ArticleRadiology. Imaging cancer2025
Using Machine Learning to Predict Advanced-Stage Progression of Intermediate-Stage Hepatocellular Carcinoma after Transarterial Chemoembolization.
Article in Radiology. Imaging cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Article
- Machine learning outperforms large language models for survival prediction in advanced hepatocellular carcinoma: a multicenter study.Scientific reports · 2026Article
- Propensity Score Matching and Clinical Interpretation.Radiology. Imaging cancer · 2026Article
- Artificial Intelligence in the Diagnosis and Prognostic Stratification of Hepatocellular Carcinoma: Current Evidence, Clinical Applications, and Future Perspectives.Biomedicines · 2026Review
- Precision Risk Stratification in Hepatocellular Carcinoma: Clinical Relevance of CatBoost-based Prediction Following TACE.Radiology. Imaging cancer · 2025Article
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16 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Purpose To develop and test a machine learning (ML)-based model that integrates preoperative variables for prediction of advanced-stage progression (ASP) after transarterial chemoembolization (TACE). Materials and Methods This multicenter retrospective study (ResearchRegistry.com identifier no. researchregistry9425) included patients with intermediate-stage hepatocellular carcinoma (HCC) who underwent TACE at seven hospitals from June 2008 to December 2022. Thirty-four preoperative clinical and CT imaging variables were input into six ML-based models for prediction of ASP, and model performances were compared. Furthermore, the best-performing ML model was compared with the major staging systems, and its utility in performing post-TACE therapies was assessed. The performances of the models were compared by using area under the receiver operating characteristic curve (AUC) with DeLong test. Kaplan-Meier survival curves were compared using the log-rank test. Results A total of 2333 eligible patients (mean age, 54 years ± 12 [SD]; 2051 male patients) were categorized into the training set (
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