Dynamic changes in postoperative risk of recurrence of non-small cell lung cancer according to variations in PD-L1 expression levels

Abstract The accurate prediction of postoperative recurrence is important for optimizing treatment strategies for non-small cell lung cancer (NSCLC). Previous studies have identified the PD-L1 expression in NSCLC as a risk factor for postoperative recurrence. This study aimed to examine the contribution of the PD-L1 expression in predicting postoperative recurrence using machine learning. The clinical data of 647 NSCLC patients 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 the PD-L1 expression. The top-performing model was assessed on the test data using a SHAP analysis and partial dependence plots to quantify the contribution of the PD-L1 expression. A multivariate Cox proportional hazards model was used to validate the association between the PD-L1 expression and postoperative recurrence. The random forest model demonstrated the highest predictive performance with the SHAP analysis highlighting the PD-L1 expression as an important feature, and the multivariate Cox analysis indicating a significant increase in the risk of postoperative recurrence with each increment in the PD-L1 expression. These findings suggest that variations in the PD-L1 expression may provide valuable information for clinical decision-making in lung cancer treatment strategies.