弥漫性肝病的无创定量 CT:脂肪变性、铁超载和纤维化。

IF 5.2 1区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Radiographics Pub Date : 2025-01-01 DOI:10.1148/rg.240176
Perry J Pickhardt, Meghan G Lubner
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引用次数: 0

摘要

慢性弥漫性肝病的患病率持续上升,是一个全球性的健康问题。肝脂肪变性、铁超载和纤维化的无创检测和定量是至关重要的,特别是考虑到有创性肝活检的许多相对缺点和潜在风险。尽管MRI技术已成为量化肝脏脂肪、铁和纤维化的首选参考标准,但CT在机会性检测未被怀疑的疾病中发挥重要作用,并且在更高的体积上进行。对于肝脏脂肪变性,非对比CT提供了与基于mri的质子密度脂肪分数(PDFF)量化非常接近的结果,肝脏衰减值小于或等于40 HU表示至少中度脂肪变性。肝脂肪定量造影后CT不太精确,但通常可以提供分类评估(例如,轻度和中度脂肪变性)。当观察到实质衰减值增加(例如,> - 75 HU)时,非对比CT也可以触发对铁过载的适当评估。各种形态和功能CT特征提示存在潜在的肝纤维化和肝硬化。除了主观评估之外,定量CT方法对纤维化分期可以提供与弹性成像相当的性能。此外,由于不需要前瞻性技术,定量CT评估可以回顾性进行。许多CT定量测量现在通过人工智能(AI)深度学习算法完全自动化。这些回顾性和自动化的优势对纵向临床护理和研究具有重要意义。最终,无论CT的指征如何,对脂肪变性、铁超载和纤维化的机会性检测都可以导致适当的临床意识和管理。©RSNA, 2024参见本期叶的特邀评论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Noninvasive Quantitative CT for Diffuse Liver Diseases: Steatosis, Iron Overload, and Fibrosis.

Chronic diffuse liver disease continues to increase in prevalence and represents a global health concern. Noninvasive detection and quantification of hepatic steatosis, iron overload, and fibrosis are critical, especially given the many relative disadvantages and potential risks of invasive liver biopsy. Although MRI techniques have emerged as the preferred reference standard for quantification of liver fat, iron, and fibrosis, CT can play an important role in opportunistic detection of unsuspected disease and is performed at much higher volumes. For hepatic steatosis, noncontrast CT provides a close approximation to MRI-based proton-density fat fraction (PDFF) quantification, with liver attenuation values less than or equal to 40 HU signifying at least moderate steatosis. Liver fat quantification with postcontrast CT is less precise but can generally provide categorical assessment (eg, mild vs moderate steatosis). Noncontrast CT can also trigger appropriate assessment for iron overload when increased parenchymal attenuation values are observed (eg, >75 HU). A variety of morphologic and functional CT features indicate the presence of underlying hepatic fibrosis and cirrhosis. Beyond subjective assessment, quantitative CT methods for staging fibrosis can provide comparable performance to that of elastography. Furthermore, quantitative CT assessment can be performed retrospectively, since prospective techniques are not required. Many of these CT quantitative measures are now fully automated via artificial intelligence (AI) deep learning algorithms. These retrospective and automated advantages have important implications for longitudinal clinical care and research. Ultimately, regardless of the indication for CT, opportunistic detection of steatosis, iron overload, and fibrosis can result in appropriate clinical awareness and management. ©RSNA, 2024 See the invited commentary by Yeh in this issue.

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来源期刊
Radiographics
Radiographics 医学-核医学
CiteScore
8.20
自引率
5.50%
发文量
224
审稿时长
4-8 weeks
期刊介绍: Launched by the Radiological Society of North America (RSNA) in 1981, RadioGraphics is one of the premier education journals in diagnostic radiology. Each bimonthly issue features 15–20 practice-focused articles spanning the full spectrum of radiologic subspecialties and addressing topics such as diagnostic imaging techniques, imaging features of a disease or group of diseases, radiologic-pathologic correlation, practice policy and quality initiatives, imaging physics, informatics, and lifelong learning. A special issue, a monograph focused on a single subspecialty or on a crossover topic of interest to multiple subspecialties, is published each October. Each issue offers more than a dozen opportunities to earn continuing medical education credits that qualify for AMA PRA Category 1 CreditTM and all online activities can be applied toward the ABR MOC Self-Assessment Requirement.
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