ArticleHepatology (Baltimore, Md.)2026
Decoding tumor heterogeneity with imaging biomarkers predicts response to TACE plus immunotherapy and targeted therapy in HCC (CHANCE2204).
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.
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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.
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
Who cites it
12 citing papers in PubMed.
- Radiomics-based outcome prediction for irinotecan-TACE in colorectal liver metastases: advanced analysis from the prospective CIREL trial.European radiology · 2026Article
- Interpretable machine learning for Parkinson's disease diagnosis, staging, and biological mechanism exploration: a multicenter analysis.BioData mining · 2026Article
- Perioperative approaches for patients with biliary tract cancer.Nature reviews. Clinical oncology · 2026Review
- Multimodal radiomics incorporating intratumoral heterogeneity for prognostic assessment of metastatic outcomes in invasive breast cancer.Breast cancer research : BCR · 2026Article
- From Tumor to Tumor-Spleen: MRI Habitat Heterogeneity for Predicting Immunotherapy Outcome in Advanced Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2026Article
- Association of week-2 Contrast-Enhanced Ultrasound Perfusion Changes with Response to Lenvatinib Plus PD-1 Inhibitors in Unresectable Hepatocellular Carcinoma: A Retrospective Exploratory Study.Journal of hepatocellular carcinoma · 2026Article
- HepaKineticNet: Deep Learning of CT-Derived Intratumoral Heterogeneity for Overall Survival Prediction in BCLC Stage B-C Hepatocellular Carcinoma Treated with Locoregional Therapy Plus First-Line TKI and Anti-PD-1 Therapy.Journal of hepatocellular carcinoma · 2026Article
- DEB-TACE-HAIC combined with donafenib and camrelizumab in the treatment of unresectable hepatocellular carcinoma: a multicenter retrospective study.Frontiers in immunology · 2026Article
- Hepatitis B virus infection and outcomes of TACE plus lenvatinib and PD-1 inhibitor therapy in unresectable hepatocellular carcinoma: a real-world propensity score-matched study.Frontiers in immunology · 2026Article
- Artificial intelligence in transarterial chemoembolization for hepatocellular carcinoma: from treatment response prediction to refractoriness assessment.Frontiers in oncology · 2026Review
- Radiomics and Deep Learning: Bridging Breast Cancer Imaging Phenotypes and Genomic Heterogeneity.Breast cancer (Dove Medical Press) · 2026Review
- Preoperative 3D morphological phenotyping of hepatocellular carcinoma via a foundation model: a non-invasive biomarker for recurrence risk stratification.eGastroenterology · 2026Article
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Authors and funding
41 authors.
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No grant is acknowledged in the PubMed record.
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.
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