ArticleJournal of hepatocellular carcinoma2022
A Machine Learning Model Based on Health Records for Predicting Recurrence After Microwave Ablation of Hepatocellular Carcinoma.
Article in Journal of hepatocellular carcinoma, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
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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
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- The application of random forest-based models in prognostication of gastrointestinal tract malignancies: a systematic review.Frontiers in artificial intelligence · 2025Pooled it
- 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
- Article
- Development and validation of a SHAP-explainable preoperative predictive model for microvascular invasion in hepatocellular carcinoma.BMC cancer · 2026Article
- An interpretable machine learning model using routine clinical data for early recurrence prediction in hepatocellular carcinoma.Scientific reports · 2026Article
- Identification and validation of an explainable machine learning model for hepatocellular carcinoma at high risk: a retrospective multicenter cohort study.International journal of surgery (London, England) · 2026Article
- A Machine Learning Guided System for Therapeutic Decision-Making in Intra-Arterial Therapy of Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2026Article
- Using Machine Learning to Predict Advanced-Stage Progression of Intermediate-Stage Hepatocellular Carcinoma after Transarterial Chemoembolization.Radiology. Imaging cancer · 2025Article
- [Establishment of a database for liver cancer ablation and its clinical research value].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2025Article
- Machine learning-based decision support model for selecting intra-arterial therapies for unresectable hepatocellular carcinoma: A national real-world evidence-based study.British journal of cancer · 2024Article
- Prognosis prediction and risk stratification of transarterial chemoembolization or intraarterial chemotherapy for unresectable hepatocellular carcinoma based on machine learning.European radiology · 2024Article
- Analysis of clinical significance and molecular characteristics of methionine metabolism and macrophage-related patterns in hepatocellular carcinoma based on machine learning.Cancer biomarkers : section A of Disease markers · 2024Article
- Threatment Strategies for Recurrent Hepatocellular Carcinoma Patients: Ablation and its Combination Patterns.Journal of Cancer · 2024Review
- Research progress on machine algorithm prediction of liver cancer prognosis after intervention therapy.American journal of cancer research · 2024Review
- Review
- Article
- Current status and prospect of treatments for recurrent hepatocellular carcinoma.World journal of hepatology · 2023Review
- Dictionary learning LASSO for feature selection with application to hepatocellular carcinoma grading using contrast enhanced magnetic resonance imaging.Frontiers in oncology · 2023Article
- The application of explainable artificial intelligence (XAI) in electronic health record research: A scoping review.Digital healthArticle
Corrections and comments
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Authors and funding
12 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Aim: Early recurrence (ER) presents a challenge for the survival prognosis of patients with hepatocellular carcinoma (HCC). The aim of this study was to investigate machine learning (ML) models using clinical data for predicting ER after microwave ablation (MWA). Methods: Between August 2005 and December 2019, 1574 patients with early-stage HCC underwent MWA at four hospitals were reviewed. Then, 36 clinical data points per patient were collected, and the patients were assigned to the training, internal, and external validation set. Apart from traditional logistic regression (LR), three ML models-random forest, support vector machine, and eXtreme Gradient Boosting (XGBoost)-were built and validated for their predictive ability with the area under ROC curve (AUC). Algorithms such as SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME) were used to realize their interpretability. Results: The three ML models all outperformed LR ( Conclusion: The XGBoost model based on clinical data can effectively predict ER risk after MWA, which can contribute to surveillance, prevention, and treatment strategies for HCC.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.