Evidence map›Paper›PMID 40597000›Full record

ArticleBMC medical imaging2025

Comparison of accuracy of CT parameters across chest, low-dose lung, and abdominal CT in diagnosing steatotic liver disease.

Yan Li, Jiahao Wang, Gengyu Xu, Yixin Si, Kaiyao Huang, Yinquan Ye, Yun Peng, Yuanyuan Liu

Abstract readComparative Study
In one paragraph

Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Yan LiDepartment of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China.
Jiahao WangDepartment of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China.
Gengyu XuDepartment of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China.
Yixin SiDepartment of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China.
Kaiyao HuangDepartment of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China.
Yinquan YeDepartment of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China.
Yun PengDepartment of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China.
Yuanyuan LiuDepartment of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China. 18970010699@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis study aims to determine the accuracy of computed tomography (CT) parameters obtained from three various scanning protocols (chest CT, low-dose lung CT, and abdominal CT) in diagnosing steatotic liver disease (SLD). MATERIALS AND

methodsThis retrospective study included 234 individuals who underwent chest CT, low-dose lung CT, or abdominal CT. SLD presence or absence was confirmed through ultrasound in all participants. Two radiologists independently measured the CT attenuation values of the liver (CT

resultsThe parameters (CT

conclusionBoth chest CT and low-dose CT-derived parameters demonstrated diagnostic accuracy comparable to that of abdominal CT in assessing SLD, suggesting their potential as viable alternatives in specific clinical scenarios.

Indexed as

Fatty LiverTomography, X-Ray ComputedAdultAgedFemaleHumansLiverLungMaleMiddle AgedRadiation DosageRadiography, AbdominalRadiography, ThoracicRetrospective StudiesROC CurveSensitivity and SpecificityAttenuation valueDiagnosisLow-dose computed tomographySteatotic liver disease

Identifiers

PMID40597000
PMCPMC12211915

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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.