Evidence map›Paper›PMID 41105281›Full record

ArticleThe ultrasound journal2025

B-mode ultrasound and contrast-enhanced ultrasound-based radiomics interpretable analysis for the prediction of macrotrabecular-massive subtype of hepatocellular carcinoma.

Dan Lu, Cheng Qin, Li-Fan Wang, Ling-Ling Li, Yu Li, Li-Ping Sun, Hui Shi, Bo-Yang Zhou, Xin Guan, Yao Miao and 4 more

Erratum issuedAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The ultrasound journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. [Ultrasound technology's new application in the diagnosis and treatment of liver cancer].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2026
    Review
  4. Review
  5. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors.

Dan Lu *Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China.
Cheng Qin *School of Future Technology, Shanghai University, Shanghai, China.
Li-Fan WangDepartment of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China.
Ling-Ling LiDepartment of Ultrasound, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Provincial Clinical Research Center for Cancer, Guangzhou, Guangdong, China.
Yu LiDepartment of Ultrasound, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Provincial Clinical Research Center for Cancer, Guangzhou, Guangdong, China.
Li-Ping SunDepartment of Medical Ultrasound, Center of Minimally Invasive Treatment for Tumor, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Hui ShiDepartment of Medical Ultrasound, Center of Minimally Invasive Treatment for Tumor, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Bo-Yang ZhouDepartment of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China.
Xin GuanDepartment of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China.
Yao MiaoDepartment of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China.
Hong HanDepartment of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China.
Jian-Hua ZhouDepartment of Ultrasound, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Provincial Clinical Research Center for Cancer, Guangzhou, Guangdong, China.
Hui-Xiong XuDepartment of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China. xuhuixiong@126.com.
Chong-Ke ZhaoDepartment of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China. zhaochongke123@163.com.

Funding

Fundamental Research Funds for the Central Universities ZD-11-202151National Natural Science Foundation of China 82402263National Natural Science Foundation of China 82402268National Natural Science Foundation of China 82430064National Natural Science Foundation of China 82502349National Natural Science Foundation of China 82572222Scientific Research and Development Fund of Zhongshan Hospital of Fudan University 2022ZSQD07
6 · The paper itself

Abstract

backgroundThis study aimed to develop and validate an interpretable radiomics model using quantitative features from B-mode ultrasound (BMUS) and contrast-enhanced ultrasound (CEUS) for predicting macrotrabecular-massive (MTM) hepatocellular carcinoma (HCC).

methodsFrom October 2020 to September 2023, 344 patients (mean age: 58.20 ± 10.70 years; 275 men) with surgically resected HCC were retrospectively enrolled from three medical centers. Radiomics features were extracted from BMUS and CEUS, followed by a multiple-step feature selection process. BMUS

resultsAmong ten distinct machine learning classifiers evaluated, the AdaBoost algorithm demonstrated the highest classification performance. The AUCs of the BM + CEUS

conclusionThe AdaBoost-based BM + CEUS

Indexed as

Contrast enhanced ultrasoundMacrotrabecular-massive hepatocellular carcinomaPrognosisRadiomicsSHapley additive explanations

Identifiers

PMID41105281
PMCPMC12534629

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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.