Evidence map›Paper›PMID 42141059›Full record

ArticleScientific reports2026

Artificial intelligence iterative imaging reconstruction with low-dose CT scans for hepatic metastases.

Xiang Liu, Zhi-Lei Zhang, Xiao-Hui Qi, Jiang-Yang Pan, Xue-Li Fan, Jing Liu, Yi-Ting Sun, Qi Wang

Abstract read
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In one paragraph

Article in Scientific reports, 2026. 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
–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

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.

Xiang LiuDepartment of Radiology, the Fourth Hospital of Hebei Medical University, No. 12 Jiankang Road, Shijiazhuang, Hebei Province, China.
Zhi-Lei ZhangDepartment of Hepatobiliary Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Xiao-Hui QiDepartment of Radiology, the Fourth Hospital of Hebei Medical University, No. 12 Jiankang Road, Shijiazhuang, Hebei Province, China.
Jiang-Yang PanDepartment of Radiology, the Fourth Hospital of Hebei Medical University, No. 12 Jiankang Road, Shijiazhuang, Hebei Province, China.
Xue-Li FanDepartment of Radiology, the Fourth Hospital of Hebei Medical University, No. 12 Jiankang Road, Shijiazhuang, Hebei Province, China.
Jing LiuDepartment of Radiology, the Fourth Hospital of Hebei Medical University, No. 12 Jiankang Road, Shijiazhuang, Hebei Province, China.
Yi-Ting SunDepartment of Radiology, the Fourth Hospital of Hebei Medical University, No. 12 Jiankang Road, Shijiazhuang, Hebei Province, China.
Qi WangDepartment of Radiology, the Fourth Hospital of Hebei Medical University, No. 12 Jiankang Road, Shijiazhuang, Hebei Province, China. ja1109w@hebmu.edu.cn.

Funding

Medical Science Research Project of Hebei (grant number: 20210403)National Key R&D Program of China (grant number: 2023YFC2412205)
6 · The paper itself

Abstract

Contrast-enhanced CT is commonly used in the evaluation of hepatic metastatic lesions. This prospective study aimed to assess the capability of artificial intelligence iterative reconstruction (AIIR) in low-dose CT for detection of hepatic metastases. Thirty-two patients with hepatic metastases were enrolled and underwent dual-phase CT scans in the venous phase. Each patient received a standard-dose (SD) scan (120 kVp, 180 mAs) followed by a reduced-dose (RD) scan (120 kVp, 30 mAs). All data were analyzed. Compared with the SD CT scan, the volume CT dose index (CTDI

Indexed as

Artificial IntelligenceImage Processing, Computer-AssistedLiver NeoplasmsRadiographic Image Interpretation, Computer-AssistedTomography, X-Ray ComputedAdultAgedContrast MediaFemaleHumansLiverMaleMiddle AgedProspective StudiesRadiation DosageSignal-To-Noise RatioContrast MediaArtificial intelligenceIterative reconstructionLiver metastasisLow-dose CT scan

Identifiers

PMID42141059
PMCPMC13369155

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Registered trials

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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.