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

Purpose: 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.

Patients and methods: 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.

Results: 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]).

Conclusion: 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.