Evidence map›Paper›PMID 32296952›Full record

ReviewCurrent oncology reports2020

Updates on Imaging of Liver Tumors.

Arya Haj-Mirzaian, Ana Kadivar, Ihab R Kamel, Atif Zaheer

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current oncology reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
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

13 citing papers in PubMed.

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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

4 authors.

Arya Haj-MirzaianThe Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 600 North Wolfe Street, Hal B164, Baltimore, MD, 21287, USA.
Ana KadivarThe Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 600 North Wolfe Street, Hal B164, Baltimore, MD, 21287, USA.
Ihab R KamelThe Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 600 North Wolfe Street, Hal B164, Baltimore, MD, 21287, USA.
Atif ZaheerThe Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 600 North Wolfe Street, Hal B164, Baltimore, MD, 21287, USA. azaheer1@jhmi.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewWe review advances in imaging of liver tumors, by particularly focusing on the utility of novel imaging in diagnosis and management of these lesions. RECENT

findingsContrast-enhanced CT and/or MRI are currently utilized for accurate diagnosis of liver tumors, but several ongoing studies are examining the use of other advanced techniques. Novel CT (i.e., dual-energy CT and perfusion CT), MRI (diffusion-weighted imaging, MR elastography, and T1 mapping), and image processing (texture analysis and artificial intelligence-based methods) techniques have emerged and can be used for precise characterization of liver tumors, quantification of treatment responses, and prediction of overall survival rate of patients. Recent advancements in imaging of liver tumors allowed for a precise assessment of tumor features. These evolving technologies can be utilized for applying individualized treatment based on the presence of specific imaging biomarkers.

Indexed as

Artificial IntelligenceElasticity Imaging TechniquesHumansLiver NeoplasmsMagnetic Resonance ImagingTomography, X-Ray ComputedArtificial intelligenceContrast-enhanced imagingCTDiagnosisDiffusion-weighted imagingDual-energy CTHepatocellular carcinomaImage processingLiver tumorMR elastographyMRIPerfusion CTT1 mappingTexture analysis

Identifiers

PMID32296952

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.