收稿日期: 2022-03-07
录用日期: 2022-06-24
网络出版日期: 2022-09-04
基金资助
国家自然科学基金(82172138);上海市科学技术委员会医学创新专项项目(21Y11902400);上海市科学技术委员会优秀学科带头人项目(21XD1402200);海南医科大学急诊与创伤教育部重点实验室(KLET-202016)
Research progress of clinical application of ASPECT score in acute ischemic stroke
Received date: 2022-03-07
Accepted date: 2022-06-24
Online published: 2022-09-04
Supported by
National Natural Science Foundation of China(82172138);Special Medical Innovation Project of Shanghai Science and Technology Committee(21Y11902400);Excellent Academic Leader Program of Shanghai Science and Technology Committee(21XD1402200);Key Laboratory of Emergency and Trauma (Hainan Medical University), Ministry of Education(KLET-202016)
急性缺血性脑卒中（acute ischemic stroke，AIS）以高致残率、高致死率为特征，如何快速准确地诊断和选择恰当的治疗方式是诊治的关键。AIS患者病情的严重程度主要取决于脑组织早期缺血性改变（early ischemic change，EIC）的位置和病灶的大小，因此结合影像学检查对患者病情进行评估十分必要。Alberta卒中项目早期CT评分（Alberta stroke program early CT score，ASPECT评分）是一个总分为10分的影像组学评分系统，以准确、简单的方式来评价脑卒中患者大脑中动脉供血区域EIC，从影像学角度量化患者的病情严重程度，帮助临床医师进行医疗决策。ASPECT评分不仅被广泛地应用于预测脑卒中患者的预后情况，也被用于识别接受血管内治疗获益的人群，及评估患者进行血管内治疗的风险。目前结合多模式影像学的ASPECT评分也用于预测缺血性病灶核心体积以及预测预后。近年来随着人工智能的发展，出现了基于机器学习的自动化ASPECT评分方法。该文对ASPECT评分方法及其在AIS治疗和预后预测中的价值、多模式ASPECT评分、与人工智能相结合的自动化ASPECT评分的应用进行综述。
关键词： 急性缺血性脑卒中; Alberta卒中项目早期CT评分; 预后; 人工智能
卫雪敏 , 高成金 . ASPECT评分在急性缺血性脑卒中临床应用中的研究进展[J]. 上海交通大学学报（医学版）, 2022 , 42(7) : 919 -924 . DOI: 10.3969/j.issn.1674-8115.2022.07.011
Acute ischemic stroke (AIS) is characterized by a high rate of disability and mortality, and rapid and accurate diagnosis and appropriate treatment are the keys. The severity of AIS patients depends on the location of early ischemic changes (EIC) in the brain tissue and the size of the lesion, so it is necessary to evaluate the patients' conditions by combining with imaging. The Alberta stroke program early CT score (ASPECT score) is a 10-point imaging score system that evaluates the EIC in the middle cerebral artery supplying region of the stroke patients in an accurate and simple way by quantifying the severity in terms of the imaging to help clinicians make medical decisions. ASPECT score is not only widely used to predict the prognosis of stroke patients, but also to identify the population benefiting from the endovascular therapy and evaluate the risk of endovascular therapy for patients. ASPECT score combined with multimodal imaging has also been used to predict ischemic core volumes and the prognosis. In recent years, with the development of artificial intelligence, automatic ASPECT scoring methods based on machine learning have emerged. This paper reviews ASPECT scoring methods, its value in the treatment and the assessment of prognosis in AIS, multimodal ASPECT scoring, and the application of automated ASPECT scoring combined with artificial intelligence.
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