ArticleClinical and molecular hepatology2024
Conventional and machine learning-based risk scores for patients with early-stage hepatocellular carcinoma.
Article in Clinical and molecular hepatology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed.
- Development and Validation of an Interpretable Machine Learning Model to Predict Mortality in Patients With Sepsis-Induced Coagulopathy: Multicenter Cohort Study.JMIR medical informatics · 2026Article
- Interactive Roles of Metabolic Dysfunction-Associated Steatotic Liver Disease and Nutritional Status in the Prognosis of Patients With Hepatocellular Carcinoma After Surgical Resection.The Kaohsiung journal of medical sciences · 2026Article
- Prognostic value of combined FIB-4 and AFP in predicting survival after hepatectomy for hepatocellular carcinoma: a multicenter cohort study.BMC cancer · 2026Article
- Non-invasive model for predicting future lung metastasis of hepatocellular carcinoma based on imaging heterogeneity analysis.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Development and validation of a personalized web-based calculator of aggressive recurrence after surgery for early-stage hepatocellular carcinoma by machine learning.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Comparison of patients with HCC with and without MASLD after surgical resection.JHEP reports : innovation in hepatology · 2026Article
- Prognostic utility of the serum uric acid-to-high-density lipoprotein cholesterol ratio following hepatectomy for hepatocellular carcinoma.The Journal of international medical research · 2026Article
- Artificial Intelligence Applications in the Diagnosis, Treatment, and Prognosis of Hepatocellular Carcinoma.Gut and liver · 2026Review
- Serum Inflammatory-Nutritional Markers Refine Machine-Learning Prognostic Stratification for Resectable Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2026Article
- CATS-BIT-ME Score: A Machine Learning and Conventional Risk Model for Prognosis in Hepatocellular Carcinoma.Gastro hep advances · 2026Article
- Personalized Mortality Risk Stratification in ALD- and MASLD-Related Hepatocellular Carcinoma Using a Machine Learning Approach.Metabolites · 2025Article
- Protein induced by vitamin K absence or antagonist II as a prognostic marker in hepatocellular carcinoma patients with normal serum alpha-fetoprotein levels.Journal of the Chinese Medical Association : JCMA · 2025Article
- Comprehensive analysis reveals the tumor suppressor role of macrophage signature gene FCER1G in hepatocellular carcinoma.Scientific reports · 2025Article
- Correspondence to letter to the editor 1 on "Conventional and machine learning-based risk scores for patients with early-stage hepatocellular carcinoma".Clinical and molecular hepatology · 2025Article
- Radiomics-based biomarker for PD-1 status and prognosis analysis in patients with HCC.Frontiers in immunology · 2025Article
- Correspondence to letter to the editor 2 on "Conventional and machine learning-based risk scores for patients with early-stage hepatocellular carcinoma".Clinical and molecular hepatology · 2025Article
- Development of risk scores for prognosis prediction among patients with early-stage hepatocellular carcinoma.Clinical and molecular hepatology · 2025Article
- Predicting Resistance and Survival of HCC Patients Post-HAIC: Based on Shapley Additive exPlanations and Machine Learning.Journal of hepatocellular carcinoma · 2025Article
- Insights on risk score development: Considerations for early-stage hepatocellular carcinoma models.Clinical and molecular hepatology · 2025Article
- Correspondence to editorial on "Conventional and machine learning-based risk scores for patients with early-stage hepatocellular carcinoma".Clinical and molecular hepatology · 2024Article
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Abstract
BACKGROUND/
aimsThe performance of machine learning (ML) in predicting the outcomes of patients with hepatocellular carcinoma (HCC) remains uncertain. We aimed to develop risk scores using conventional methods and ML to categorize early-stage HCC patients into distinct prognostic groups.
methodsThe study retrospectively enrolled 1,411 consecutive treatment-naïve patients with the Barcelona Clinic Liver Cancer (BCLC) stage 0 to A HCC from 2012 to 2021. The patients were randomly divided into a training cohort (n=988) and validation cohort (n=423). Two risk scores (CATS-IF and CATS-INF) were developed to predict overall survival (OS) in the training cohort using the conventional methods (Cox proportional hazards model) and ML-based methods (LASSO Cox regression), respectively. They were then validated and compared in the validation cohort.
resultsIn the training cohort, factors for the CATS-IF score were selected by the conventional method, including age, curative treatment, single large HCC, serum creatinine and alpha-fetoprotein levels, fibrosis-4 score, lymphocyte-tomonocyte ratio, and albumin-bilirubin grade. The CATS-INF score, determined by ML-based methods, included the above factors and two additional ones (aspartate aminotransferase and prognostic nutritional index). In the validation cohort, both CATS-IF score and CATS-INF score outperformed other modern prognostic scores in predicting OS, with the CATSINF score having the lowest Akaike information criterion value. A calibration plot exhibited good correlation between predicted and observed outcomes for both scores.
conclusionBoth the conventional Cox-based CATS-IF score and ML-based CATS-INF score effectively stratified patients with early-stage HCC into distinct prognostic groups, with the CATS-INF score showing slightly superior performance.
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