Evidence map›Paper›PMID 34243919›Full record

ReviewMagnetic resonance imaging clinics of North America2021

Computed Tomography Techniques, Protocols, Advancements, and Future Directions in Liver Diseases.

Naveen M Kulkarni, Alice Fung, Avinash R Kambadakone, Benjamin M Yeh

Open access · greenAbstract readReview
In one paragraph

Review in Magnetic resonance imaging clinics of North America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed, 1 pooled it
2.7field-weighted citation impact, top 10% 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

35 citing papers in PubMed, 1 synthesis or guideline pooled it, 55 citations in OpenAlex.

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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 at 4 institutions in 1 country.

Naveen M KulkarniDepartment of Radiology, Medical College of Wisconsin, 9200 W Wisconsin Avenue, Milwaukee, WI 53226, USA.
Alice FungDepartment of Radiology, Massachusetts General Hospital, White 270, 55 Fruit Street, Boston, MA 02114, USA.
Avinash R KambadakoneDepartment of Diagnostic Radiology, Oregon Health & Science University, SW Sam Jackson Park Road, L-340, Portland, OR 97239, USA.
Benjamin M YehDepartment of Radiology and Biomedical Imaging, UCSF Medical Center, 505 Parnassus Avenue, San Francisco, CA 94143-0628, USA. Electronic address: Benjamin.Yeh@ucsf.edu.
Massachusetts General Hospital · USMedical College of Wisconsin · USOregon Health & Science University · USUniversity of California San Francisco Medical Center · US

Funding

Nanoparticle CT Contrast Agent for New Capabilities in Tumor Detection, Staging, and Therapy Planning and ResponseR01CA226868 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI YEH, BENJAMIN M · 2019 to 2022
$3.1M
NCI NIH HHS R01 CA226868
6 · The paper itself

Abstract

Computed tomography (CT) is often performed as the initial imaging study for the workup of patients with known or suspected liver disease. Our article reviews liver CT techniques and protocols in clinical practice along with updates on relevant CT advances, including wide-detector CT, radiation dose optimization, and multienergy scanning, that have already shown clinical impact. Particular emphasis is placed on optimizing the late arterial phase of enhancement, which is critical to evaluation of hepatocellular carcinoma. We also discuss emerging techniques that may soon influence clinical care.

Indexed as

Carcinoma, HepatocellularLiver NeoplasmsContrast MediaHumansMagnetic Resonance ImagingTomography, X-Ray ComputedContrast MediaCTCT techniqueDual-energy CTLate arterial phaseLiver

Identifiers

PMID34243919
PMCPMC8388239
OpenAlexW3178271562

What OpenQuestion holds

Textmetadata
LicenceTDM
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.