影像学和人工智能技术定量评估肝硬化肌少症的研究进展  

Research progress of imaging and artificial intelligence technology in quantitative assessment of sarcopenia in liver cirrhosis

作　　者：徐媛 刘建莉[1] XU Yuan;LIU Jianli(Radiology Department of Lanzhou University Second Hospital,Second Clinical School of Lanzhou University,Key Laboratory of Medical Imaging of Gansu Province,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence,Lanzhou 730030,China)

机构地区：[1]兰州大学第二医院放射科,兰州大学第二临床医学院,甘肃省医学影像重点实验室,医学影像人工智能甘肃省国际科技合作基地,兰州730030

出　　处：《磁共振成像》2022年第11期149-153,共5页Chinese Journal of Magnetic Resonance Imaging

基　　金：国家自然科学基金地区科学基金项目(编号:81960337);甘肃省基础研究创新群体(编号:21JR7RA432);兰州市人才创新创业项目(编号:2020-RC-49);兰州大学第二医院“萃英研究生指导教师”培育计划项目(编号:CYDSPY202003)。

摘　　要：肌少症是肝硬化的常见并发症,也是导致肝硬化患者不良预后发生的重要原因,早期识别并预防肌少症已成为临床工作的重点、热点。影像学检查方法不仅可以评估肝硬化患者的肝脏病变情况,还可定量肌肉面积、肌肉密度和肌肉脂肪含量以评估肝硬化预后情况;此外,人工智能(artificial intelligence, AI)技术在医学领域的应用也为肝硬化肌少症的精准、快速识别和定量评估提供了新的思路。本文主要对双能X射线吸收法(dual-energy X-ray absorptiometry, DEXA)、超声(ultrasound, US)、MRI、CT和AI技术定量评价肝硬化肌少症的研究进展进行综述,以期为指导临床决策提供影像学参考。Sarcopenia is a common complication of liver cirrhosis and an important cause of poor prognosis in patients with liver cirrhosis, the early identification and prevention has become the focus of clinical work and a hot spot. Imaging methods can not only evaluate liver lesions in patients with liver cirrhosis, but also quantify muscle area, muscle density and muscle fat content to evaluate the prognosis of liver cirrhosis;in addition, the application of artificial intelligence(AI) technology in the medical field has provided new ideas for accurate and rapid identification and quantitative assessment of cirrhotic sarcopenia. This article focuses on dual-energy X-ray absorptiometry(DEXA), ultrasound(US), MRI, CT and AI techniques for quantitative evaluation of cirrhotic sarcopenia are reviewed with the aim of providing imaging references to guide clinical decision-making.

关 键 词：肝硬化 肌少症 肌肉面积 肌肉密度 肌肉脂肪含量 双能X射线吸收法 超声 磁共振成像 计算机断层成像 人工智能 影像组学 

分 类 号：R445.2[医药卫生—影像医学与核医学] R657.31[医药卫生—诊断学]