ArticleScientific reports2025
Predicting hepatocellular carcinoma survival with artificial intelligence.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Development and Validation of a Machine Learning Model to Prognosticate Hepatocellular Carcinoma.Alimentary pharmacology & therapeutics · 2026Article
- Can machine learning predict vessels that encapsulates tumor cluster ptterns and histological differentiation in solitary small (< 5 cm) hepatocellular carcinoma?Surgery today · 2026Article
- A Machine Learning Framework for Prognostic Modeling in Stage III Colon Cancer.Journal of clinical medicine · 2026Article
- Machine learning-based prediction of recurrence after curative resection in non-small cell lung cancer.Scientific reports · 2026Article
- Pioneering efficient deep learning architectures for enhanced hepatocellular carcinoma prediction and clinical translation.World journal of gastrointestinal oncology · 2026Article
- Liver transplantation for hepatocellular carcinoma: from patient selection and downstaging to risk stratification and post-transplant surveillance.Gastroenterology report · 2026Review
- Machine learning integrating MRI and clinical features predicts early recurrence of hepatocellular carcinoma after resection.Scientific reports · 2026Article
- Evaluating the clinical utility of large language models for hepatocellular carcinoma treatment recommendations: A nationwide retrospective registry study.PLoS medicine · 2026Article
- Understanding Liver and Digestive Diseases: A Paved Road to Improve Diagnosis, Management, and Treatment.Exploration of digestive diseases · 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
- Can Vascular Vertigo Be Recognized by Artificial Intelligence Methods?Noro psikiyatri arsivi · 2026Article
- From bioinformatics to clinical translation: BIRC5 as a pivotal diagnostic biomarker and therapeutic target for NAFLD-driven HCC.Cell biology and toxicology · 2025Article
- Structural and temporal dynamics analysis of PD-1/PD-L1 immunotherapy in hepatocellular carcinoma: History, research hotspots, and emerging trends.Human vaccines & immunotherapeutics · 2025Article
- A Novel Method for Predicting Oncogenic Types of Human Papillomavirus.Diagnostics (Basel, Switzerland) · 2025Article
- Mechanisms of ferroptosis in primary hepatocellular carcinoma and progress of artificial intelligence-based predictive modeling in hepatocellular carcinoma.World journal of gastroenterology · 2025Review
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- Hepatocellular Carcinoma Recurrence After Liver Transplantation: Current Insights and Future Directions.Journal of clinical medicine · 2025Review
- Artificial intelligence for multi-time-point arterial phase contrast-enhanced MRI profiling to predict prognosis after transarterial chemoembolization in hepatocellular carcinoma.La Radiologia medica · 2025Article
- Integrating Clinical and Transcriptomic Profiles Associated with Vitamin D to Enhance Disease-Free Survival in Cervical Cancer Recurrence Using the CatBoost Algorithm.Diagnostics (Basel, Switzerland) · 2025Article
- Machine Learning-Based Survival Analysis for Patients Receiving Lenvatinib for Unresectable Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2025Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite the extensive research on hepatocellular carcinoma (HCC) exploring various treatment strategies, the survival outcomes have remained unsatisfactory. The aim of this research was to evaluate the ability of machine learning (ML) methods in predicting the survival probability of HCC patients. The study retrospectively analyzed cases of patients with stage 1-4 HCC. Demographic, clinical, pathological, and laboratory data served as input variables. The researchers employed various feature selection techniques to identify the key predictors of patient mortality. Additionally, the study utilized a range of machine learning methods to model patient survival rates. The study included 393 individuals with HCC. For early-stage patients (stages 1-2), the models reached recall values of up to 91% for 6-month survival prediction. For advanced-stage patients (stage 4), the models achieved accuracy values of up to 92% for 3-year overall survival prediction. To predict whether patients are ex or not, the accuracy was 87.5% when using all 28 features without feature selection with the best performance coming from the implementation of weighted KNN. Further improvements in accuracy, reaching 87.8%, were achieved by applying feature selection methods and using a medium Gaussian SVM. This study demonstrates that machine learning techniques can reliably predict survival probabilities for HCC patients across all disease stages. The research also shows that AI models can accurately identify a high proportion of surviving individuals when assessing various clinical and pathological factors.
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