海马结构
认知障碍
2型糖尿病
医学
接收机工作特性
内科学
神经影像学
磁共振成像
糖尿病
蒙特利尔认知评估
痴呆
认知
阿尔茨海默病神经影像学倡议
心脏病学
无线电技术
金标准(测试)
相关性
萎缩
听力学
人工智能
部分各向异性
2型糖尿病
灰质
神经科学
睡眠剥夺对认知功能的影响
核医学
磁共振弥散成像
心理学
作者
Ran-chao Wang,Jinxin Wang,Yang Li,Yang Li,Yan Xu,Yu Shen,Yue-feng Li,Yue-feng Li
标识
DOI:10.1177/13872877261422272
摘要
BackgroundType 2 diabetes mellitus (T2DM) is associated with a higher risk of mild cognitive impairment (MCI), but conventional structural MRI (sMRI) metrics lack sensitivity for detecting early hippocampal alterations. Radiomic analysis of hippocampal subfields may capture diabetes-related microstructural abnormalities.ObjectiveTo develop and interpret a structural MRI-based model incorporating hippocampal subfield radiomic features for identifying MCI in T2DM patients.MethodsThis retrospective study included 149 T2DM patients. Based on Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE) scores, participants were classified into T2DM-MCI and T2DM-NC groups. A total of 2232 radiomic features were extracted from 24 hippocampal subfields segmented by FreeSurfer. Maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) were used to construct the hippocampal radiomics score (Hip-Radscore). Additional models were built using gray matter volume (sMRI-Radscore), subfield volumes (SFV-Score), and clinical variables. Model performance was evaluated by bootstrap resampling, ROC analysis, and SHapley Additive exPlanations (SHAP) interpretation.ResultsThe Hip-Radscore model achieved an AUC of 0.76 (95% CI: 0.68-0.84), outperforming the gray matter volume (0.62) and SFV-Score (0.63) models. The combined model integrating imaging and clinical variables reached the highest accuracy (AUC = 0.87, 95% CI: 0.81-0.93), significantly superior to the clinical-only model (AUC = 0.70, p < 0.05). The Hip-Radscore correlated positively with diabetes duration (ρ = 0.47) and HbA1c (ρ = 0.56), and negatively with MMSE (ρ = -0.63) and MoCA (ρ = -0.65) (all p < 0.001).ConclusionsHippocampal subfield radiomics significantly improves the detection of MCI in T2DM and reflects diabetes-specific structural alterations linked to metabolic status and cognitive decline.
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