Evidence map›Paper›PMID 35978727›Full record

ArticleAustralasian journal of ultrasound in medicine2022

Algorithm-based approach to focal liver lesions in contrast-enhanced ultrasound.

Kheng Song Leow, Christine Ying Kwok, Hsien Min Low, Rahul Lohan, Tze Chwan Lim, Su Chong Albert Low, Cher Heng Tan

Open access · bronzeAbstract read
In one paragraph

Article in Australasian journal of ultrasound in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 36% 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

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. 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

7 authors at 5 institutions in 1 country.

Kheng Song LeowDepartment of Radiology Woodlands Health Campus 2 Yishun Central 2, Tower E, Level 5 Singapore Singapore.ORCID https://orcid.org/0000-0001-5342-1464
Christine Ying KwokDepartment of Diagnostic Radiology Tan Tock Seng Hospital 11 Jalan Tan Tock Seng Singapore 308433 Singapore.
Hsien Min LowDepartment of Diagnostic Radiology Tan Tock Seng Hospital 11 Jalan Tan Tock Seng Singapore 308433 Singapore.
Rahul LohanDepartment of Diagnostic Radiology Khoo Teck Puat Hospital 90 Yishun Central Singapore Singapore.
Tze Chwan LimDepartment of Radiology Woodlands Health Campus 2 Yishun Central 2, Tower E, Level 5 Singapore Singapore.
Su Chong Albert LowDepartment of Diagnostic Radiology Singapore General Hospital Outram Road Singapore 169608 Singapore.
Cher Heng TanDepartment of Diagnostic Radiology Tan Tock Seng Hospital 11 Jalan Tan Tock Seng Singapore 308433 Singapore.
Tan Tock Seng Hospital · SGWoodlands Health Campus · SGKhoo Teck Puat Hospital · SGNanyang Technological University · SGSingapore General Hospital · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Focal liver lesions are commonly encountered. Grey-scale and Doppler sonographic characteristics of focal liver lesions are often non-specific and insufficient to conclusively characterise lesions as benign or malignant. Contrast-enhanced ultrasound is useful for the characterisation of FLLs in patients who are unable to undergo contrast-enhanced computed tomography or magnetic resonance imaging. It is also easily available and relatively cheap. However, interpretation of contrast-enhanced ultrasound can be challenging without a systematic approach. In this pictorial essay, we highlight an algorithm-based approach to FLLs and discuss the characteristic contrast-enhanced ultrasound features of commonly encountered and clinically significant focal liver lesions.

Indexed as

algorithmcontrastliver neoplasmsultrasonography

Identifiers

PMID35978727
PMCPMC9351431
OpenAlexW4284692870

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.