SynthesisJournal of the American College of Radiology : JACR2026
Performance of Artificial Intelligence Models in Predicting Responsiveness of Hepatocellular Carcinoma to Transarterial Chemoembolization (TACE): A Systematic Review and Meta-Analysis.
Synthesis in Journal of the American College of Radiology : JACR, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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.
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Who cites it
5 citing papers in PubMed.
- Human-AI Interaction in Interventional Radiology: A Narrative Review of Current Applications, Challenges, and Future Directions.Journal of imaging · 2026Review
- Article
- Artificial intelligence in transarterial chemoembolization for hepatocellular carcinoma: from treatment response prediction to refractoriness assessment.Frontiers in oncology · 2026Review
- Navigating the Labyrinth of Hepatocellular Carcinoma: Leveraging AI/ML for Precision Oncology.Oncology research · 2026Review
- The translational paradox of AI in hepatocellular carcinoma: from algorithmic over-engineering to real-world clinical utility.Frontiers in oncology · 2026Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundHepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide. Handcrafted radiomics (HCR) and deep learning (DL) models have emerged as promising predictive tools extracting granular insights from images.
objectiveThis systematic review and meta-analysis aims to evaluate the predictive performance of these artificial intelligence models in predicting treatment efficacy in patients with HCC who are undergoing transarterial chemoembolization (TACE).
methodsA comprehensive search was conducted on PubMed, Embase, Web of Science, and Cochrane Library databases up to June 15, 2024. Inclusion criteria encompassed studies involving patients with confirmed HCC undergoing TACE. Random-effects diagnostic test accuracy meta-analyses were performed using bivariate modeling. Methodological quality was assessed using the Prediction model Risk of Bias Assessment Tool.
resultsTwenty-seven studies were included in this review. The overall meta-analysis of models for TACE treatment response included 11 studies. The pooled area under the receiver operating characteristic curve was 0.89 (95% confidence interval: 0.81-0.93) for internal validation and 0.81 (95% confidence interval: 0.80-0.92) for external validation, with no significant differences (P = .66). Moreover, no significant differences were found between DL and HCR models (P = .21) or between models with and without clinical data (P = .78).
conclusionArtificial intelligence models, including DL and HCR, show potential for predicting treatment outcomes in patients with HCC who are undergoing TACE. However, the observed heterogeneity stresses the need for further research.
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