基于 T1 加权成像的海马影像组学在阿尔茨海默病诊断中的应用。

T1-Weighted Imaging-Based Hippocampal Radiomics in the Diagnosis of Alzheimer's Disease.

机构信息

Department of Medical Imaging, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, China (T.T.Y., J.C.Y., T.Y.S., X.H.M., X.Y.W., Z.Z.J.).

Department of Neurology, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, China (M.H.C.).

出版信息

Acad Radiol. 2024 Dec;31(12):5183-5192. doi: 10.1016/j.acra.2024.06.012. Epub 2024 Jun 19.

DOI:10.1016/j.acra.2024.06.012

PMID:38902110

Abstract

RATIONALE AND OBJECTIVES

To investigate the potential of T1-weighted imaging (T1WI)-based hippocampal radiomics as imaging markers for the diagnosis of Alzheimer's disease (AD) and their efficacy in discriminating between mild cognitive impairment (MCI) and dementia in AD.

METHODS

A total of 126 AD patients underwent T1WI-based magnetic resonance imaging (MRI) examinations, along with 108 age-sex-matched healthy controls (HC). This was a retrospective, single-center study conducted from November 2021 to February 2023. AD patients were categorized into two groups based on disease progression and cognitive function: AD-MCI and dementia (AD-D). T1WI-based radiomics features of the bilateral hippocampi were extracted. To diagnose AD and differentiate between AD-MCI and AD-D, predictive models were developed using random forest (RF), logistic regression (LR), and support vector machine (SVM). We compared radiomics features between the AD and HC groups, as well as within the subgroups of AD-MCI and AD-D. Area under the curve (AUC), accuracy, sensitivity, and specificity were all used to assess model performance. Furthermore, correlations between radiomics features and Mini-Mental State Examination (MMSE) scores, tau protein phosphorylated at threonine 181 (P-tau-181), and amyloid β peptide1-42 (Aβ1-42) were analyzed.

RESULTS

The RF model demonstrated superior performance in distinguishing AD from HC (AUC=0.961, accuracy=90.8%, sensitivity=90.7%, specificity=90.9%) and in identifying AD-MCI and AD-D (AUC=0.875, accuracy=80.7%, sensitivity=87.2%, specificity=73.2%) compared to the other models. Additionally, radiomics features were correlated with MMSE scores, P-tau-181, and Aβ1-42 levels in AD.

CONCLUSION

T1WI-based hippocampal radiomics features are valuable for diagnosing AD and identifying AD-MCI and AD-D.

摘要

背景和目的

探讨 T1 加权成像（T1WI）基础上的海马放射组学作为阿尔茨海默病（AD）诊断的影像学标志物的潜力，以及其在区分 AD 患者中的轻度认知障碍（MCI）和痴呆（AD-D）方面的效果。

方法

共纳入 126 例 AD 患者和 108 例年龄、性别匹配的健康对照组（HC）进行 T1WI 磁共振成像（MRI）检查。这是一项回顾性、单中心研究，于 2021 年 11 月至 2023 年 2 月进行。根据疾病进展和认知功能，将 AD 患者分为 AD-MCI 和 AD-D 两组。提取双侧海马的 T1WI 基础放射组学特征。为了诊断 AD 并区分 AD-MCI 和 AD-D，使用随机森林（RF）、逻辑回归（LR）和支持向量机（SVM）建立预测模型。我们比较了 AD 组与 HC 组之间以及 AD-MCI 和 AD-D 亚组之间的放射组学特征。曲线下面积（AUC）、准确性、敏感度和特异度均用于评估模型性能。此外，还分析了放射组学特征与简易精神状态检查（MMSE）评分、磷酸化 tau 蛋白 181 位（P-tau-181）和淀粉样β肽 1-42（Aβ1-42）之间的相关性。

结果

与其他模型相比，RF 模型在区分 AD 与 HC（AUC=0.961，准确性=90.8%，敏感度=90.7%，特异度=90.9%）以及识别 AD-MCI 和 AD-D（AUC=0.875，准确性=80.7%，敏感度=87.2%，特异度=73.2%）方面表现出更好的性能。此外，AD 患者的放射组学特征与 MMSE 评分、P-tau-181 和 Aβ1-42 水平相关。