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肝脏影像报告和数据系统（LI-RADS）中使用MRI的证据现状

Current State of Evidence for Use of MRI in LI-RADS.

作者信息

Kulkarni Ameya Madhav, Kruse Danielle, Harper Kelly, Lam Eric, Osman Hoda, Ansari Danyaal H, Sivanesan Umaseh, Bashir Mustafa R, Costa Andreu F, McInnes Matthew, van der Pol Christian B

机构信息

Department of Medical Imaging, Hamilton Health Sciences, McMaster University, Hamilton, Ontario, Canada.

Department of Diagnostic Imaging, Juravinski Hospital and Cancer Centre, Hamilton Health Sciences, Hamilton, Ontario, Canada.

出版信息

J Magn Reson Imaging. 2025 Sep;62(3):640-653. doi: 10.1002/jmri.29748. Epub 2025 Feb 21.

DOI:10.1002/jmri.29748
PMID:39981949
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12335345/
Abstract

The American College of Radiology Liver Imaging Reporting and Data System (LI-RADS) is the preeminent framework for classification and risk stratification of liver observations on imaging in patients at high risk for hepatocellular carcinoma. In this review, the pathogenesis of hepatocellular carcinoma and the use of MRI in LI-RADS is discussed, including specifically the LI-RADS diagnostic algorithm, its components, and its reproducibility with reference to the latest supporting evidence. The LI-RADS treatment response algorithms are reviewed, including the more recent radiation treatment response algorithm. The application of artificial intelligence, points of controversy, LI-RADS relative to other liver imaging systems, and possible future directions are explored. After reading this article, the reader will have an understanding of the foundation and application of LI-RADS as well as possible future directions.

摘要

美国放射学会肝脏影像报告和数据系统（LI-RADS）是对肝细胞癌高危患者肝脏影像观察进行分类和风险分层的卓越框架。在本综述中，讨论了肝细胞癌的发病机制以及MRI在LI-RADS中的应用，具体包括LI-RADS诊断算法、其组成部分以及参考最新支持证据的可重复性。对LI-RADS治疗反应算法进行了综述，包括最新的放射治疗反应算法。探讨了人工智能的应用、争议点、LI-RADS相对于其他肝脏影像系统的情况以及可能的未来方向。阅读本文后，读者将了解LI-RADS的基础和应用以及可能的未来方向。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b31/12335345/3302775ea66f/JMRI-62-640-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b31/12335345/d5c81f49291a/JMRI-62-640-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b31/12335345/3cf72f58e7a8/JMRI-62-640-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b31/12335345/4b20f77a52b2/JMRI-62-640-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b31/12335345/dac137c01d16/JMRI-62-640-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b31/12335345/a130e7f9770b/JMRI-62-640-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b31/12335345/682322757c47/JMRI-62-640-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b31/12335345/8300b2176de0/JMRI-62-640-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b31/12335345/6ad6dbdbf548/JMRI-62-640-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b31/12335345/5f035e242582/JMRI-62-640-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b31/12335345/3302775ea66f/JMRI-62-640-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b31/12335345/d5c81f49291a/JMRI-62-640-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b31/12335345/3cf72f58e7a8/JMRI-62-640-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b31/12335345/4b20f77a52b2/JMRI-62-640-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b31/12335345/dac137c01d16/JMRI-62-640-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b31/12335345/a130e7f9770b/JMRI-62-640-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b31/12335345/682322757c47/JMRI-62-640-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b31/12335345/8300b2176de0/JMRI-62-640-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b31/12335345/6ad6dbdbf548/JMRI-62-640-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b31/12335345/5f035e242582/JMRI-62-640-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b31/12335345/3302775ea66f/JMRI-62-640-g004.jpg

相似文献

[1]
Current State of Evidence for Use of MRI in LI-RADS.

J Magn Reson Imaging. 2025-9

[2]
Impact of LI-RADS CT and MRI Ancillary Features on Diagnostic Performance: An Individual Participant Data Meta-Analysis.

Radiology. 2025-7

[3]
Evidence Supporting LI-RADS Major Features for CT- and MR Imaging-based Diagnosis of Hepatocellular Carcinoma: A Systematic Review.

Radiology. 2018-1

[4]
Interactive Explainable Deep Learning Model for Hepatocellular Carcinoma Diagnosis at Gadoxetic Acid-enhanced MRI: A Retrospective, Multicenter, Diagnostic Study.

Radiol Imaging Cancer. 2025-5

[5]
Effect of combining serum alpha-fetoprotein with LI-RADS v2018 on gadoxetate-enhanced MRI in the diagnosis and prognostication of hepatocellular carcinoma.

Eur Radiol. 2025-2-10

[6]
LI-RADS in 2024: recent updates, planned refinements, and future directions.

Abdom Radiol (NY). 2024-12-13

[7]
External Validation of an Artificial Intelligence Algorithm Using Biparametric MRI and Its Simulated Integration with Conventional PI-RADS for Prostate Cancer Detection.

Acad Radiol. 2025-7

[8]
The added value of artificial intelligence to LI-RADS categorization: A systematic review.

Eur J Radiol. 2022-5

[9]
International perspectives on LI-RADS.

Abdom Radiol (NY). 2024-12-19

[10]
LI-RADS CT/MRI Radiation Treatment Response Algorithm Version 2024: Category Redistribution and Short-Term Outcomes in Patients Undergoing Y-90 Radioembolization for HCC.

AJR Am J Roentgenol. 2025-4-30

本文引用的文献

[1]
Do Risk Factors for HCC Impact the Association of CT/MRI LIRADS Major Features With HCC? An Individual Participant Data Meta-Analysis.

Can Assoc Radiol J. 2025-8

[2]
Utilizing a domain-specific large language model for LI-RADS v2018 categorization of free-text MRI reports: a feasibility study.

Insights Imaging. 2024-11-22

[3]
Risk of Bias in Liver Imaging Reporting and Data System Studies Using QUADAS-2.

Can Assoc Radiol J. 2025-5

[4]
Is concurrent LR-5 associated with a higher rate of hepatocellular carcinoma in LR-3 or LR-4 observations? An individual participant data meta-analysis.

Abdom Radiol (NY). 2025-4

[5]
Next-Gen Medical Imaging: U-Net Evolution and the Rise of Transformers.

Sensors (Basel). 2024-7-18

[6]
ChatGPT yields low accuracy in determining LI-RADS scores based on free-text and structured radiology reports in German language.

Front Radiol. 2024-7-5

[7]
A novel radiomics approach for predicting TACE outcomes in hepatocellular carcinoma patients using deep learning for multi-organ segmentation.

Sci Rep. 2024-6-26

[8]
Individual Participant Data Meta-Analyses for Diagnostic Accuracy Research: Challenges and Lessons Learned from the LI-RADS IPD Group.

Radiol Imaging Cancer. 2024-5

[9]
Improving automatic segmentation of liver tumor images using a deep learning model.

Heliyon. 2024-3-21

[10]
LI-RADS CT and MRI Ancillary Feature Association with Hepatocellular Carcinoma and Malignancy: An Individual Participant Data Meta-Analysis.

Radiology. 2024-2

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