Decision-Level Ensemble Stacking for Predicting Postoperative Recurrence in NSCLC Patients

Early prediction of post-surgical recurrence in Non-Small Cell Lung Cancer (NSCLC) is crucial for improving patient outcomes. Radiomics, particularly multimodal approaches, shows promise in enhancing predictive accuracy. However, studies using PET, CT, and clinicopathological (CP) data to improve prediction remain limited. Notably, the integration of these modalities through ensemble stacking for decision-level fusion has yet to be fully explored.This study evaluates radiomics extracted from positron emission tomography (PET) and computed tomography (CT) scans-individually, combined, and integrated with CP data- for NSCLC recurrence classification. A cohort of 131 patients underwent PET and CT imaging, with CP variables collected from The Cancer Imaging Archive (TCIA). Radiomics features were extracted by pyradiomics library. Models were developed through feature concatenation followed by decision fusion using ensemble stacking and were assessed using precision, recall, F1 score, accuracy, and AUC.Results show PET+CT fusion achieved the highest performance (AUC = 0.80), while CP integration did not improve the performance and, in some cases, negatively affected the performance results. These findings suggest that optimized radiomics models alone may suffice for predictive modeling in NSCLC.Clinical Relevance-The robust performance of PET+CT models highlights the potential of non-invasive radiomics for personalized recurrence assessment in NSCLC. By integrating these models into clinical workflows, clinicians can tailor followup strategies and treatment plans, ensuring patients receive neither excessive nor insufficient therapy after surgery. This approach optimizes care, enhances surveillance, and improves patient outcomes.