Evidence map›Paper›PMID 38137868›Full record

ReviewLife (Basel, Switzerland)2023

Hepatocellular Carcinoma: Optimal Radiological Evaluation before Liver Transplantation.

Marco Dioguardi Burgio, Lorenzo Garzelli, Roberto Cannella, Maxime Ronot, Valérie Vilgrain

Open access · goldAbstract readReview
In one paragraph

Review in Life (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.2field-weighted citation impact, top 20% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 2 countries.

Marco Dioguardi BurgioDepartment of Radiology, Hôpital Beaujon, AP-HP. Nord, 100 Boulevard du Général Leclerc, 92110 Clichy, France.ORCID 0000-0002-3243-6699
Lorenzo GarzelliService d'Imagerie Medicale, Centre Hospitalier de Cayenne, Avenue des Flamboyants, Cayenne 97306, French Guiana.
Roberto CannellaDepartment of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University Hospital "Paolo Giaccone", Via del Vespro 129, 90127 Palermo, Italy.ORCID 0000-0002-3808-0785
Maxime RonotDepartment of Radiology, Hôpital Beaujon, AP-HP. Nord, 100 Boulevard du Général Leclerc, 92110 Clichy, France.ORCID 0000-0001-7464-3939
Valérie VilgrainDepartment of Radiology, Hôpital Beaujon, AP-HP. Nord, 100 Boulevard du Général Leclerc, 92110 Clichy, France.ORCID 0000-0002-3568-7725
Université Paris Cité · FRCentre Hospitalier Andrée Rosemon · GF

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Liver transplantation (LT) is the recommended curative-intent treatment for patients with early or intermediate-stage hepatocellular carcinoma (HCC) who are ineligible for resection. Imaging plays a central role in staging and for selecting the best LT candidates. This review will discuss recent developments in pre-LT imaging assessment, in particular LT eligibility criteria on imaging, the technical requirements and the diagnostic performance of imaging for the pre-LT diagnosis of HCC including the recent Liver Imaging Reporting and Data System (LI-RADS) criteria, the evaluation of the response to locoregional therapy, as well as the non-invasive prediction of HCC aggressiveness and its impact on the outcome of LT. We will also briefly discuss the role of nuclear medicine in the pre-LT evaluation and the emerging role of artificial intelligence models in patients with HCC.

Indexed as

carcinomahepatocellularlivertransplantation, liver

Identifiers

PMID38137868
PMCPMC10744421
OpenAlexW4389045289

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.