Evidence map›Paper›PMID 39665988›Full record

ArticleEuropean radiology2025

Noninvasive identification of proliferative hepatocellular carcinoma on multiphase dynamic CT: quantitative and LI-RADS lexicon-based evaluation.

Wanli Zhang, Nan Li, Jiamin Li, Yue Zhao, Yi Long, Chutong He, Chuanxian Zhang, Bo Li, Yandong Zhao, Shengsheng Lai and 6 more

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Article in European radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

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0cells of the map it votes in
6citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

16 authors.

Wanli ZhangDepartment of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, China.
Nan LiDepartment of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, China.
Jiamin LiDepartment of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, China.
Yue ZhaoDepartment of Radiology, Central People's Hospital of Zhanjiang, Zhanjiang, China.
Yi LongDepartment of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, China.
Chutong HeMedical Imaging Center, Jinan University First Affiliated Hospital, Guangzhou, China.
Chuanxian ZhangDepartment of Radiology, The Zhaoqing Hospital of the Third Affiliated Hospital, Sun Yat-sen University, Zhaoqing, China.
Bo LiDepartment of Radiology, The First People's Hospital of Foshan, Foshan, China.
Yandong ZhaoDepartment of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, China.
Shengsheng LaiSchool of Medical Equipment, Guangdong Food and Drug Vocational College, Guangzhou, China.
Wenshuang DingDepartment of Pathology, Guangzhou First People's Hospital, Guangzhou, China.
Mingyong GaoDepartment of Radiology, The First People's Hospital of Foshan, Foshan, China.
Lilian TanDepartment of Radiology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Xinhua WeiDepartment of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, China.
Ruimeng Yang *Department of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, China. eyruimengyang@scut.edu.cn.
Xinqing Jiang *Department of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, China. eyjiangxq@scut.edu.cn.ORCID http://orcid.org/0000-0003-0773-523X

Funding

Basic and Applied Basic Research Foundation of Guangdong Province 2021A1515220060National Natural Science Foundation of China 81971574National Natural Science Foundation of China 82271938National Natural Science Foundation of China 82371908The Guangzhou Key Laboratory of Molecular Imaging and Clinical Translational Medicine 202201020376the Natural Science Program of Guangdong Food and Drug Vocational College 2021ZR03
6 · The paper itself

Abstract

objectiveTo identify proliferative hepatocellular carcinoma (HCC) preoperatively using quantitative measurements combined with the updated standard 2021 LI-RADS universal lexicon-based qualitative features on multiphase dynamic CT (MDCT).

methodsWe retrospectively analyzed 273 patients (102 proliferative HCCs) who underwent preoperative MDCT with surgically confirmed HCC in two medical centers. Imaging features were evaluated according to the updated 2021 LI-RADS universal lexicon, and quantitative measurements were analyzed. All MDCT findings and clinical factors were compared. Four predictive models (clinical, CT quantitative-clinical, CT qualitative-clinical, and combinational models) were developed and validated in an external cohort for identifying proliferative HCC. ROC analysis was used to assess model performances. All models were tested in a subgroup of patients with a single lesion ≤ 5 cm (n = 124).

resultsBoth the CT quantitative-clinical and CT qualitative-clinical models effectively identified proliferative HCC in the training and external validation cohorts (all AUCs > 0.79). The combinational model, integrating one clinical (AFP ≥ 200 ng/mL), three qualitative (rim arterial phase hyperenhancement (APHE), non-smooth tumor margin, and incomplete or absent capsule), and one quantitative feature (standardized tumor-to-aorta density ratio in portal venous phase ≤ (- 0.13), showed significant improvement in the training cohort (AUC 0.871) and comparable performance in the validation cohort (AUC 0.870). Additionally, AFP ≥ 200 ng/mL and Rim APHE were significantly associated with HCC recurrence (p < 0.05).

conclusionsThe combinational model, integrating clinical, CT quantitative, and qualitative features, shows potential for the noninvasively preoperative prediction of proliferative HCC. Further validation is needed to establish its broader clinical utility. KEY POINTS: Question Preoperative identification of proliferative HCC could influence patient treatment and prognosis, yet there is no CT-based universally applicable model to identify this subtype. Findings The updated standard 2021 LI-RADS universal lexicon-based features, in combination with quantitative MDCT measurements, could aid in the noninvasive detection of proliferative HCC. Clinical relevance The updated standard 2021 LI-RADS universal lexicon-based CT qualitative features and quantitative measurements may aid in identifying proliferative HCC and tumor recurrence, offering potential guidance for personalized treatment. Further studies are required to assess their generalizability to different clinical scenarios.

Indexed as

Carcinoma, HepatocellularLiver NeoplasmsAdultAgedFemaleHumansMaleMiddle AgedRetrospective StudiesTomography, X-Ray ComputedComputed tomographyCytokeratin 19Liver imaging reporting and data systemNomogramProliferative hepatocellular carcinoma

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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.