School of Medicine, Bushehr University of Medical Sciences, Bushehr, Iran.
Department of Radiology, Imam Ali Hospital, North Khorasan University of Medical Science, Bojnurd, Iran.
Aging Clin Exp Res. 2023 Nov;35(11):2333-2348. doi: 10.1007/s40520-023-02565-x. Epub 2023 Oct 6.
BACKGROUND: Alzheimer's disease (AD) is a debilitating neurodegenerative disease. Early diagnosis of AD and its precursor, mild cognitive impairment (MCI), is crucial for timely intervention and management. Radiomics involves extracting quantitative features from medical images and analyzing them using advanced computational algorithms. These characteristics have the potential to serve as biomarkers for disease classification, treatment response prediction, and patient stratification. Of note, Magnetic resonance imaging (MRI) radiomics showed a promising result for diagnosing and classifying AD, and MCI from normal subjects. Thus, we aimed to systematically evaluate the diagnostic performance of the MRI radiomics for this task. METHODS AND MATERIALS: A comprehensive search of the current literature was conducted using relevant keywords in PubMed/MEDLINE, Embase, Scopus, and Web of Science databases from inception to August 5, 2023. Original studies discussing the diagnostic performance of MRI radiomics for the classification of AD, MCI, and normal subjects were included. Method quality was evaluated with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and the Radiomics Quality Score (RQS) tools. RESULTS: We identified 13 studies that met the inclusion criteria, involving a total of 5448 participants. The overall quality of the included studies was moderate to high. The pooled sensitivity and specificity of MRI radiomics for differentiating AD from normal subjects were 0.92 (95% CI [0.85; 0.96]) and 0.91 (95% CI [0.85; 0.95]), respectively. The pooled sensitivity and specificity of MRI radiomics for differentiating MCI from normal subjects were 0.74 (95% CI [0.60; 0.85]) and 0.79 (95% CI [0.70; 0.86]), respectively. Also, the pooled sensitivity and specificity of MRI radiomics for differentiating AD from MCI were 0.73 (95% CI [0.64; 0.80]) and 0.79 (95% CI [0.64; 0.90]), respectively. CONCLUSION: MRI radiomics has promising diagnostic performance in differentiating AD, MCI, and normal subjects. It can potentially serve as a non-invasive and reliable tool for early diagnosis and classification of AD and MCI.
背景：阿尔茨海默病（AD）是一种使人虚弱的神经退行性疾病。早期诊断 AD 及其前驱轻度认知障碍（MCI）对于及时干预和管理至关重要。放射组学涉及从医学图像中提取定量特征，并使用先进的计算算法对其进行分析。这些特征有可能作为疾病分类、治疗反应预测和患者分层的生物标志物。值得注意的是，磁共振成像（MRI）放射组学在诊断和分类 AD 以及从正常受试者中诊断 MCI 方面显示出有希望的结果。因此，我们旨在系统评估 MRI 放射组学在这一任务中的诊断性能。
方法和材料：我们使用 PubMed/MEDLINE、Embase、Scopus 和 Web of Science 数据库中的相关关键字，从成立到 2023 年 8 月 5 日进行了全面的文献检索。纳入了讨论 MRI 放射组学对 AD、MCI 和正常受试者分类的诊断性能的原始研究。使用诊断准确性研究的质量评估工具（QUADAS-2）和放射组学质量评分（RQS）工具评估方法质量。
结果：我们确定了符合纳入标准的 13 项研究，共涉及 5448 名参与者。纳入研究的总体质量为中等到较高。MRI 放射组学用于区分 AD 与正常受试者的汇总敏感性和特异性分别为 0.92（95%CI [0.85；0.96]）和 0.91（95%CI [0.85；0.95]）。MRI 放射组学用于区分 MCI 与正常受试者的汇总敏感性和特异性分别为 0.74（95%CI [0.60；0.85]）和 0.79（95%CI [0.70；0.86]）。此外，MRI 放射组学用于区分 AD 与 MCI 的汇总敏感性和特异性分别为 0.73（95%CI [0.64；0.80]）和 0.79（95%CI [0.64；0.90]）。
结论：MRI 放射组学在区分 AD、MCI 和正常受试者方面具有有前途的诊断性能。它有可能成为 AD 和 MCI 早期诊断和分类的一种非侵入性和可靠的工具。
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