代谢相关脂肪性肝病的早期筛查策略
DOI: 10.12449/JCH260223
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摘要: 代谢相关脂肪性肝病(MAFLD)是一种全球范围内高发的慢性肝脏疾病,及时精准干预可延缓病程,显著降低肝纤维化、肝硬化及肝癌等严重并发症的发生风险。传统活检技术结合代谢指标虽为金标准,但是作为一项有创检查,可能引发疼痛、出血等并发症,该现状促使科学研究将研究重点转向无创评估体系的构建。近年来,基于多维度检测策略的无创诊断技术不断更新,包括血清学模型、影像技术和临床算法等。本文系统综述了MAFLD在纤维化F1~F3期的筛查方法,重点探讨结合人工智能的深度学习模型,旨在为MAFLD的早期筛查提供思路,并为优化疾病管理策略提供科学参考。Abstract: Metabolic associated fatty liver disease (MAFLD) is a common chronic liver disease worldwide, and timely and precise intervention can delay disease progression and significantly reduce the risk of serious complications such as liver fibrosis, liver cirrhosis, and liver cancer. Although traditional liver biopsy combined with metabolic markers is the gold standard, it may cause complications such as pain and bleeding as an invasive examination, which has promoted scientific research to shift its focus to the construction of noninvasive assessment systems. In recent years, noninvasive diagnostic technologies based on multi-dimensional detection strategies have been continuously updated, including serological models, imaging techniques, and clinical algorithms. This article systematically reviews the screening methods for MAFLD during the fibrotic stages F1—F3, especially deep learning models based on artificial intelligence, in order to provide ideas for the early screening of MAFLD, as well as a scientific reference for optimizing disease management strategies.
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表 1 基于影像学早期筛查MAFLD的方法
Table 1. Early screening of MAFLD based on imaging methods
方法 优点 缺点 诊断效能差异 CT 易获得,清楚显示肝脏的形态、
结构及其与周围组织的关系存在辐射,孕妇及儿童等不适用 对轻度MAFLD诊断效能不佳 MRE 取样均一,无辐射,精准量化肝
脂肪含量,适合长期随访检查费用较高,植入心脏起搏器者不
适用特异度与敏感度均高,适用于初筛后
进一步确诊FibroScan 出现最早,占据欧美市场,不断
更新受肥胖、腹水和肋间隙狭窄等影响 适用于晚期肝炎与肝纤维化的评估 FibroTouch 无需根据肥胖程度选择探头型
号,仅需1个探头成本更高,个别特殊人群,如植入起
搏器患者、大量腹水患者等不适用对于轻中度脂肪变性更敏感 iLivTouch 检测效率更高,操作难度下降 普及率低,测量结果稳定性不高,在
腹水患者中效果同样不佳对于早期纤维化的筛查效果更佳 SWE 对肥胖患者准确性较高,可动
态监测病变区域设备昂贵,操作复杂 适用于专科医院对于肝纤维化精准
分期,不适合早期筛查MAFLDQUS 在不同亚组人群中均展现出稳
定的适用性依赖后处理算法,处理时间较长 各阶段诊断效能尚可 2D-CNN+
USFF算法适合MAFLD早期筛查 无法评估潜在的混杂因素,例如炎症
或纤维化对于临床疑似MAFLD患者诊断效能
较高FibroScan-AST 对体重、腹水等干扰因素适应
性更强基层医院缺少计算资源 在肝纤维化中晚期(≥F3)的诊断效能
优于早期Noureddin团队
机器学习模型能够减少代谢指标干扰 难以解释预测逻辑,接受度低 对于≥F2的患者敏感度和特异性平
衡较好注:CT,计算机体层成像;MRE,磁共振弹性成像;SWE,剪切波弹性成像技术;QUS,定量超声;USFF,超声脂肪分数;MAFLD,代谢相关脂肪性肝病。
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