Nonlinear association between PD-L1 expression levels and the risk of postoperative recurrence in non-small cell lung cancer.

ABSTRACT: Accurate prediction of postoperative recurrence is important for optimizing the treatment strategies for non-small cell lung cancer (NSCLC). Previous studies identified the PD-L1 expression in NSCLC as a risk factor for postoperative recurrence. This study aimed to examine the contribution of PD-L1 expression to predicting postoperative recurrence using machine learning. The clinical data of 647 patients with NSCLC who underwent surgical resection were collected and stratified into training (80%), validation (10%), and testing (10%) datasets. Machine learning models were trained on the training data using clinical parameters including PD-L1 expression. The top-performing model was assessed on the test data using the SHAP analysis and partial dependence plots to quantify the contribution of

SUBMITTER: Kojima K 

PROVIDER: S-EPMC11224325 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature