ArticleCancers2024
Machine Learning-Based Assessment of Survival and Risk Factors in Non-Alcoholic Fatty Liver Disease-Related Hepatocellular Carcinoma for Optimized Patient Management.
Article in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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The trial behind it
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
10 citing papers in PubMed.
- Exploratory Comparison of Meld, Meld-Na, and Meld 3.0 Scores for Prognostic Assessment at Diagnosis in Hepatocellular Carcinoma Using Machine Learning Approaches.Digestive diseases and sciences · 2026Article
- Development and validation of a modified PAGE-B score based nomogram for predicting hepatocellular carcinoma in antiviral-treated chronic hepatitis B patients with metabolic dysfunction.BMC infectious diseases · 2026Article
- Personalized Mortality Risk Stratification in ALD- and MASLD-Related Hepatocellular Carcinoma Using a Machine Learning Approach.Metabolites · 2025Article
- Machine learning for predicting all-cause mortality of metabolic dysfunction-associated fatty liver disease: a longitudinal study based on NHANES.BMC gastroenterology · 2025Article
- Application of the Random Forest Algorithm for Accurate Bipolar Disorder Classification.Life (Basel, Switzerland) · 2025Article
- Inflammation and Immunity in Liver Neoplasms: Implications for Future Therapeutic Strategies.Molecular cancer therapeutics · 2025Review
- Role of Artificial Intelligence in Identifying Vital Biomarkers with Greater Precision in Emergency Departments During Emerging Pandemics.International journal of molecular sciences · 2025Article
- Assessment of Albumin-Incorporating Scores at Hepatocellular Carcinoma Diagnosis Using Machine Learning Techniques: An Evaluation of Prognostic Relevance.Bioengineering (Basel, Switzerland) · 2024Article
- Innovation through Artificial Intelligence in Triage Systems for Resource Optimization in Future Pandemics.Biomimetics (Basel, Switzerland) · 2024Article
- Prognostic Impact of Metabolic Syndrome and Steatotic Liver Disease in Hepatocellular Carcinoma Using Machine Learning Techniques.Metabolites · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide, with an incidence that is exponentially increasing. Hepatocellular carcinoma (HCC) is the most frequent primary tumor. There is an increasing relationship between these entities due to the potential risk of developing NAFLD-related HCC and the prevalence of NAFLD. There is limited evidence regarding prognostic factors at the diagnosis of HCC. This study compares the prognosis of HCC in patients with NAFLD against other etiologies. It also evaluates the prognostic factors at the diagnosis of these patients. For this purpose, a multicenter retrospective study was conducted involving a total of 191 patients. Out of the total, 29 presented NAFLD-related HCC. The extreme gradient boosting (XGB) method was employed to develop the reference predictive model. Patients with NAFLD-related HCC showed a worse prognosis compared to other potential etiologies of HCC. Among the variables with the worst prognosis, alcohol consumption in NAFLD patients had the greatest weight within the developed predictive model. In comparison with other studied methods, XGB obtained the highest values for the analyzed metrics. In conclusion, patients with NAFLD-related HCC and alcohol consumption, obesity, cirrhosis, and clinically significant portal hypertension (CSPH) exhibited a worse prognosis than other patients. XGB developed a highly efficient predictive model for the assessment of these patients.
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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 Socratic graph.