Evidence map›Paper›PMID 30643771›Full record

ArticleOcular oncology and pathology2018

Liver Imaging Techniques: Recognition of Uveal Melanoma Metastases.

Claudine Bellerive, Etienne Ouellet, Aya Kamaya, Arun D Singh

Open access · bronzeAbstract read
In one paragraph

Article in Ocular oncology and pathology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
1.7field-weighted citation impact, top 17% 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

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Article
  6. Review
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

4 authors at 3 institutions in 1 country.

Claudine BelleriveCole Eye Institute, Cleveland Clinic, Cleveland, OH, USA.
Etienne OuelletDepartment of Radiology, Stanford University Medical Center, Stanford, CA, USA.
Aya KamayaDepartment of Radiology, Stanford University Medical Center, Stanford, CA, USA.
Arun D SinghCole Eye Institute, Cleveland Clinic, Cleveland, OH, USA.
Cleveland Eye Clinic · USStanford Medicine · USStanford University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe liver is the most common site for metastases of several primary malignancies including uveal melanoma.

methodsReview of imaging characteristics of incidental common benign liver lesions including hepatic cyst, hemangioma, focal nodular hyperplasia, and hepatic adenoma and contrasting them with uveal melanoma metastases.

resultsBenign hepatic lesions may be cystic or, if solid, relatively stable in size over time. For hepatic lesions larger than 10 mm in size, characteristic imaging features typically allow for confident diagnosis. When lesions are small (less than 10 mm), definitive characterization can be difficult. Moreover, lesions smaller than 10 mm can be difficult to biopsy under ultrasound or computed tomography (CT) guidance, and short-term follow-up will often be useful to assess for stability or progression. Overall, magnetic resonance imaging is more specific than CT scan and at least as sensitive as CT for detecting uveal melanoma liver metastases.

conclusionsNew multiple enhancing solid liver lesions should raise suspicion of uveal melanoma liver metastases. Discussion of challenging cases with the radiologist may be beneficial, as pertinent information such as size, location, and molecular prognostication status of the primary tumor can guide radiological interpretation of hepatic lesions.

Indexed as

Hepatic cystHepatic hemangiomaLiver imagingMetastasisUveal melanoma

Identifiers

PMID30643771
PMCPMC6322083
OpenAlexW2792651990

What OpenQuestion holds

Textmetadata
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.