Abstract
BACKGROUND: The potential of the application of artificial intelligence and biochemical markers of oxidative stress to predict the prognosis of older patients with gastric cancer (GC) remains unclear. METHODS: This retrospective multicenter study included consecutive patients with GC aged ≥ 65 years treated between January 2012 and April 2018. The patients were allocated into three cohorts (training, internal, and external validation). The GC-Integrated Oxidative Stress Score (GIOSS) was developed using Cox regression to correlate biochemical markers with patient prognosis. Predictive models for five-year overall survival (OS) were constructed using random forest (RF), decision tree (DT), and support vector machine (SVM) methods, and validated using area under the curve (AUC) and calibration plots. The SHapley Additive exPlanations (SHAP) method was used for model interpretation. RESULTS: This study included a total of 1,859 older patients. The results demonstrated that a low GIOSS was a predictor of poor prognosis. RF was the most efficient method, with AUCs of 0.999, 0.869, and 0.796 in the training, internal validation, and external validation sets, respectively. The DT and SVM models showed low AUC values. Calibration and decision curve analyses demonstrated the considerable clinical usefulness of the RF model. The SHAP results identified pN, pT, perineural invasion, tumor size, and GIOSS as key predictive features. An online web calculator was constructed based on the best model. CONCLUSIONS: Incorporating the GIOSS, the RF model effectively predicts postoperative OS in older patients with GC and is a robust prognostic tool. Our findings emphasize the importance of oxidative stress in cancer prognosis and provide a pathway for improved management of GC. TRIAL REGISTRATION: Retrospectively registered at ClinicalTrials.gov (trial registration number: NCT06208046, date of registration: 2024-05-01).