Machine learning-based prediction of 5-year survival in elderly NSCLC patients using oxidative stress markers.

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Oxidative stress plays a significant role in aging and cancer, yet there is currently a lack of research utilizing machine learning models to examine the relationship between oxidative stress and prognosis in elderly non-small cell lung cancer (NSCLC) patients. This study included elderly NSCLC patients who underwent radical lung cancer resection from January 2012 to April 2018, exploring the relationship between Oxidative Stress Score (OSS) and prognosis. Machine learning techniques, including Decision Trees (DT), Random Forest (RF), and Support Vector Machine (SVM), were employed to develop predictive models for 5-year overall survival (OS). The datasets consisted of 1647 patients in the training set, 705 in the internal validation set, and 516 in the external validation set. An OSS was formulated from six systemic oxidative stress biomarkers, such as albumin, total bilirubin, and blood urea nitrogen, among others. Boruta variable importance analysis identified low OSS as a key indicator of poor prognosis. The OSS was subsequently integrated into the DT, RF, and SVM models for training. These models, optimized through hyperparameter tuning on the training set, were then evaluated on the internal and external validation sets. The RF model demonstrated the highest predictive performance, with an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.794 in the internal validation set, compared to AUCs of 0.711 and 0.760 for the DT and SVM models, respectively. Similarly, in the external validation set, the RF model achieved an AUC of 0.784, outperforming the DT and SVM models, which had AUCs of 0.699 and 0.730, respectively. Calibration plots confirmed the RF model's superior calibration, followed by the SVM model, with the DT model performing the poorest. The OSS-based clinical prediction model, constructed using machine learning methodologies, effectively predicts the prognosis of elderly NSCLC patients post-radical surgery.