Evidence map›Paper›PMID 39896274›Full record

ArticleJournal of hepatocellular carcinoma2025

TRACE Model: Predicting Treatment Response to Transarterial Chemoembolization in Unresectable Hepatocellular Carcinoma.

Weilang Wang, Qi Zhang, Ying Cui, Shuhang Zhang, Binrong Li, Tianyi Xia, Yang Song, Shuwei Zhou, Feng Ye, Wenbo Xiao and 9 more

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of hepatocellular carcinoma, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03113955 (A Single-arm Trial of Transcatheter Arterial Chemoembolization With Tandem Microspheres in the Treatment of Localized Hepatocellular Carcinoma), which is not on this map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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.

NCT03113955 nacompletednot on this map

A Single-arm Trial of Transcatheter Arterial Chemoembolization With Tandem Microspheres in the Treatment of Localized Hepatocellular Carcinoma

TypeinterventionalSponsorVarian Medical SystemsRan2017 to 2020Enrolled109ConditionsLocalized Hepatocellular CarcinomaArmsTandem Microsphere loaded with Epirubicin
3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

19 authors.

Weilang Wang *Department of Radiology, Zhongda Hospital, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, School of Medicine, Southeast University, Nanjing, 210009, People's Republic of China.
Qi Zhang *Center of Interventional Radiology and Vascular Surgery, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, People's Republic of China.
Ying Cui *Department of Radiology, Zhongda Hospital, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, School of Medicine, Southeast University, Nanjing, 210009, People's Republic of China.
Shuhang ZhangDepartment of Radiology, Zhongda Hospital, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, School of Medicine, Southeast University, Nanjing, 210009, People's Republic of China.
Binrong LiDepartment of Radiology, Zhongda Hospital, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, School of Medicine, Southeast University, Nanjing, 210009, People's Republic of China.
Tianyi XiaDepartment of Radiology, Zhongda Hospital, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, School of Medicine, Southeast University, Nanjing, 210009, People's Republic of China.
Yang SongMR Scientific Marketing, Siemens Healthineers Ltd, Shanghai, People's Republic of China.
Shuwei ZhouDepartment of Radiology, Zhongda Hospital, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, School of Medicine, Southeast University, Nanjing, 210009, People's Republic of China.
Feng YeDepartment of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China.
Wenbo XiaoDepartment of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, People's Republic of China.
Kun CaoDepartment of Radiology, Peking University Cancer Hospital and Institute, Beijing, People's Republic of China.
Yuan ChiDepartment of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China.
Jinrong QuDepartment of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, Henan, People's Republic of China.ORCID 0000-0001-8751-9988
Guofeng ZhouDepartment of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China.
Zhao ChenDepartment of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, People's Republic of China.
Teng ZhangInstitute for Artificial Intelligence in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, People's Republic of China.
Xunjun ChenDepartment of Radiology, The People's Hospital of Xuyi County, Huaian, Jiangsu, People's Republic of China.
Shenghong JuDepartment of Radiology, Zhongda Hospital, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, School of Medicine, Southeast University, Nanjing, 210009, People's Republic of China.
Yuan-Cheng WangDepartment of Radiology, Zhongda Hospital, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, School of Medicine, Southeast University, Nanjing, 210009, People's Republic of China.ORCID 0000-0002-6253-8311

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To develop and validate a predictive model for predicting six-month outcome by integrating pretreatment MRI features and one-month treatment response after TACE. Methods: A total of 108 patients with 160 hCCs from a single-arm, multicenter clinical trial (NCT03113955) were analyzed and served as the training cohort. An external multicenter dataset (ChiCTR2100046020) consisting of 63 patients with 99 hCCs served as the test dataset. Radiomics model was constructed based on the selected features from pretreatment MR images. Univariate and multivariate logistic regression analysis of clinical and radiological factors were used to identify the independent predictors for the 6-month treatment response. A combined model was further constructed by incorporating one-month treatment response, selected clinical and radiological factors and radiomics signature. Results: Among all the clinical and radiological features, only corona enhancement and one-month treatment response were selected. The combined model, named TRACE model (Treatment response at 1 month, RAdiomics and Corona Enhancement), with AUCs of 0.91 (training cohort) and 0.84 (test cohort). The TRACE model demonstrated a significantly higher AUC than the radiomics model ( Conclusion: The TRACE model exhibited favorable predictive capability for six-month TACE response, and holds potential as a marker for long-term survival outcomes.

Indexed as

hepatocellular carcinomaradiomicstransarterial chemoembolizationtreatment response

Identifiers

PMID39896274
PMCPMC11787783

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Registered trials

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.