Luo Q, Zhang C, Luo YP. Development and validation of machine learning models for esophagogastric variceal bleeding risk in hepatocellular carcinoma patients. World J Gastrointest Oncol 2026; 18(9): 121356 [DOI: 10.4251/wjgo.121356]
Corresponding Author of This Article
Yong-Ping Luo, Department of Gastroenterology, Yibin Second People’s Hospital, No. 96 Beida Street, Cuiping District, Yibin 644000, Sichuan Province, China. lyp365e@163.com
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Gastroenterology & Hepatology
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research-article
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Luo Q, Zhang C, Luo YP. Development and validation of machine learning models for esophagogastric variceal bleeding risk in hepatocellular carcinoma patients. World J Gastrointest Oncol 2026; 18(9): 121356 [DOI: 10.4251/wjgo.121356]
World J Gastrointest Oncol. Sep 15, 2026; 18(9): 121356 Published online Sep 15, 2026. doi: 10.4251/wjgo.121356
Development and validation of machine learning models for esophagogastric variceal bleeding risk in hepatocellular carcinoma patients
Qiao Luo, Chao Zhang, Yong-Ping Luo
Qiao Luo, School of Medicine and Life Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, Sichuan Province, China
Qiao Luo, Chao Zhang, Yong-Ping Luo, Department of Gastroenterology, Yibin Second People’s Hospital, Yibin 644000, Sichuan Province, China
Author contributions: Luo Q and Luo YP designed the research study; Luo Q collected and analyzed the data and wrote the manuscript; Zhang C and Luo YP revised the manuscript; all authors have read and approved the final manuscript.
AI contribution statement: AI tools (DeepL and Grammarly) were used solely for linguistic refinement. No AI tool was involved in the generation of research data, interpretation of results, or formulation of conclusions. All AI-generated outputs were critically reviewed and revised by the authors.
Institutional review board statement: This study was approved by the Ethics Committee of Yibin Second People’s Hospital (No. 2024-117-01).
Informed consent statement: Informed consent was waived by the local Ethics Committee because of the retrospective nature of the study.
Conflict-of-interest statement: All authors report no relevant conflicts of interest for this article.
Data sharing statement: No additional data are available.
Corresponding author: Yong-Ping Luo, Department of Gastroenterology, Yibin Second People’s Hospital, No. 96 Beida Street, Cuiping District, Yibin 644000, Sichuan Province, China. lyp365e@163.com
Received: March 23, 2026 Revised: May 6, 2026 Accepted: June 18, 2026 Published online: September 15, 2026 Processing time: 156 Days and 11.7 Hours
Abstract
BACKGROUND
Esophagogastric variceal bleeding (EGVB) is a common and highly fatal complication in patients with hepatocellular carcinoma (HCC). In this study, our aim was to develop a predictive model to assess the risk of EGVB in patients with HCC.
AIM
To construct and internally validate a machine learning model to predict the risk of EGVB in patients with HCC.
METHODS
This study included 188 patients with HCC, who were randomly assigned to the training set and validation set in a 7:3 ratio. Using LASSO regression and multivariate Logistic regression, the variables significantly associated with EGVB were identified, and six machine learning models were constructed using these variables. The predictive performance of different models was compared using metrics such as the area under the curve (AUC). The optimal model was further evaluated, and the SHapley Additive exPlanation (SHAP) interpretability analysis was performed.
RESULTS
In the end, four characteristic variables, namely albumin, splenic vein diameter, tumor burden score, and ascites, were selected. These four variables were used to build six machine learning models. Among these, the support vector machine (SVM) achieved the highest AUC (0.931), F1 score (0.889), Youden index (0.785), and sensitivity (0.889) across all six models. Based on the overall performance, SVM was identified as the optimal model. Internal validation demonstrated that the SVM model had good calibration performance and clinical applicability. SHAP interpretability analysis further visualized the contribution pathways of each variable through a feature importance bar chart, a swarm plot, dependence plots, and a waterfall plot, providing interpretable evidence for clinical decision-making.
CONCLUSION
The successful development of a predictive model for EGVB in HCC patients, along with SHAP analysis, can help clinicians identify high-risk individuals at an early stage and implement personalized interventions.
Core Tip: This study developed and validated six machine learning models for the early identification of the risk of early-stage esophagogastric variceal bleeding in patients with hepatocellular carcinoma. Albumin, splenic vein diameter, tumor burden score, and ascites were identified as feature variables through LASSO and multivariate Logistic regression. Based on these variables, six machine learning models were constructed, and the support vector machine model was selected as the optimal model. This model demonstrated satisfactory predictive performance, calibration, and clinical applicability.
