Objective : To develop and to validate a combined model integrating18F-FDG PET/CT radiomics with clinical features to distinguish between lymphoma and lymphoid inflammatory hyperplasia. Methods : A retrospective study was conducted on a cohort of 232 patients diagnosed with lymphoma or lymphoid inflammatory hyperplasia. Comparative analyses of clinical and traditional imaging indicators were performed to identify inter-group differences. The clinical features were delineated and extracted using medical software including 3D-Slicer and Lifex. Selection of the features was performed to construct a PET/CT-based radiomics Logistic model, with a combined model integrating PET/CT with clinical features then used to evaluate the discriminative efficacy of these models. Results: Analysis of inter-group differences indicated that age, CTmean, and metabolic tumor volume(MTV)were effective for differentiating between lymphoma and lymphoid inflammatory hyperplasia(P<0.05). The PET/CT-based radiomics Logistic model differentiated between lymphoma and lymphoid inflammatory hyperplasia, with an area under curve(AUC) of 0.924(95%CI: 0.884-0.960) and 0.863(95%CI: 0.774-0.939) in the training and testing cohorts, respectively. The integrated Logistic model that combined PET/CT-based radiomics with clinical features to distinguish between lymphoma and lymphoid inflammatory hyperplasia achieved an AUC of 0.933(95%CI: 0.889-0.969) in the training cohort and 0.884(95%CI: 0.792-0.964) in the testing cohort. Decision curve analysis(DCA) demonstrated that the integrated model provided the greatest clinical net benefit. Conclusion The hybrid model integrating18F-FDG PET/CT radiomics with clinical features shows robust diagnostic efficacy to distinguish between lymphoma and lymphoid inflammatory hyperplasia.