Evidence map›Paper›PMID 37226012›Full record

ArticleInsights into imaging2023

Short-term PET-derived kinetic estimation for the diagnosis of hepatocellular carcinoma: a combination of the maximum-slope method and dual-input three-compartment model.

Tao Wang, Boqiao Li, Hong Shi, Pengfei Li, Yinglei Deng, Siyu Wang, Qiao Luo, Dongdong Xv, Jianfeng He, Shaobo Wang

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Insights into imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.9field-weighted citation impact, top 25% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Feasibility of Early DynamicMolecular imaging and biology · 2025
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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

10 authors at 1 institution in 1 country.

Tao Wang *Yunnan Key Laboratory of Artificial Intelligence, Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China.
Boqiao Li *Yunnan Key Laboratory of Artificial Intelligence, Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China.
Hong ShiYunnan Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China.
Pengfei LiPET/CT Center, Affiliated Hospital of Kunming University of Science and Technology, First People's Hospital of Yunnan, Kunming, 650031, China.
Yinglei DengPET/CT Center, Affiliated Hospital of Kunming University of Science and Technology, First People's Hospital of Yunnan, Kunming, 650031, China.
Siyu WangPET/CT Center, Affiliated Hospital of Kunming University of Science and Technology, First People's Hospital of Yunnan, Kunming, 650031, China.
Qiao LuoPET/CT Center, Affiliated Hospital of Kunming University of Science and Technology, First People's Hospital of Yunnan, Kunming, 650031, China.
Dongdong XvPET/CT Center, Affiliated Hospital of Kunming University of Science and Technology, First People's Hospital of Yunnan, Kunming, 650031, China.
Jianfeng HeYunnan Key Laboratory of Artificial Intelligence, Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, Yunnan, 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.ORCID http://orcid.org/0000-0002-2745-522X
Kunming University of Science and Technology · CN

Funding

the Basic Research on Application of Joint Special Funding of Science and Technology Department of Yunnan Province-Kunming Medical University No. 2018FE001(-291)the High-level Talent Project of Health in Yunnan Province, No. D-2018011the National Natural Science Foundation of China No. 81760306the National Natural Science Foundation of China No. 82160347the National Natural Science Foundation of China No. 82260355the Ten Thousand People Plan in Yunnan Province No. YNWR-QNBJ-2018-243Yunnan Key Laboratory of Smart City in Cyberspace Security No.202105AG070010
6 · The paper itself

Abstract

backgroundKinetic estimation provides fitted parameters related to blood flow perfusion and fluorine-18-fluorodeoxyglucose (

methodsThis study preliminarily evaluated the equivalence of liver kinetic estimation between short-term (5-min dynamic data supplemented with 1-min static data at 60 min postinjection) and fully 60-min dynamic protocols and whether short-term

resultsThere is a strong correlation between the kinetic parameters K

conclusionsShort-term PET is closely equivalent to fully dynamic PET for liver kinetic estimation. Short-term PET-derived kinetic parameters can be used to distinguish HCC from background liver tissue, and the combined model improves the kinetic estimation. CLINICAL RELEVANCE STATEMENT: Short-term PET could be used for hepatic kinetic parameter estimation. The combined model could improve the estimation of liver kinetic parameters.

Indexed as

Compartmental modelHepatocellular carcinomasPositron-emission tomography

Identifiers

PMID37226012
PMCPMC10209370
OpenAlexW4378070711

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.