Evaluating clinical and radiomic features for predicting lung cancer recurrence pre- and post-tumor resection

Among patients with early-stage non-small cell lung cancer (NSCLC) undergoing surgical resection, identifying who is at high risk of recurrence can inform clinical guidelines with respect to more aggressive follow-up and/or adjuvant therapy. While predicting recurrence based on pre-surgical resection data is ideal, clinically important pathological features are only evaluated postoperatively. Therefore, we developed two supervised classification models to assess the importance of pre- and post-surgical features for predicting 5-year recurrence. An integrated dataset was generated by combining clinical covariates and radiomic features calculated from pre-surgical computed tomography images. After removing correlated radiomic features, the SHapley Additive exPlanations (SHAP) method and recursive feature elimination (RFE) were used for feature selection. A support vector machine (SVM) classification model was then trained to predict recurrence. We demonstrate that the post-surgical model outperforms the pre-surgical model in predicting lung cancer recurrence, with tumor pathological features and peritumoral radiomic features contributing significantly to the model's performance.