ORIGINAL RESEARCH article
Front. Oncol.
Sec. Radiation Oncology
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1601985
This article is part of the Research TopicAI-Based Prognosis Prediction and Dose Optimization Strategy in Radiotherapy for Malignant TumorsView all 5 articles
Predicting Lymph Node Metastasis in Patients with Hepatocellular Carcinoma Using Machine Learning
Provisionally accepted
- 1Jinan Central Hospital, Jinan, China
- 2Southwest Medical University, Luzhou, Sichuan, China
- 3Department of Anesthesiology, Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, Sichuan, China
- 4Sichuan Cancer Hospital, Chengdu, Sichuan Province, China
- 5Dezhou City People’s Hospital, Dezhou, Shandong Province, China
- 6Clinical Medical Research Center, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China
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This study aims to develop a population-adapted machine learning-based prediction model for hepatocellular carcinoma (HCC) lymph node metastasis (LNM) to identify high-risk patients requiring intensive surveillance.Data from 23511 HCC patients in the SEER database and 57 patients from our hospital were analyzed. Seven LNM risk indicators were selected. Four machine learning algorithms-decision tree (DT), logistic Regression (LR), multilayer perceptron (MLP), and extreme gradient boosting (XGBoost)-were employed to construct prediction models. Model performance was evaluated using area under the curve, accuracy, sensitivity, and specificity.Among 23511 SEER patients, 1679 (7.14%) exhibited LNM. Race, Sequence number, Tumor size, T stage and AFP were identified as independent predictors of LNM. The LR model achieved optimal performance (area under the curve: 0.751; accuracy: 0.707; sensitivity: 0.711; specificity: 0.661). External validation with 57 patients from our hospital confirmed robust generalizability (area under the curve: 0.73; accuracy: 0.737; sensitivity: 0.829; specificity: 0.5), outperforming other models.The LR-based model demonstrates superior predictive capability for LNM in HCC, offering clinicians a valuable tool to guide personalized therapeutic strategies.
Keywords: Hepatocellular Carcinoma, machine learning, predictive model, lymph node metastasis, logisic regression
Received: 28 Mar 2025; Accepted: 23 Jun 2025.
Copyright: © 2025 Yunhui, Yuqin, Hongyan, Hongyuan, Tingting, Jie and Kun. 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:
Han Yunhui, Jinan Central Hospital, Jinan, China
Fang Jie, Dezhou City People’s Hospital, Dezhou, 253014, Shandong Province, China
He Kun, Clinical Medical Research Center, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China
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