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LI-RADS Version 2018 Ancillary Features at MRI.

Milena Cerny1, Victoria Chernyak1, Damien Olivié1

  • 1From the Department of Radiology, Centre Hospitalier de l'Université de Montréal, 1000 rue Saint-Denis, Montréal, QC, Canada H2X 0C2 (M.C., D.O., J.S.B., J.M.L., L.B., A.T.); Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montréal, Quebec, Canada (M.C., A.T.); Department of Radiology, Montefiore Medical Center, Bronx, NY (V.C.); Department of Radiology, University of Ottawa, Ottawa, Ontario, Canada (A.Z.K.); Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (K.M.E.); and Liver Imaging Group, Department of Radiology, University of California San Diego, San Diego, Calif (J.C.H., C.B.S.).

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PubMed
Summary
This summary is machine-generated.

The Liver Imaging Reporting and Data System (LI-RADS) uses ancillary features to improve hepatocellular carcinoma (HCC) detection and characterization in liver imaging. These features help refine LI-RADS category assignments, enhancing diagnostic accuracy for HCC.

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Area of Science:

  • Radiology and Medical Imaging
  • Oncology
  • Hepatology

Background:

  • The Liver Imaging Reporting and Data System (LI-RADS) standardizes liver imaging for hepatocellular carcinoma (HCC) risk assessment.
  • LI-RADS categorization primarily relies on major imaging features but can be refined using ancillary features.
  • Ancillary features offer additional information to improve detection, characterization, and confidence in LI-RADS assessments.

Purpose of the Study:

  • To detail the ancillary features of LI-RADS version 2018 as applied to MRI.
  • To provide guidance on the application, rationale, and diagnostic performance of these ancillary features.
  • To illustrate ancillary features with clinical MRI examples and discuss potential pitfalls.

Main Methods:

  • Review and synthesis of LI-RADS version 2018 ancillary features applicable to MRI.
  • Classification of ancillary features into those favoring malignancy, HCC, or benignity.
  • Analysis of rules for applying ancillary features to adjust LI-RADS categories.

Main Results:

  • Ancillary features can upgrade LI-RADS categories by one level (up to LR-4) if favoring malignancy or HCC.
  • Ancillary features can downgrade categories by one level if favoring benignity.
  • Conflicting ancillary features (favoring both malignancy and benignity) preclude category adjustment.

Conclusions:

  • Ancillary features are valuable adjuncts to major imaging features in LI-RADS MRI interpretation.
  • Proper application of ancillary features can enhance the diagnostic accuracy of LI-RADS for HCC.
  • Understanding ancillary features and their limitations is crucial for accurate liver lesion characterization.