Evidence map›Paper›PMID 39994556›Full record

ArticleBMC medical imaging2025

A prior information-based multi-population multi-objective optimization for estimating

Yiwei Xiong, Siming Li, Jianfeng He, Shaobo Wang

Abstract read
PubMed Full text at PMC Publisher
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
–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

4 authors.

Yiwei XiongFaculty of Information Engineering and Automation, Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming, Yunnan, 650500, China.
Siming LiFaculty of Information Engineering and Automation, Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming, Yunnan, 650500, China.
Jianfeng HeFaculty of Information Engineering and Automation, Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming, Yunnan, 650500, China. jfenghe@foxmail.com.
Shaobo WangPET/CT Center, Affiliated Hospital of Kunming University of Science and Technology, First People's Hospital of Yunnan, Kunming, 650031, China. wshbo_98@126.com.

Funding

Ten Thousand People Plan in Yunnan Province YNWR-QNBJ2018-243the Basic Research on Application of Joint Special Funding of Science and Technology Department of Yunnan Province-Kunming Medical University 202301AY070001-211the National Natural Science Foundation of China 82160347the Yunnan Key Laboratory of Smart City in Cyberspace Security 202105AG070010
6 · The paper itself

Abstract

background

methodsPET data from 24 hepatocellular carcinoma (HCC) tumors of 5-min dynamic PET/CT supplemented with 1-min static PET at 60 min were prospectively collected. A reversible double-input three-compartment model and kinetic parameters (K

resultsThe areas under the curve (AUCs) of the three p-MPMO methods were significantly higher than other methods in K

conclusionsThe p-MPMOO approach proposed in this paper performs well for distinguishing HCC tumors from normal liver tissue.

Indexed as

Carcinoma, HepatocellularFluorodeoxyglucose F18Liver NeoplasmsPositron Emission Tomography Computed TomographyRadiopharmaceuticalsAgedAlgorithmsFemaleHumansMaleMiddle AgedProspective StudiesFluorodeoxyglucose F18Radiopharmaceuticals18F FDG PET/CTMulti-population multi-objective optimizationPrior information

Identifiers

PMID39994556
PMCPMC11854238

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.