Development and validation of a machine learning model for predicting pulmonary metastasis in hepatocellular carcinoma patients.

Background: Hepatocellular carcinoma with pulmonary metastasis (HCC-PM) is a common complication of hepatocellular carcinoma (HCC) and has gained increasing attention. However, there is currently no effective model for predicting the risk of HCC-PM in patients with HCC. This study aims to develop a precise predictive model to assess the risk of HCC-PM in patients with HCC.

Methods: We retrospectively analyzed HCC cases from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2018. To address data imbalance, we applied the synthetic minority oversampling technique (SMOTE). Feature selection was conducted using the Boruta algorithm and multivariate logistic regression. Eight machine learning models were then developed and evaluated using validation cohorts for predictive performance. Feature importance was further analyzed using Shapley Additive Explanations (SHAP).

Results: This study included 20,346 patients diagnosed with HCC. Age, race, T stage, N stage, bone metastasis, brain metastasis, radiation, chemotherapy, and surgery were identified as independent risk factors for patients with HCC-PM. Among the machine learning models, RF achieved the best performance, with an AUC of 0.894 in the training cohort and 0.830 in the validation cohort. Based on the RF algorithm, we developed a user-friendly, web-based tool to estimate the risk of pulmonary metastasis in patients with HCC.

Conclusion: The ML model developed in this study accurately predicts the risk of pulmonary metastasis in patients with HCC by analyzing individual clinical parameters, TNM stage, and treatment information. By enabling precise risk stratification, this model may support clinical risk assessment and facilitate more personalized management of HCC.