ArticleJournal of hepatocellular carcinoma2025
Machine Learning Radiomics for Predicting Response to MR-Guided Radiotherapy in Unresectable Hepatocellular Carcinoma: A Multicenter Cohort Study.
Article in Journal of hepatocellular carcinoma, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Development and validation of prediction model for intrapulmonary metastasis in lung adenocarcinoma based on machine learning.Journal of thoracic disease · 2026Article
- Recent advancements in radiotherapy for hepatocellular carcinoma.ESMO gastrointestinal oncology · 2026Review
- From Variant to Biomarker in NSCLC Immunotherapy Resistance: Multiomics Evidence Chains and Accountable AI Integration.Human mutation · 2026Review
- Clinically deployable AI to predict objective response to radiotherapy-intensified immunotherapy in advanced hepatocellular carcinoma.Frontiers in oncology · 2026Article
- Data-driven strategies for immunoradiotherapy in uveal melanoma: the role of artificial intelligence.Frontiers in pharmacology · 2026Review
- Machine Learning-Based Sialylation-Associated Gene Signature Predicts Prognosis and Immune Landscape in Hepatocellular Carcinoma: Validation via Multi-Omics Analysis and in vitro Assays.Journal of hepatocellular carcinoma · 2026Article
- Artificial intelligence-based prognostic modeling of immunoradiotherapy in Barcelona clinic liver cancer stage C hepatocellular carcinoma: a multicenter retrospective study.Frontiers in oncology · 2026Article
- Interpretable AI for treatment decision-making in immunoradiotherapy of locally advanced nasopharyngeal carcinoma.Frontiers in oncology · 2026Article
- Machine-learning CT radiomics for prognostication in unresectable pancreatic cancer.Frontiers in pharmacology · 2026Article
- Artificial intelligence-based personalized treatment strategies for unresectable hepatocellular carcinoma: integrating HSP90α for prognosis and survival prediction.NPJ digital medicine · 2025Article
- Machine learning-based prognostic modeling for locally advanced non-small cell lung cancer treated with immuno-radiotherapy.Frontiers in pharmacology · 2025Article
- Exploring HSP90α and hs-CRP using AI models to predict prognosis in advanced hepatocellular carcinoma treated with PD-1 inhibitors and targeted therapy.Frontiers in pharmacology · 2025Article
- AI-driven immunotherapy: synergizing with radiotherapy to reconfigure the tumor microenvironment and treatment landscape.Frontiers in pharmacology · 2025Review
- Prediction of prognosis in T4 or N3 locally advanced nasopharyngeal carcinoma receiving chemoradiotherapy using machine learning methods.Frontiers in oncology · 2025Article
- Exploring the prognostic value of EBV DNA in advanced nasopharyngeal carcinoma treated with chemoradiotherapy using AI-based modeling.Frontiers in oncology · 2025Article
- Artificial intelligence-based evaluation of prognostic benefits from immunotherapy plus targeted therapy with or without radiotherapy or TACE in advanced hepatocellular carcinoma.Frontiers in oncology · 2025Article
- Performance of AI-based machine learning models for overall survival prediction in advanced hepatocellular carcinoma patients receiving immunoradiotherapy.Frontiers in pharmacology · 2025Article
- A nomogram-based radiomics for predicting survival to concurrent chemoradiotherapy in inoperable pancreatic cancer: a dual-center cohort study.Frontiers in immunology · 2025Article
- Multi-Omics-Enabled Precision Strategies for Overcoming CAR-T Therapy Limitations in Gastrointestinal Malignancies.BioFactors (Oxford, England)Review
Corrections and comments
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Authors and funding
11 authors.
Funding
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Abstract
Background: This study was conducted to assess the efficacy and safety of magnetic resonance (MR)-guided hypofractionated radiotherapy in patients with unresectable hepatocellular carcinoma (HCC). Machine learning-based radiomics was utilized to predict responses in these patients. Methods: This retrospective study included 118 hCC patients who received MR-guided hypofractionated radiotherapy. The primary study endpoint was the objective response rate (ORR). Radiomics features were based on the gross tumor volume (GTV). K-means clustering was performed to differentiate cancer subtypes based on radiomics. Nine radiomics-utilizing machine learning models were built and validated internally through 5-fold cross-validation. Results: The ORR, median progression-free survival (mPFS), and median overall survival (mOS) were 54.4%, 21.7 months, and 40.7 months, respectively. No patient experienced Grade 3/4 adverse events. 1130 radiomics features were extracted from the GTV, of which 7 were included for further analysis. K-means clustering identified 2 subtypes based on the selected features. Subtype 1 had significantly higher response, longer mPFS, and longer mOS than Subtype 2. In both internal and external validations, the multi-layer perceptron (MLP) model demonstrated superior predictive performance for response, achieving a receiver operating characteristic-area under the curve (ROC-AUC) of 0.804 and 0.842, respectively. Conclusion: MR-guided radiotherapy was proven to be effective and safe for HCC. The machine learning radiomics model developed in this study could accurately predict the response of radiotherapy-treated inoperable HCC.
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