Comparative study on predicting postoperative distant metastasis of lung cancer based on machine learning models.

ABSTRACT: Lung cancer remains the leading cause of cancer-related incidence and mortality worldwide. Its tendency for postoperative distant metastasis significantly compromises long-term prognosis and survival. Accurately predicting the metastatic potential in a timely manner is crucial for formulating optimal treatment strategies. This study aimed to comprehensively compare the predictive performance of nine machine learning (ML) models and to enhance interpretability through SHAP (Shapley Additive Explanations), with the goal of developing a practical and transparent risk stratification tool for postoperative lung cancer management. Clinical data from 3,120 patients with stage I-III lung cancer who underwent radical surgery were retrospectively collected and randomly divided into training and test

SUBMITTER: Guo X 

PROVIDER: S-EPMC12910018 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature