PURPOSE: To investigate the clinical value of the radiomics model of grayscale ultrasound (GUS) and contrast-enhanced ultrasound (CEUS) to diagnosis subpleural pulmonary tuberculosis and nonpulmonary tuberculosis based on GUS and CEUS images. METHODS: This study included 221 patients with 228 lesions diagnosed using the composite reference standard. The patients were randomly divided into training (n = 183) and test (n = 45) cohorts in an 8:2 ratio. The regions of interest of the GUS and CEUS images were manually segmented to extract the radiomic features. The GUS, CEUS and GUS+CEUS radiomics models were constructed via the multistep selection of highly correlated features. Receiver operating characteristic curves of the different models were plotted, and the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value and negative predictive value (NPV) of the different models were compared. RESULTS: Following Least Absolute Shrinkage and Selection Operator dimension reduction we selected 4, 9, and 11 features to construct the GUS, CEUS, and GUS+CEUS radiomics models, respectively. The AUC values of the three groups in the test cohort were 0.689, 0.748 and 0.779, respectively, and they did not differ significantly. In the test cohort, the GUS+CEUS radiomics model exhibited the highest AUC (0.779), accuracy (75.56%), and NPV (68.7%) of the three models. CONCLUSIONS: The GUS+CEUS radiomics model possesses good clinical value in diagnosing pulmonary tuberculosis.