Lee Hwunjae
YUHS-KRIBB Medical Convergence Research Institute, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea.
Graduate Program of Biomedical Engineering, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea.
Curr Med Sci. 2025 Jun 13. doi: 10.1007/s11596-025-00069-5.
Atherosclerosis involves not only the narrowing of blood vessels and plaque accumulation but also changes in plaque composition and stability, all of which are critical for disease progression. Conventional imaging techniques such as magnetic resonance angiography (MRA) and digital subtraction angiography (DSA) primarily assess luminal narrowing and plaque size, but have limited capability in identifying plaque instability and inflammation within the vascular muscle wall. This study aimed to develop and evaluate a novel imaging approach using ligand-modified nanomagnetic contrast (lmNMC) nanoprobes in combination with molecular magnetic resonance imaging (mMRI) to visualize and quantify vascular inflammation and plaque characteristics in a rabbit model of atherosclerosis.
A rabbit model of atherosclerosis was established and underwent mMRI before and after administration of lmNMC nanoprobes. Radiomic features were extracted from segmented images using discrete wavelet transform (DWT) to assess spatial frequency changes and gray-level co-occurrence matrix (GLCM) analysis to evaluate textural properties. Further radiomic analysis was performed using neural network-based regression and clustering, including the application of self-organizing maps (SOMs) to validate the consistency of radiomic pattern between training and testing data.
Radiomic analysis revealed significant changes in spatial frequency between pre- and post-contrast images in both the horizontal and vertical directions. GLCM analysis showed an increase in contrast from 0.08463 to 0.1021 and a slight decrease in homogeneity from 0.9593 to 0.9540. Energy values declined from 0.2256 to 0.2019, while correlation increased marginally from 0.9659 to 0.9708. Neural network regression demonstrated strong convergence between target and output coordinates. Additionally, SOM clustering revealed consistent weight locations and neighbor distances across datasets, supporting the reliability of the radiomic validation.
The integration of lmNMC nanoprobes with mMRI enables detailed visualization of atherosclerotic plaques and surrounding vascular inflammation in a preclinical model. This method shows promise for enhancing the characterization of unstable plaques and may facilitate early detection of high-risk atherosclerotic lesions, potentially improving diagnostic and therapeutic strategies.
动脉粥样硬化不仅涉及血管狭窄和斑块积聚，还包括斑块成分和稳定性的变化，所有这些对于疾病进展都至关重要。传统成像技术，如磁共振血管造影（MRA）和数字减影血管造影（DSA），主要评估管腔狭窄和斑块大小，但在识别血管肌壁内的斑块不稳定性和炎症方面能力有限。本研究旨在开发和评估一种使用配体修饰的纳米磁性造影剂（lmNMC）纳米探针结合分子磁共振成像（mMRI）的新型成像方法，以在动脉粥样硬化兔模型中可视化和量化血管炎症及斑块特征。
建立动脉粥样硬化兔模型，并在注射lmNMC纳米探针前后进行mMRI检查。使用离散小波变换（DWT）从分割图像中提取放射组学特征，以评估空间频率变化，并使用灰度共生矩阵（GLCM）分析来评估纹理特性。使用基于神经网络的回归和聚类进行进一步的放射组学分析，包括应用自组织映射（SOM）来验证训练数据和测试数据之间放射组学模式的一致性。
放射组学分析显示，对比前后图像在水平和垂直方向上的空间频率均有显著变化。GLCM分析表明对比度从0.08463增加到0.1021，均匀性从0.9593略有下降至0.9540。能量值从0.2256降至0.2019，而相关性从0.9659略有增加至0.9708。神经网络回归显示目标坐标和输出坐标之间有很强的收敛性。此外，SOM聚类揭示了跨数据集的一致权重位置和邻域距离，支持了放射组学验证的可靠性。
lmNMC纳米探针与mMRI的结合能够在临床前模型中详细可视化动脉粥样硬化斑块及周围血管炎症。该方法在增强不稳定斑块特征描述方面显示出前景，并可能有助于早期检测高危动脉粥样硬化病变， potentially improving diagnostic and therapeutic strategies.（此处原文有误，可改为“ potentially improving diagnostic and therapeutic strategies.”），可能改善诊断和治疗策略。
