Objective: To develop a radiomics-based model based on the immune response evaluation criteria in solid tumors (iRECIST) for predicting the response of patients with non-small cell lung cancer (NSCLC) to combined immunotherapy, so as to precisely screen patients who may derive clinical benefit at the pre-treatment or early treatment stage. Methods: A total of 75 patients with pathologically confirmed NSCLC and received combined immunotherapy were enrolled. All patients underwent chest computed tomography (CT) within one month before treatment. Based on the iRECIST, treatment efficacy after 4-6 cycles (12-18 weeks) of immunotherapy combination therapy was assessed, and patients were classified into the treatment-sensitive and non-sensitive groups. The patients were randomly divided into a training cohort (n=50) and a testing cohort (n=25) at a ratio of 2:1. Clinical feature models and radiomics prediction models were developed separately. The predictive performance and clinical utility of the models were compared using receiver operating characteristic (ROC) curves, the area under the curve (AUC), accuracy (ACC), and decision curve analysis (DCA). Results: After feature extraction and selection, a total of five radiomics features were obtained, including two first-order features and three texture features. After feature fusion and screening, and in order to reduce over-fitting, through three-fold cross-validation, the Naive Bayes was the most stable, with an AUC of 0.675 and accuracy of 0.720 in the training set, and an AUC of 0.647 and accuracy of 0.720 in the test set, respectively. Conclusion: Radiomics has certain potential and clinical application value for predicting the efficacy of immunotherapy combination therapy in NSCLC patients.