ROC-AUC values.
A total of 87 patients with MCI, 109 patients with AD, and 55 healthy controls (HCs) matched by gender and age were included for model construction and validation. Additionally, 13 patients with MCI and 26 patients with AD were included for external validation. The 10-fold cross-validation accuracy and ROC AUC for identifying cognitive impairment (CI) in the training set using a combination of cerebellar T1, T2, and T2-FLAIR weighted images were better than those of hippocampal models (91.0% vs. 86.8%, 0.943 vs. 0.931). The accuracy and ROC AUC in the independent test set were similar (89.3% vs. 89.3%, 0.908 vs. 0.906). The 10-fold cross-validation accuracy and ROC AUC for identifying MCI in the training set, using a combination of cerebellar T1, T2, and T2-FLAIR weighted images, were similar to those of hippocampal models (85.2% vs. 83.7%, 0.877 vs. 0.905). Furthermore, the results were consistent with the external validation set (89.7% vs. 93.1%, 0.962 vs. 0.974).
Cerebellar MRI radiomics models exhibit diagnostic accuracy comparable to hippocampal models for identifying CI and MCI, supporting the cerebellum's role in detecting early cognitive dysfunction. These findings provide novel insights into cerebellar contributions to AD pathophysiology and offer potential biomarkers for clinical application.
本研究旨在探讨小脑磁共振成像(MRI)特征在识别轻度认知障碍(MCI)中的作用。
这项回顾性多中心研究纳入了来自中国三家三级医院(2022年1月至2023年12月)的MCI患者、阿尔茨海默病(AD)患者和健康对照(HCs)。从小脑和海马的T1加权、T2加权和T2液体衰减反转恢复(T2-FLAIR)加权MRI中提取影像组学特征。使用10折交叉验证开发了一种稀疏表示分类器,并在独立数据集上进行验证。通过敏感性、特异性和ROC曲线下面积(ROC-AUC)值评估诊断性能。
共纳入87例MCI患者、109例AD患者和55例按性别和年龄匹配的健康对照用于模型构建和验证。此外,纳入13例MCI患者和26例AD患者进行外部验证。使用小脑T1加权、T2加权和T2-FLAIR加权图像组合在训练集中识别认知障碍(CI)的10折交叉验证准确率和ROC-AUC优于海马模型(91.0%对86.8%,0.943对0.931)。独立测试集中的准确率和ROC-AUC相似(89.3%对89.3%,0.908对0.906)。使用小脑T1加权、T2加权和T2-FLAIR加权图像组合在训练集中识别MCI的10折交叉验证准确率和ROC-AUC与海马模型相似(85.2%对83.7%,0.877对0.905)。此外,结果与外部验证集一致(89.7%对93.1%,0.962对0.974)。
小脑MRI影像组学模型在识别CI和MCI方面表现出与海马模型相当的诊断准确性,支持小脑在检测早期认知功能障碍中的作用。这些发现为小脑在AD病理生理学中的作用提供了新的见解,并为临床应用提供了潜在的生物标志物。