〔Abstract〕 To develop and to validate a combined model integrating 18 F-FDG PET/CT radiomics with clinical features to distinguish between lymphoma and lymphoid inflammatory hyperplasia. Methods A retrospec- tive study was conducted on a cohort of 232 patients diagnosed with lymphoma or lymphoid inflammatory hyperplasi- a. Comparative analyses of clinical and traditional imaging indicators were performed to identify inter-group differ- ences . The clinical features were delineated and extracted using medical software including 3DSlicer and Lifex . Se- lection of the features was performed to construct a PET/CT-based radiomics Logistic model , with a combined mod- el integrating PET/CT with clinical features then used to evaluate the discriminative efficacy of these models . Re- sults Analysis of inter-group differences indicated that age , CTmean , and metabolic tumor volume (MTV) were ef- fective 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 co- hort . Decision curve analysis (DCA) demonstrated that the integrated model provided the greatest clinical net ben- efit. Conclusion The hybrid model integrating 18 F-FDG PET/CT radiomics with clinical features shows robust di- agnostic efficacy to distinguish between lymphoma and lymphoid inflammatory hyperplasia.