ArticleScientific reports2024
A novel radiomics approach for predicting TACE outcomes in hepatocellular carcinoma patients using deep learning for multi-organ segmentation.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- From black-box prediction to transparent insight: the status quo and paradigm shift of explainable artificial intelligence in hepatocellular carcinoma research.Journal of the Egyptian National Cancer Institute · 2026Review
- Human-AI Interaction in Interventional Radiology: A Narrative Review of Current Applications, Challenges, and Future Directions.Journal of imaging · 2026Review
- Fully automated CT-based quantitative body composition analysis for predicting survival in patients with HCC undergoing TACE: a dual-cohort study.Scientific reports · 2026Article
- Advancing risk stratification in intermediate-stage hepatocellular carcinoma undergoing transarterial chemoembolization: the promise of the NTAA model.Hepatobiliary surgery and nutrition · 2026Article
- Radiogenomics and machine learning in hepatocellular carcinoma: from foundations to clinical translation.World journal of surgical oncology · 2026Review
- Pioneering efficient deep learning architectures for enhanced hepatocellular carcinoma prediction and clinical translation.World journal of gastrointestinal oncology · 2026Article
- A bibliometric study on microenvironment-mediated drug resistance in interventional liver cancer treatment.Discover oncology · 2025Article
- From texture analysis to artificial intelligence: global research landscape and evolutionary trajectory of radiomics in hepatocellular carcinoma.Discover oncology · 2025Article
- A deep learning-based psi CT network effectively predicts early recurrence after hepatectomy in HCC patients.Abdominal radiology (New York) · 2025Article
- Current State of Evidence for Use of MRI in LI-RADS.Journal of magnetic resonance imaging : JMRI · 2025Review
- Hepatocellular Carcinoma: A Comprehensive Review.Diseases (Basel, Switzerland) · 2025Review
- Integrating CT Radiomics and Clinical Features to Optimize TACE Technique Decision-Making in Hepatocellular Carcinoma.Cancers · 2025Article
- WAW-TACE: A Hepatocellular Carcinoma Multiphase CT Dataset with Segmentations, Radiomics Features, and Clinical Data.Radiology. Artificial intelligence · 2024Article
- Research progress on machine algorithm prediction of liver cancer prognosis after intervention therapy.American journal of cancer research · 2024Review
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12 authors.
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Abstract
Transarterial chemoembolization (TACE) represent the standard of therapy for non-operative hepatocellular carcinoma (HCC), while prediction of long term treatment outcomes is a complex and multifactorial task. In this study, we present a novel machine learning approach utilizing radiomics features from multiple organ volumes of interest (VOIs) to predict TACE outcomes for 252 HCC patients. Unlike conventional radiomics models requiring laborious manual segmentation limited to tumoral regions, our approach captures information comprehensively across various VOIs using a fully automated, pretrained deep learning model applied to pre-TACE CT images. Evaluation of radiomics random survival forest models against clinical ones using Cox proportional hazard demonstrated comparable performance in predicting overall survival. However, radiomics outperformed clinical models in predicting progression-free survival. Explainable analysis highlighted the significance of non-tumoral VOI features, with their cumulative importance superior to features from the largest liver tumor. The proposed approach overcomes the limitations of manual VOI segmentation, requires no radiologist input and highlight the clinical relevance of features beyond tumor regions. Our findings suggest the potential of this radiomics models in predicting TACE outcomes, with possible implications for other clinical scenarios.
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