Purpose <p>To preliminarily evaluate the predictive value of baseline contrast-enhanced CT (CECT) radiomics for assessing chemotherapy response in pediatric lymphoma.</p> Methods <p>This retrospective study included 92 pediatric patients with lymphoma (72 males, 20 females). Patients were classified as responders (<i>n</i> = 74) and non-responders (<i>n</i> = 18) based on treatment outcomes. The cohort was randomly stratified into a training set (<i>n</i> = 65, 70%) and a test set (<i>n</i> = 27, 30%). A total of 960 radiomics features were extracted from venous-phase baseline CECT images of target lesions. Feature selection was performed, and a logistic regression model was developed for response classification using the Synthetic Minority Over-sampling Technique (SMOTE). To evaluate model robustness, the entire radiomics pipeline was repeated across 10 independent randomized train-test splits. Model performance was evaluated using receiver operating characteristic (ROC) curve analysis, reporting the area under the ROC curve (AUC), 95% confidence intervals (CIs), and accuracy.</p> Results <p>Eight radiomics features were selected for the final model, including four filter-transformed first-order features and four filter-transformed texture features. The SMOTE model achieved an AUC of 0.883 (95% CI: 0.799–0.967) and an accuracy of 0.800 in the training set. In the test set, the SMOTE model achieved an AUC of 0.809 (95% CI: 0.606–1.000) and an accuracy of 0.741. In repeated validation, the SMOTE model showed mean AUCs of 0.915 (training) and 0.767 (test) across 10 splits.</p> Conclusion <p>This hypothesis-generating study demonstrates that baseline CECT radiomics shows promise for predicting chemotherapy response in pediatric lymphoma.</p>