Image-based modeling for better understanding and assessment of atherosclerotic plaque progression and vulnerability: data, modeling, validation, uncertainty and predictions.
Image-based modeling for better understanding and assessment of atherosclerotic plaque progression and vulnerability: data, modeling, validation, uncertainty and predictions.
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DOI：
10.1016/j.jbiomech.2014.01.012
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发表时间：
2014-03-03
影响因子：
2.4
通讯作者：
Yuan, Chun
中科院分区：
文献类型：
--
作者：
Tang, Dalin;Kamm, Roger D.;Yang, Chun;Zheng, Jie;Canton, Gador;Bach, Richard;Huang, Xueying;Hatsukami, Thomas S.;Zhu, Jian;Ma, Genshan;Maehara, Akiko;Mintz, Gary S.;Yuan, Chun
关键词：
近年来，医学成像和基于图像的建模在识别动脉粥样硬化斑块形态和机械风险因素方面取得了相当大的进展，这些因素可用于开发改进的患者筛查策略。然而，我们需要清楚地了解我们已经取得的成就，以及将研究转化为实际临床实践并为公共卫生带来益处的真正需要。缺乏体内数据和临床事件作为验证模型预测的金标准是一个严重的限制。虽然这篇观点论文回顾了我们小组在基于图像的人类颈动脉和冠状动脉斑块模型中的关键步骤和发现，并对其他小组的相关工作进行了有限的回顾，但我们还关注该领域研究人员面临的巨大挑战和不确定性，以开发更准确和预测性的患者筛查工具。
Medical imaging and image-based modeling have made considerable progress in recent years in identifying atherosclerotic plaque morphological and mechanical risk factors which may be used in developing improved patient screening strategies. However, a clear understanding is needed about what we have achieved and what is really needed to translate research to actual clinical practices and bring benefits to public health. Lack of in vivo data and clinical events to serve as gold standard to validate model predictions is a severe limitation. While this perspective paper provides a review of the key steps and findings of our group in image-based models for human carotid and coronary plaques and a limited review of related work by other groups, we also focus on grand challenges and uncertainties facing the researchers in the field to develop more accurate and predictive patient screening tools.
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影响因子：
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作者：
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发表时间：
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影响因子：
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