JCO Clin Cancer Inform. 2025 Mar;9:e2400194. doi: 10.1200/CCI-24-00194. Epub 2025 Mar 5.
ABSTRACT
PURPOSE: To comprehensively analyze the association between preoperative maximum standardized uptake value (SUVmax) 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 SUVmax 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 SUVmax as an important predictor. The PDP analysis showed a nonlinear relationship between SUVmax and recurrence risk, with a sharp increase at SUVmax 2-5. The random survival forest model achieved a C-index of 0.82. A permutation importance analysis identified SUVmax as the most important feature. In the Cox model, increased SUVmax was associated with a higher risk of recurrence (adjusted hazard ratio, 1.03 [95% CI, 1.00 to 1.06]).
CONCLUSION: Preoperative SUVmax showed significant predictive value for postoperative recurrence after NSCLC resection. The nonlinear relationship between SUVmax and recurrence risk, with a sharp increase at relatively low SUVmax 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.
PMID:40043221 | DOI:10.1200/CCI-24-00194
