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World J Gastrointest Oncol. Sep 15, 2026; 18(9): 121356
Published online Sep 15, 2026. doi: 10.4251/wjgo.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, 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
Revised: May 6, 2026
Accepted: June 18, 2026
Published online: September 15, 2026
Processing time: 156 Days and 11.7 Hours
Core Tip
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.