影像组学识别高风险颈动脉斑块的性能：一项系统综述。
完整摘要 · 中文译文
背景：卒中（中风）是全球死亡的主要原因，其中15-20%的缺血性卒中由颅外颈动脉粥样硬化性疾病引起。风险分层传统上依赖于管腔狭窄的严重程度，而这不足以反映斑块的易损性。影像组学通过从标准医学图像中提取高维定量数据，提供了一种新兴的解决方案。本系统综述评估了基于影像组学的模型在识别高危颈动脉斑块方面的性能。 材料与方法：在多个数据库进行了系统检索。纳入标准为开发或验证了用于颈动脉斑块评估的基于影像组学的模型并报告了性能指标，特别是曲线下面积（AUC）的研究。提取了关于研究设计、成像方式、分割方法和建模技术的数据。 结果：共纳入28项研究，涉及6，108例患者（72.4%为男性）。计算机断层扫描血管造影是最常用的成像方式（n = 16），28项研究中有27项进行了手动感兴趣区分割。影像组学模型显示出强大的预测性能。内部验证AUC高达0.984，而结合影像组学与临床和/或常规放射学特征的联合模型达到了最佳预测准确性，通常超过0.90。然而，仅有四项研究纳入了外部验证队列。 结论：影像组学是一种强大的无创工具，可用于识别高危颈动脉斑块，具有更优的判别能力 相较于传统的基于狭窄程度的评估。尽管取得了这些有前景的结果，该领域仍面临重大障碍，未来研究必须优先开展前瞻性多中心试验、开发自动化分割工作流程，并将影像组学特征与组织病理学发现相关联，以确保临床可重复性并转化为常规卒中预防。 要点：问题 标准颈动脉粥样硬化疾病评估无法捕捉斑块的生物学复杂性和易损性，因此需要客观、可重复且无创的方法来改善卒中预防。发现 与传统评估相比，影像组学及联合模型在识别高危颈动脉斑块方面表现出更优能力。经过四项外部验证，该领域仍处于探索阶段。相关性声明 影像组学为个性化卒中预防提供了一种无创工具。然而，目前技术障碍阻碍其转化为常规临床使用，包括依赖手动分割以及与国际标准化指南的依从性较低。
完整摘要 · 英文原文
BACKGROUND: Stroke is a leading cause of mortality worldwide, with 15-20% of ischemic strokes caused by extracranial carotid atherosclerotic disease. Risk stratification has traditionally relied on luminal stenosis severity, which inadequately reflects plaque vulnerability. Radiomics offers an emerging solution by extracting high-dimensional quantitative data from standard medical images. This systematic review evaluated the performance of radiomics-based models for identifying high-risk carotid plaques. MATERIALS AND METHODS: A systematic search was conducted across multiple databases. Studies were included if they developed or validated radiomics-based models for carotid plaque assessment and reported performance metrics, specifically the area under the curve (AUC). Data were extracted on study design, imaging modalities, segmentation methods, and modeling techniques. RESULTS: Twenty-eight studies involving 6,108 patients (72.4% male) were included. Computed tomography angiography was the most commonly used imaging modality (n = 16), and manual region-of-interest segmentation was performed in 27 of 28 studies. Radiomics models showed strong predictive performance. Internal validation AUCs were as high as 0.984, whereas combined models incorporating radiomics with clinical and/or conventional radiological features achieved the best predictive accuracy, often exceeding 0.90. However, only four studies included external validation cohorts. CONCLUSION: Radiomics is a powerful, non-invasive tool for identifying high-risk carotid plaques, offering superior discriminative power over conventional stenosis-based assessments. Despite these promising results, the field faces significant hurdles, and future research must prioritize prospective multicenter trials, the development of automated segmentation workflows, and the correlation of radiomic features with histopathological findings to ensure clinical reproducibility and transition into routine stroke prevention. KEY POINTS: Question Standard carotid atherosclerotic disease assessments fail to capture biological plaque complexity and vulnerability, necessitating objective, reproducible, and non-invasive methods to improve stroke prevention. Findings Radiomics and combined models demonstrate superior capability in identifying high-risk carotid plaques compared to traditional assessments. With four external validations, the field remains exploratory. Relevance statement Radiomics offers a non-invasive tool for personalized stroke prevention. However, technical barriers currently prevent its transition to routine clinical use, including reliance on manual segmentation and low compliance with international standardization guidelines.