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无创影像学技术与颈动脉血管新生评估:前景与挑战。

Non-invasive imaging techniques and assessment of carotid vasa vasorum neovascularization: Promises and pitfalls.

机构信息

Institute for Biomedical Research in Lleida Dr. Pifarré Foundation, Catalonia, Spain; Departament de Ciències Mèdiques Bàsiques, University of Lleida, Catalonia, Spain.

Unit for the Detection and Treatment of Atherothrombotic Diseases, Hospital Universitari Arnau de Vilanova de Lleida, Catalonia, Spain; Vascular and Renal Translational Research Group - IRBLleida, Catalonia, Spain.

出版信息

Trends Cardiovasc Med. 2019 Feb;29(2):71-80. doi: 10.1016/j.tcm.2018.06.007. Epub 2018 Jun 21.

Abstract

Carotid adventitia vasa vasorum neovascularization (VVn) is associated with the initial stages of arteriosclerosis and with the formation of unstable plaque. However, techniques to accurately quantify that neovascularization in a standard, fast, non-invasive, and efficient way are still lacking. The development of such techniques holds the promise of enabling wide, inexpensive, and safe screening programs that could stratify patients and help in personalized preventive cardiovascular medicine. In this paper, we review the recent scientific literature pertaining to imaging techniques that could set the stage for the development of standard methods for quantitative assessment of atherosclerotic plaque and carotid VVn. We present and discuss the alternative imaging techniques being used in clinical practice and we review the computational developments that are contributing to speed up image analysis and interpretation. We conclude that one of the greatest upcoming challenges will be the use of machine learning techniques to develop automated methods that assist in the interpretation of images to stratify patients according to their risk.

摘要

颈动脉外膜血管新生(VVn)与动脉粥样硬化的初始阶段和不稳定斑块的形成有关。然而,仍然缺乏一种准确、快速、非侵入性且高效的方法来量化这种新生血管。开发这种技术有望实现广泛、廉价和安全的筛查计划,以便对患者进行分层,并有助于个性化的心血管预防医学。在本文中,我们回顾了与成像技术相关的最新科学文献,这些技术可能为开发定量评估动脉粥样硬化斑块和颈动脉 VVn 的标准方法奠定基础。我们展示并讨论了目前在临床实践中使用的替代成像技术,并回顾了有助于加快图像分析和解释的计算进展。我们得出的结论是,未来最大的挑战之一将是使用机器学习技术来开发自动化方法,以协助对图像进行解释,根据患者的风险进行分层。

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