Purpose To develop a predictive model by(18)F-FDG PET/CT radiomic features and to validate the predictive value of the model for distinguishing solitary lung adenocarcinoma from tuberculosis. Methods A total of 235(18)F-FDG PET/CT patients with pathologically or follow-up confirmed lung adenocarcinoma (n = 131) or tuberculosis (n = 104) were retrospectively and randomly divided into a training (n = 163) and validation (n = 72) cohort. Based on the Transparent Reporting of Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD), this work was belonged to TRIPOD type 2a study. The Mann-WhitneyUtest and least absolute shrinkage and selection operator (LASSO) algorithm were used to select the optimal predictors from 92 radiomic features that were extracted from PET/CT, and the optimal predictors were used to build the radiomic model in the training cohort. The meaningful clinical variables comprised the clinical model, and the combination of the radiomic model and clinical model was a complex model. The performances of the models were assessed by the area under the receiver operating characteristic curve (AUC) in the training and validation cohorts. Results In the training cohort, 9 radiomic features were selected as optimal predictors to build the radiomic model. The AUC of the radiomic model was significantly higher than that of the clinical model in the training cohort (0.861 versus 0.686,p < 0.01), and this was similar in the validation cohort (0.889 versus 0.644,p < 0.01). The AUC of the radiomic model was slightly lower than that of the complex model in the training cohort (0.861 versus 0.884,p > 0.05) and validation cohort (0.889 versus 0.909,p > 0.05), but there was no significant difference. Conclusion F-18-FDG PET/CT radiomic features have a significant value in differentiating solitary lung adenocarcinoma from tuberculosis.