Evidence map›Paper›PMID 41213031›Full record

ArticleHepatology (Baltimore, Md.)2026

Decoding tumor heterogeneity with imaging biomarkers predicts response to TACE plus immunotherapy and targeted therapy in HCC (CHANCE2204).

Zhi-Cheng Jin, Jingwei Wei, Yu-Dong Xiao, Anfeng Si, Jian-Jian Chen, Xiao-Li Zhu, Jin-Ze Li, Fang Nie, Rong Ding, Hai-Feng Zhou and 31 more

Registry-linked trialAbstract readMulticenter Study
In one paragraph

Article in Hepatology (Baltimore, Md.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07584317 (Artificial Intelligence-Based Multimodal Data Integration for Tumor Microenvironment Analysis and Response Prediction in Hepatocellular Carcinoma Patients Undergoing TACE Combined With Immunotherapy and Targeted Therapy), which is not on this map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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.

NCT07584317 not yet recruitingnot on this map

Artificial Intelligence-Based Multimodal Data Integration for Tumor Microenvironment Analysis and Response Prediction in Hepatocellular Carcinoma Patients Undergoing TACE Combined With Immunotherapy and Targeted Therapy

Typeobservational_patient_registrySponsorGao-jun TengRan2026 to 2028Enrolled1,170ConditionsHepatocellular CarcinomaArmsArtificial Intelligence
3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

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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

41 authors.

Zhi-Cheng JinDepartment of Radiology, Center of Interventional Radiology and Vascular Surgery, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, China.ORCID 0000-0002-6114-154
Jingwei WeiFoundation Model Research Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Yu-Dong XiaoDepartment of Radiology, Center of Interventional Radiology and Vascular Surgery, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, China.
Anfeng SiDepartment of General Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, China.
Jian-Jian ChenDepartment of Radiology, Center of Interventional Radiology and Vascular Surgery, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, China.ORCID 0000-0002-4946-9446
Xiao-Li ZhuDepartment of Interventional Radiology, The First Affiliated Hospital of Soochow University, Soochow University, Suzhou, China.
Jin-Ze LiDepartment of Radiology, Center of Interventional Radiology and Vascular Surgery, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, China.
Fang NieDepartment of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, China.
Rong DingDepartment of Minimally Invasive Interventional Medicine, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, China.
Hai-Feng ZhouDepartment of Interventional Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Wei DingDepartment of Interventional Radiology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, China.
Bin-Yan ZhongDepartment of Interventional Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
Yangyang XieZhejiang Key Laboratory of Multi-omics Precision Diagnosis and Treatment of Liver Diseases, Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Hong-Tao HuDepartment of Minimally Invasive Intervention, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China.
Guo-Wen YinDepartment of Interventional Radiology, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China.
Jian-Song JiDepartment of Radiology, Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, School of Medicine, Lishui Hospital of Zhejiang University, Lishui, China.
Wei-Hua ZhangDepartment of Radiology, Center of Interventional Radiology and Vascular Surgery, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, China.
Hai-Bin ShiDepartment of Interventional Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Jian-Bing WuDepartment of Oncology, The Second Affiliated Hospital of Nanchang University, Nanchang, China.
Guo-Hui XuDepartment of Interventional Radiology, Sichuan Cancer Hospital and Institute, Chengdu, China.
Chun-Wang YuanCenter of Interventional Oncology and Liver Diseases, Beijing Youan Hospital, Capital Medical University, Beijing, China.
Wei-Zhu YangDepartment of Interventional Radiology, Union Hospital of Fujian Medical University, Fuzhou, China.
Rui-Bao LiuDepartment of Interventional Radiology, The Tumor Hospital of Harbin Medical University, Harbin, China.
Ye-Ming WuDepartment of Radiology, Center of Interventional Radiology and Vascular Surgery, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, China.ORCID 0009-0009-6542-6065
Chuan-Sheng ZhengDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Ai-Bing XuDepartment of Interventional Therapy, Nantong Tumor Hospital, Nantong, China.
Ming-Sheng HuangDepartment of Interventional Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
Jia-Ping LiDepartment of Interventional Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Li ChenDepartment of Radiology, Center of Interventional Radiology and Vascular Surgery, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, China.
Shu-Wei WenDepartment of Interventional Therapy, Shanxi Tumor Hospital, Taiyuan, China.
Yu-Qing WangDepartment of Radiology, Center of Interventional Radiology and Vascular Surgery, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, China.
Shan-Zhi GuInterventional Department, Hunan Provincial Tumor Hospital, Changsha, China.
Dongrui LiDepartment of General Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang, China.
Duo WangDepartment of Radiology, Center of Interventional Radiology and Vascular Surgery, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, China.
Guan-Hui ZhouHepatobiliary and Pancreatic Interventional Treatment Center, Division of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Wei-Dong WangDepartment of Interventional Radiology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China.
Zhenwei PengCancer Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Xinying WangClinical Nutrition Service Center, Department of General Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, China.
Hai-Dong ZhuDepartment of Radiology, Center of Interventional Radiology and Vascular Surgery, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, China.
Jie TianKey Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Gao-Jun TengDepartment of Radiology, Center of Interventional Radiology and Vascular Surgery, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, China.ORCID 0000-0003-1145-0798

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

aimsThis study aims to quantify intratumoral heterogeneity (ITH) and identify prognostic imaging biomarkers in patients with HCC treated with transarterial chemoembolization combined with immune checkpoint inhibitor plus molecular targeted therapy (TACE-ICI-MTT). APPROACH AND

resultsThis multicenter cohort study included 742 patients with unresectable HCC who received first-line TACE-ICI-MTT from January 2018 to December 2022. Radiomic features representing global tumor regions (GTRs) and ITH were extracted from pretreatment CT scans. A composite GTR-ITH score was generated using principal component analysis to integrate both GTR- and ITH-related features. Ensemble learning with multiple machine learning algorithms was employed to predict treatment response. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC). Following feature selection, 17 GTR- and 27 ITH-related radiomic features were retained to construct the GTR-ITH score. The model demonstrated high discriminative performance, with AUCs of 0.94 in the training set, 0.82 in the internal validation set, and 0.83 in the independent test set. The GTR-ITH score was strongly associated with treatment response (OR: 34.39; p<0.001) and independently predicted overall survival (HR: 0.63; p=0.004). Patients classified as GTR-ITH low-risk consistently showed significantly prolonged progression-free survival and overall survival. To reveal the biological relevance of the radiomic score, immune infiltration patterns were characterized using bulk RNA sequencing data from the Cancer Imaging Archive. The GTR-ITH low-risk group also exhibited an immune-inflamed microenvironment characterized by enriched plasma cells and M1 macrophages, and reduced M2 macrophage infiltration.

conclusionsAn imaging radiomic biomarker that captures both global and intratumoral heterogeneity robustly predicts response and survival in patients with HCC treated with TACE-ICI-MTT, and reflects underlying immune microenvironment phenotypes.

Indexed as

Carcinoma, HepatocellularChemoembolization, TherapeuticImmunotherapyLiver NeoplasmsAgedBiomarkers, TumorCombined Modality TherapyFemaleHumansImmune Checkpoint InhibitorsMaleMiddle AgedMolecular Targeted TherapyPrognosisRadiomicsTomography, X-Ray ComputedBiomarkers, TumorImmune Checkpoint Inhibitorsbiomarkercarcinomachemoembolization therapeuticcombined modality therapyhepatocellularimmunotherapymolecular targeted therapyradiomicstherapeutic

Identifiers

PMID41213031
PMCPMC13668028

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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.