Objective <p>The purpose of this research was to evaluate the effectiveness of contrast-enhanced computed tomography (CECT)-based habitat radiomics in differentiating low-risk thymomas from high-risk thymomas prior to surgery.</p> Materials and methods <p>A retrospective study was conducted involving patients with thymomas who had undergone CECT at three medical centers. The patients were divided into two cohorts: a training cohort comprising 134 patients from Centers A and B, and a validation cohort consisting of 41 patients from Center C. The k-means clustering algorithm was employed to segment the CECT images into distinct tumor habitats. Radiomic features were extracted from the entire tumor and the specific habitats identified. After feature selection, logistic regression (LR) model was developed to distinguish between low-risk and high-risk thymomas.</p> Results <p>A total of 175 patients were enrolled in the study, with 106 diagnosed with low-risk thymomas and 69 with high-risk thymomas. In the validation cohort, the area under the receiver operating characteristic curve (AUC) values for the models derived from the whole tumor, habitat_1, habitat_2, and habitat_3 were 0.806 (95% CI 0.675–0.938), 0.946 (95% CI 0.861–1.000), 0.620 (95% CI 0.446–0.794), and 0.946 (95% CI 0.885–1.000), respectively. The habitats model demonstrated superior predictive performance compared to the whole tumor model.</p> Conclusion <p>CECT-based habitat radiomics represents a promising diagnostic approach for distinguishing between low-risk and high-risk thymomas in the preoperative setting, highlighting its potential for enhanced diagnostic accuracy.</p>