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LI-RADS® ancillary features on CT and MRI

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Abstract

The Liver Imaging Reporting and Data System (LI-RADS) uses an algorithm to assign categories that reflect the probability of hepatocellular carcinoma (HCC), non-HCC malignancy, or benignity. Unlike other imaging algorithms, LI-RADS utilizes ancillary features (AFs) to refine the final category. AFs in LI-RADS v2017 are divided into those favoring malignancy in general, those favoring HCC specifically, and those favoring benignity. Additionally, LI-RADS v2017 provides new rules regarding application of AFs. The purpose of this review is to discuss ancillary features included in LI-RADS v2017, the rationale for their use, potential pitfalls encountered in their interpretation, and tips on their application.

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Abbreviations

AF:

Ancillary features

AASLD:

American Association for the Study of Liver Diseases

ADC:

Apparent diffusion coefficient

APHE:

Arterial phase hyperenhancement

CT:

Computed tomography

DWI:

Diffusion-weighted imaging

ECA:

Extracellular agents

HCC:

Hepatocellular carcinoma

HBP:

Hepatobiliary phase

LI-RADS:

Liver Imaging Reporting And Data System

MRI:

Magnetic resonance imaging

US:

Ultrasound

OPTN:

ORGAN Procurement and Transplantation Network

PVP:

Portal venous phase

TP:

Transitional phase

TVDT:

Tumor volume doubling time

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Acknowledgements

This work was supported by [1] the Fonds de recherche du Québec—Santé (Career Award#26993), and New Researcher Startup Grant from the Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM) to An Tang.

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Correspondence to Victoria Chernyak.

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V Chernyak, M. Flusberg, D. Papadatos, B. Bijan, C. Santillan: None. A. Tang, MD, MSc: Advisory board member of Imagia Cybernetics. Y. Kono: Research Grant support: Toshiba Medical Systems Co.; Contrast agent support: Lantheus Medical Imaging Inc.; Equipment support: GE Healthcare; Equipment support: Philips Ultrasound.

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Chernyak, V., Tang, A., Flusberg, M. et al. LI-RADS® ancillary features on CT and MRI. Abdom Radiol 43, 82–100 (2018). https://doi.org/10.1007/s00261-017-1220-6

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