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LI-RADS: Looking Back, Looking Forward

delete2023-04-01
delete25
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OA
AI
V
Victoria Chernyak *
K
Kathryn J. Fowler
R
Richard Kinh Gian
A
Aya Kamaya
Y
Yuko Kono
A
An Tang
D
Donald G. Mitchell
J
Jeffrey C. Weinreb
C
Cynthia Santillan
C
Claude B. Sirlin
DOI:10.1148/radiol.222801delete
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摘要

摘要

En 中文
Since its initial release in 2011, the Liver Imaging Reporting and Data System (LI-RADS) has evolved and expanded in scope. It started as a single algorithm for hepatocellular carcinoma (HCC) diagnosis with CT or MRI with extracellular contrast agents and has grown into a multialgorithm network covering all major liver imaging modalities and contexts of use. Furthermore, it has de-veloped its own lexicon, report templates, and supplementary materials. This article highlights the major achievements of LI-RADS in the past 11 years, including adoption in clinical care and research across the globe, and complete unification of HCC diagnostic systems in the United States. Additionally, the authors discuss current gaps in knowledge, which include challenges in surveil-lance, diagnostic population definition, perceived complexity, limited sensitivity of LR-5 (definite HCC) category, management implications of indeterminate observations, challenges in reporting, and treatment response assessment following radiation-based therapies and systemic treatments. Finally, the authors discuss future directions, which will focus on mitigating the current chal-lenges and incorporating advanced technologies. Tha authors envision that LI-RADS will ultimately transform into a probability -based system for diagnosis and prognostication of liver cancers that will integrate patient characteristics and quantitative imaging features, while accounting for imaging modality and contrast agent.(c) RSNA, 2023
Keyword:
ACID-ENHANCED MRI
HYPERVASCULAR HYPOINTENSE NODULES
LONG-TERM OUTCOMES
HEPATOCELLULAR-CARCINOMA
HEPATOBILIARY PHASE
MAGNETIC-RESONANCE
NONINVASIVE DIAGNOSIS
ANCILLARY FEATURES
T2 HYPERINTENSITY
VERSION 2018

期刊

Radiology 封面图
Radiology
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15.2
论文数:
4.0W
被引数:
6.0W

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U
universite de montreal
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被引数: 46
S
Stanford University
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University of California System
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M
Memorial Sloan Kettering Cancer Center
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3.4W
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被引数: 4.6W
U
University of California San Diego
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4.6W
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被引数: 924
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引用论文

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