ArticleJournal of translational medicine2024
Development of a machine learning-based model to predict prognosis of alpha-fetoprotein-positive hepatocellular carcinoma.
Article in Journal of translational medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Association of Estimated Pulse Wave Velocity with Chronic Kidney Disease Risk: A Machine Learning Analysis Based on NHANES and CHARLS.Healthcare (Basel, Switzerland) · 2026Article
- Integrated Transcriptomic Analyses Identify Four Prognosis-Associated Genes in Hepatocellular Carcinoma.International journal of molecular sciences · 2026Article
- Genetic Ancestry and Colorectal Cancer in the All of Us Dataset.JAMA network open · 2026Article
- Development and internal validation of an XGBoost-based prognostic prediction model and risk stratification system for overall survival in pediatric neuroblastoma: a SEER-based study.Translational pediatrics · 2026Article
- Machine learning outperforms large language models for survival prediction in advanced hepatocellular carcinoma: a multicenter study.Scientific reports · 2026Article
- Review
- Machine learning-based prognostic modeling and surgical value analysis of de novo metastatic invasive ductal carcinoma of the breast.Updates in surgery · 2026Article
- Machine learning integrating MRI and clinical features predicts early recurrence of hepatocellular carcinoma after resection.Scientific reports · 2026Article
- Survival prediction in colorectal cancer liver metastases using machine learning with SHAP-based interpretation.Frontiers in oncology · 2026Article
- An Interpretable MRI-Based Machine Learning Model for Preoperative Identification of the Vascular Dissemination Phenotype in Hepatocellular Carcinoma: A Dual-Center Study.Journal of hepatocellular carcinoma · 2026Article
- SREBP2 as a Novel Biomarker for Posttransplant Hepatocellular Carcinoma Recurrence: A New Prognostic Nomogram.BioMed research international · 2026Article
- Identification of predictive biomarkers and dose optimization for camrelizumab combined with apatinib in the treatment of advanced hepatocellular carcinoma: a quantitative systems pharmacology approach.Frontiers in immunology · 2026Article
- Machine learning-based multicenter prediction of postoperative sepsis in emergency colon cancer: role of surgical approach and inflammatory markers.Frontiers in oncology · 2026Article
- Survival of patients with advanced hepatocellular carcinoma based on Gustave Roussy immune score and portal vein tumor thrombosis: a single-center, retrospective, non-randomized case-controlled study.Discover oncology · 2025Article
- Translational artificial intelligence in gastrointestinal and hepatic disorders: Advancing intelligent clinical decision-making for diagnosis, treatment, and prognosis.World journal of gastroenterology · 2025Review
- Interpretable machine learning models for survival prediction in prostate cancer bone metastases.Scientific reports · 2025Article
- Identification of Crucial Genes Associated With MYCN-Driven Neuroblastoma Based on Single-Cell Analysis and Machine Learning.Cancer medicine · 2025Article
- Machine learning model for predicting recurrence following intensity-modulated radiation therapy in nasopharyngeal carcinoma.World journal of surgical oncology · 2025Article
- Development and validation of an MRI radiomics-based interpretable machine learning model for predicting the progression-free survival in locally advanced nasopharyngeal carcinoma.Quantitative imaging in medicine and surgery · 2025Article
- Artificial intelligence in gastrointestinal cancers: Diagnostic, prognostic, and surgical strategies.Cancer letters · 2025Review
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6 authors.
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Abstract
backgroundPatients with alpha-fetoprotein (AFP)-positive hepatocellular carcinoma (HCC) have aggressive biological behavior and poor prognosis. Therefore, survival time is one of the greatest concerns for patients with AFP-positive HCC. This study aimed to demonstrate the utilization of six machine learning (ML)-based prognostic models to predict overall survival of patients with AFP-positive HCC.
methodsData on patients with AFP-positive HCC were extracted from the Surveillance, Epidemiology, and End Results database. Six ML algorithms (extreme gradient boosting [XGBoost], logistic regression [LR], support vector machine [SVM], random forest [RF], K-nearest neighbor [KNN], and decision tree [ID3]) were used to develop the prognostic models of patients with AFP-positive HCC at one year, three years, and five years. Area under the receiver operating characteristic curve (AUC), confusion matrix, calibration curves, and decision curve analysis (DCA) were used to evaluate the model.
resultsA total of 2,038 patients with AFP-positive HCC were included for analysis. The 1-, 3-, and 5-year overall survival rates were 60.7%, 28.9%, and 14.3%, respectively. Seventeen features regarding demographics and clinicopathology were included in six ML algorithms to generate a prognostic model. The XGBoost model showed the best performance in predicting survival at 1-year (train set: AUC = 0.771; test set: AUC = 0.782), 3-year (train set: AUC = 0.763; test set: AUC = 0.749) and 5-year (train set: AUC = 0.807; test set: AUC = 0.740). Furthermore, for 1-, 3-, and 5-year survival prediction, the accuracy in the training and test sets was 0.709 and 0.726, 0.721 and 0.726, and 0.778 and 0.784 for the XGBoost model, respectively. Calibration curves and DCA exhibited good predictive performance as well.
conclusionsThe XGBoost model exhibited good predictive performance, which may provide physicians with an effective tool for early medical intervention and improve the survival of patients.
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