Fat-containing hepatocellular carcinoma in patients with cirrhosis: proposal of a diagnostic modification regarding enhancement characteristics.

Carcinoma, hepatocellular Fatty Liver cirrhosis Liver neoplasms Magnetic resonance imaging

Journal

European radiology
ISSN: 1432-1084
Titre abrégé: Eur Radiol
Pays: Germany
ID NLM: 9114774

Informations de publication

Date de publication:
Apr 2024
Historique:
received: 26 08 2022
accepted: 03 08 2023
revised: 28 07 2023
pubmed: 11 10 2023
medline: 11 10 2023
entrez: 10 10 2023
Statut: ppublish

Résumé

The aim of this study was to develop and validate an algorithm for the non-invasive diagnosis of these fat-containing HCCs. Eighty-four cirrhotic patients with 77 fat-containing HCCs and 11 non-HCC fat-containing nodules were retrospectively included. All MRIs were reviewed; nodule characteristics, European Association for the Study of the Liver (EASL) and LI-RADS classifications, and survival were collected. One of the major features of LI-RADS v2018 (non-rim-like arterial phase hyperenhancement [APHE]) was changed to include different enhancing patterns at arterial phase and a new fat-LI-RADS algorithm was created for fat-containing nodules in cirrhosis. Its diagnostic performance was evaluated in both a derivation and external validation cohort (external cohort including 58 fat-containing HCCs and 10 non-HCC fat nodules). Reproducibility of this new algorithm was assessed. In the derivation cohort, 54/77 (70.1%) fat-containing HCCs had APHE, 62/77 (80.5%) had enhancement compared to the nodule itself at arterial phase (APE), 43/77 (55.8%) had washout, and 20/77 (26.0%) had an enhancing capsule. EASL and LI-RADS had a sensitivity of 37.7% (29/77) and 36.4% (28/77), respectively, for the diagnosis of fat-containing HCC and both had a specificity of 100% (11/11). The new fat-LI-RADS algorithm increased sensitivity to 50.6% (39/77) without decreasing the specificity of 100% (11/11). The validation cohort confirmed the increased sensitivity, with a slight decrease in specificity. The concordance for the diagnosis of HCC for fat-LR5 was 85.3% (58/68). The new fat-LI-RADS algorithm proposed here significantly improves the performance of the non-invasive diagnosis of fat-containing HCC and thus could reduce the number of biopsies conducted for fat-containing HCCs. The European Association for the Study of the Liver and LI-RADS guidelines are poorly sensitive for the diagnosis of fat-containing HCC, mainly because of the low rate of arterial phase hyperenhancement (APHE) displayed by fat-containing HCC. Using all types of enhancement instead of APHE improves sensitivity of LI-RADS. • Fat-containing HCCs on MRI account for 7.5% of HCCs and have different imaging characteristics from non-fatty HCCs. • The European Association for the Study of the Liver and LI-RADS algorithms for the non-invasive diagnosis of HCC have low sensitivity for the diagnosis of fat-containing HCC with MRI (37.7% and 36.4%, respectively). • The new fat-LI-RADS, which includes a slight modification of the "arterial enhancement" criterion, improves the sensitivity for the diagnosis of fat-containing HCC using MRI, without degrading the specificity.

Identifiants

pubmed: 37816923
doi: 10.1007/s00330-023-10236-8
pii: 10.1007/s00330-023-10236-8
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

2283-2293

Informations de copyright

© 2023. The Author(s), under exclusive licence to European Society of Radiology.

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Auteurs

Anaïs Delagnes (A)

Department of Radiology, Angers University Hospital, 4 Rue Larrey, 49933, Angers, France. anais.delagnes@chu-angers.fr.

Marine Roux (M)

HIFIH Laboratory, EA 3859, UNIV Angers, 49045, Angers, France.

Valérie Vilgrain (V)

Department of Radiology, APHP, University Hospitals Paris Nord Val de Seine, Beaujon, Clichy, France.
INSERM U1149, CRI, University Paris Diderot, Paris, France.

Boris Guiu (B)

Department of Radiology, Saint-Eloi Hospital, University Hospital of Montpellier, Montpellier, France.

Valérie Laurent (V)

Department of Radiology, Nancy University Hospital, Lorraine University, 54500, Vandœuvre-Lès-Nancy, France.

Olivier Sutter (O)

Department of Radiology, Avicenne Hospital, Paris-Seine-Saint-Denis University Hospitals, Assistance-Publique Hôpitaux de Paris, 93000, Bobigny, France.

Ivan Bricault (I)

Laboratory of Techniques for Biomedical Engineering and Complexity Management, University Grenoble Alpes, National Center for Scientific Research, Grenoble, France.
Department of Radiology and Medical Imaging, University Hospital, Grenoble, La Tronche, France.

Hervé Trillaud (H)

Department of Diagnostic and Interventional Radiology, University Hospital Bordeaux, 33600, Pessac, France.
EA Imotion, University of Bordeaux, 33076, Bordeaux, France.

Christophe Aubé (C)

Department of Radiology, Angers University Hospital, 4 Rue Larrey, 49933, Angers, France.
HIFIH Laboratory, EA 3859, UNIV Angers, 49045, Angers, France.

Anita Paisant (A)

Department of Radiology, Angers University Hospital, 4 Rue Larrey, 49933, Angers, France.
HIFIH Laboratory, EA 3859, UNIV Angers, 49045, Angers, France.

Classifications MeSH