Evidence map›Paper›PMID 40214698›Full record

ArticleAbdominal radiology (New York)2025

Development of a model combining CEUS LI-RADS and clinical features for predicting glypican-3 expression in hepatocellular carcinoma.

Fen Huang, Jinshu Pang, Yuquan Wu, Yueting Sun, Rong Wen, Xiumei Bai, Wanxian Nong, Ruizhi Gao, Yun He, Cuiling Li and 2 more

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Article in Abdominal radiology (New York), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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1 · What the graph read from it

What it found

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3 · Its place in the literature

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4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Fen Huang *Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China.
Jinshu Pang *Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China.
Yuquan WuDepartment of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China.
Yueting SunDepartment of Medical Ultrasonics, The First Affliated Hospital of Sun Yat-Sen University, Guangzhou, China.
Rong WenDepartment of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China.
Xiumei BaiDepartment of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China.
Wanxian NongDepartment of Medical Ultrasound, Guangxi Hospital Division of The First Affiliated Hospital, Sun Yat-Sen University, Nanning, Guangxi Zhuang Autonomous Region, China.
Ruizhi GaoDepartment of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China.
Yun HeDepartment of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China.
Cuiling LiDepartment of Medical Ultrasound, Guangxi Hospital Division of The First Affiliated Hospital, Sun Yat-Sen University, Nanning, Guangxi Zhuang Autonomous Region, China.
Guangliang HuangDepartment of Medical Ultrasound, Guangxi Hospital Division of The First Affiliated Hospital, Sun Yat-Sen University, Nanning, Guangxi Zhuang Autonomous Region, China. huanggl9@mail.sysu.edu.cn.
Hong YangDepartment of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China. yanghong@gxmu.edu.cn.

Funding

Guangxi medical and health appropriate technology development and application project S-2023005Natural Science Foundation of Guangxi 2020GXNSFDA238005Self-raised Scientific Research Funds of Ministry of Health of Guangxi Province Z-A20240164
6 · The paper itself

Abstract

objectiveTo establish a predictive model incorporating clinical features and contrast-enhanced ultrasound (CEUS) liver Imaging Reporting and Data System (LI-RADS) for predicting glypican-3 (GPC3) expression in hepatocellular carcinoma (HCC).

methodsA total of 142 HCC patients between January 2020 to June 2021 in our institution were retrospectively analyzed. All patients underwent CEUS before surgery, and the reference standard was immunohistochemical analysis of surgical specimen. The clinical features, conventional ultrasound features, and CEUS LI-RADS features of patients in the GPC3-positive and GPC3-negative groups were evaluated and compared. The variables screened by multivariable logistic regression were used to develop a model for predicting GPC3 expression and the predictive precision and clinical utility of the model was evaluated using receiver operating characteristic analysis and decision curve analysis.

resultsAmong the 142 HCC patients, 96 (67.6%) were classified as LR-4/5 lesions, 46 (32.4%) were classified as LR-M lesions, 101 (71.1%) were GPC3-positive and 41 (28.9%) were negative. Multivariable logistic regression analysis showed that younger age (OR = 0.947; 95% CI: 0.910-0.985; p = 0.007), alpha-fetoprotein > 400 ng/ml (OR = 5.202; 95% CI: 1.808-14.966; p = 0.002) and LI-RADS M (OR = 2.822; 95% CI: 1.101-7.236; p = 0.031) was independent risk factors for GPC3-positive HCC. The model combining clinical features and LI-RADS categories showed better performance than single variable, with AUC of 0.759 (p < 0.05). The nomogram and decision curves revealed substantial clinical benefit of the prediction model in predicting GPC3 expression.

conclusionThe combined model incorporating clinical features and CEUS LI-RADS achieved a satisfactory performance for predicting GPC3 expression in HCC patients.

Indexed as

Carcinoma, HepatocellularGlypicansLiver NeoplasmsRadiology Information SystemsAdultAgedContrast MediaFemaleHumansMaleMiddle AgedPredictive Value of TestsRetrospective StudiesUltrasonographyContrast MediaGlypicansGPC3 protein, humanContrast-enhanced ultrasoundGlypican-3Hepatocellular carcinomaLiver imaging and reporting and data system

Identifiers

PMID40214698

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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.