Preoperative Maximum Standardized Uptake Value Emphasized in Explainable Machine Learning Model for Predicting the Risk of Recurrence in Resected Non-Small Cell Lung Cancer.

Abstract

To comprehensively analyze the association between preoperative maximum standardized uptake value (SUV_max) on 18F-fluorodeoxyglucose positron emission tomography-computed tomography and postoperative recurrence in resected non-small cell lung cancer (NSCLC) using machine learning (ML) and statistical approaches. This retrospective study included 643 patients who had undergone NSCLC resection. ML models (random forest, gradient boosting, extreme gradient boosting, and AdaBoost) and a random survival forest model were developed to predict postoperative recurrence. Model performance was evaluated using the receiver operating characteristic (ROC) AUC and concordance index (C-index). Shapley additive explanations (SHAP) and partial dependence plots (PDPs) were used to interpret model predictions and quantify feature importance. The relationship between SUV_max and recurrence risk was evaluated by using a multivariable Cox proportional hazards model. The random forest model showed the highest predictive performance (ROC AUC, 0.90; 95% CI, 0.86 to 0.97). The SHAP analysis identified SUV_max as an important predictor. The PDP analysis showed a nonlinear relationship between SUV_max and recurrence risk, with a sharp increase at SUV_max 2-5. The random survival forest model achieved a C-index of 0.82. A permutation importance analysis identified SUV_max as the most important feature. In the Cox model, increased SUV_max was associated with a higher risk of recurrence (adjusted hazard ratio, 1.03 [95% CI, 1.00 to 1.06]). Preoperative SUV_max showed significant predictive value for postoperative recurrence after NSCLC resection. The nonlinear relationship between SUV_max and recurrence risk, with a sharp increase at relatively low SUV_max values, suggests its potential as a sensitive biomarker for early identification of high-risk patients. This may contribute to more precise assessments of the risk of recurrence and personalized treatment strategies for NSCLC.