Lymph node metastasis in patients with hepatocellular carcinoma using machine learning: a population-based study.

Aim: This study aims to develo\p a population-adapted machine learning-based prediction model for hepatocellular carcinoma (HCC) lymph node metastasis (LNM) to identify high-risk patients requiring intensive surveillance. Methods: 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. Results: 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. Conclusions: The LR-based model demonstrates superior predictive capability for LNM in HCC, offering clinicians a valuable tool to guide personalized therapeutic strategies.