ORIGINAL RESEARCH article
Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1640075
This article is part of the Research TopicAdvances in Surgical Techniques and ML/DL-based Prognostic Biomarkers for Surgical and Adjuvant Therapies of Hepatobiliary and Pancreatic CancersView all 11 articles
Development of a machine learning model to predict overall survival for large hepatocellular carcinoma at BCLC stage A or B after curative hepatectomy
Provisionally accepted- 1Guangxi Medical University Cancer Hospital, Nanning, China
- 2Guangxi Medical University, Nanning, China
- 3Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumors (Guangxi Medical University), Ministry of Education, Nanning, China
- 4Guangxi Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumors, Nanning, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Introduction: Patients with large hepatocellular carcinoma (LHCC) have a poor prognosis even after curative hepatectomy. This study aimed to develop and validate an interpretable machine learning (ML) model to predict their overall survival (OS). Methods: This study included 2,565 patients with hepatocellular carcinoma (HCC) who underwent curative hepatectomy between January 2014 and December 2021. The LHCC patients were randomly assigned (7:3 ratio) to a training (n=1069) or validation (n=457) group. Independent risk factors for OS were identified using multivariable Cox regression. Eight ML models were developed and compared. The optimal model's interpretability was assessed using Shapley Additive Explanations (SHAP). Results: LHCC patients experienced a considerable reduction in OS (Hazard Ratio, HR: 1.810, 95% Confidence Interval, CI: 1.585-2.068) compared to SHCC patients. Among eight ML models, the gradient boosting machine (GBM) model demonstrated superior performance. In the validation group, the GBM model achieved area under the receiver operating characteristic curve (AUC) values of 0.742, 0.744, and 0.750 for 1-, 3-, and 5-year OS, respectively. These results were comparable with or superior to established postoperative predictive models. The GBM model showed the ability to stratify patients with LHCC into distinct prognostic groups. A web-based calculator was developed for risk score generation. Notably, the GBM model showed enhanced predictive accuracy in patients with a high neutrophil-lymphocyte ratio (C-index: 0.819). Conclusions: The GBM-based model demonstrated the potential to predict prognosis for patients with LHCC after curative hepatectomy. This interpretable model may assist in personalized risk assessment and tailoring postoperative management strategies.
Keywords: gradient boosting machine, Hepatectomy, Large hepatocellular carcinoma, overall survival, Shap
Received: 03 Jun 2025; Accepted: 30 Sep 2025.
Copyright: © 2025 Yang, Su, Li, Shuang, Wang, Wei, Huang, Qin, Ran, Huang, Huang, Zhang, Xiang and Gong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Jie Zhang, zhangjie1@gxmu.edu.cn
Bang-De Xiang, xiangbangde@gxmu.edu.cn
Wen-Feng Gong, gwf0771@163.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.