Evidence map›Paper›PMID 40987943›Full record

ArticleInsights into imaging2025

Dynamic contrast-enhanced ultrasound perfusion analysis for preoperative prediction of aggressive hepatocellular carcinoma subtypes.

Xin Guan, Xiao Li, Daohui Yang, Chongke Zhao, Dan Lu, Yaqin Zhang, Yikang Sun, Boyang Zhou, Zitong Chen, Xinyuan Hu and 4 more

Abstract read
In one paragraph

Article in Insights into imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Article
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

14 authors.

Xin Guan *Department of Ultrasound, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China.
Xiao Li *Department of Ultrasound, Institute of Ultrasound in Medicine and Engineering, Zhongshan Hospital, Fudan University, Shanghai, China.
Daohui YangDepartment of Ultrasound, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China.
Chongke ZhaoDepartment of Ultrasound, Institute of Ultrasound in Medicine and Engineering, Zhongshan Hospital, Fudan University, Shanghai, China.
Dan LuDepartment of Ultrasound, Institute of Ultrasound in Medicine and Engineering, Zhongshan Hospital, Fudan University, Shanghai, China.
Yaqin ZhangDepartment of Ultrasound, Institute of Ultrasound in Medicine and Engineering, Zhongshan Hospital, Fudan University, Shanghai, China.
Yikang SunDepartment of Ultrasound, Institute of Ultrasound in Medicine and Engineering, Zhongshan Hospital, Fudan University, Shanghai, China.
Boyang ZhouDepartment of Ultrasound, Institute of Ultrasound in Medicine and Engineering, Zhongshan Hospital, Fudan University, Shanghai, China.
Zitong ChenDepartment of Ultrasound, Institute of Ultrasound in Medicine and Engineering, Zhongshan Hospital, Fudan University, Shanghai, China.
Xinyuan HuDepartment of Ultrasound, Institute of Ultrasound in Medicine and Engineering, Zhongshan Hospital, Fudan University, Shanghai, China.
Hong HanDepartment of Ultrasound, Institute of Ultrasound in Medicine and Engineering, Zhongshan Hospital, Fudan University, Shanghai, China.
Qing LuDepartment of Ultrasound, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China. lu.qing@zs-hospital.sh.cn.
Huixiong XuDepartment of Ultrasound, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China. xuhuixiong@126.com.ORCID http://orcid.org/0000-0002-8699-854X
Lifan WangDepartment of Ultrasound, Institute of Ultrasound in Medicine and Engineering, Zhongshan Hospital, Fudan University, Shanghai, China. 1364730590@qq.com.

Funding

National Nature Science Foundation of China 82402263Scientific Research and Development Fund of Zhongshan Hospital of Fudan University 2022ZSQD07
6 · The paper itself

Abstract

objectiveTo evaluate the potential of dynamic contrast-enhanced ultrasound (CEUS) quantitative parameters in preoperative prediction of macrotrabecular-massive (MTM) subtype and high Ki-67 pattern in hepatocellular carcinoma (HCC) patients. MATERIALS AND

methodsThis study included a retrospective primary cohort and a multicenter prospective validation cohort comprising HCC patients who underwent surgical resection and preoperative CEUS between January 2023 and April 2024. The Clinic-CEUS model was established by combining clinical data and CEUS features, while the Clinic-Q-CEUS model was constructed by combining clinical data and CEUS features with matched quantitative parameters. Model performance was tested with the area under the receiver operating characteristic curve (AUC) in the validation cohort.

resultsA total of 170 patients (mean age, 61 years ± 11 [SD]; 130 men; primary cohort, n = 118; validation cohort, n = 52) were included. The Clinic-Q-CEUS model better predicted MTM subtype and high Ki-67 pattern than the Clinic-CEUS model (AUC, 0.860 vs. 0.753, p = 0.027 and AUC, 0.836 vs. 0.738, p = 0.036) in the primary cohort, with similar performance in the validation cohort (AUC, 0.868 vs. 0.693, p = 0.046 and AUC, 0.787 vs. 0.610, p = 0.018).

conclusionsDynamic CEUS quantification analysis could be used as an effective adjunct tool for preoperative identification of MTM subtype and high Ki-67 pattern in HCC patients. CRITICAL RELEVANCE STATEMENT: Dynamic contrast-enhanced ultrasound (CEUS) quantitative parameters can help radiologists more accurately identify aggressive macrotrabecular-massive (MTM) subtype and high Ki-67 pattern in HCC patients preoperatively, which provides useful information for subsequent treatment planning. KEY POINTS: Macrotrabecular-massive (MTM) subtype and high Ki-67 pattern in HCC affect prognosis, but diagnosis relies on invasive histopathology. A clinical-Q-CEUS model performed well in preoperative predicting aggressive HCC subtypes. Quantitative parameters of dynamic CEUS can provide valuable information to help accurately identify aggressive HCC subtypes.

Indexed as

Contrast-enhanced ultrasoundHepatocellular carcinomaMacrotrabecular-massive subtypeQuantitative ultrasound

Identifiers

PMID40987943
PMCPMC12457242

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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.