ArticleFrontiers in medicine2024
Development of a deep learning model for predicting recurrence of hepatocellular carcinoma after liver transplantation.
Article in Frontiers in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A Leakage-Free Survival-Modelling Benchmark for Hepatocellular Carcinoma Recurrence After Liver Transplantation: Nested Cross-Validation Against the Milan Criteria.Bioengineering (Basel, Switzerland) · 2026Article
- Prognosis of liver transplantation and hepatectomy in patients with hepatocellular carcinoma meeting the Milan criteria: A systematic review and meta-analysis.Oncology letters · 2026Article
- Artificial Intelligence in Post-Liver Transplantation: A Scoping Review of Comparative Model Performance.Journal of clinical medicine · 2026Review
- Article
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Authors and funding
7 authors.
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Abstract
Background: Liver transplantation (LT) is one of the main curative treatments for hepatocellular carcinoma (HCC). Milan criteria has long been applied to candidate LT patients with HCC. However, the application of Milan criteria failed to precisely predict patients at risk of recurrence. As a result, we aimed to establish and validate a deep learning model comparing with Milan criteria and better guide post-LT treatment. Methods: A total of 356 HCC patients who received LT with complete follow-up data were evaluated. The entire cohort was randomly divided into training set ( Results: Patients with larger tumor size over 7 cm, poorer differentiation of tumor grade and multiple tumor numbers were first classified as high risk of recurrence. We trained a classification model with TabNet and our proposed model performed better than the Milan criteria in terms of accuracy (0.95 vs. 0.86, Conclusion: A prognostic model had been proposed based on the use of TabNet on various parameters from HCC patients. The model performed well in post-LT recurrence prediction and the identification of high-risk subgroups.
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