Multiparameter diagnostic model based on F-FDG PET and clinical characteristics can differentiate thymic epithelial tumors from thymic lymphomas.

OBJECTIVE

To evaluate the diagnostic performance of combined multiparametric F-fluorodeoxyglucose positron emission tomography (FDG PET) with clinical characteristics in differentiating thymic epithelial tumors (TETs) from thymic lymphomas.

PATIENTS AND METHODS

A total of 173 patients with 80 TETs and 93 thymic lymphomas who underwent F-FDG PET/CT before treatment were enrolled in this retrospective study. All patients were confirmed by pathology, and baseline characteristics and clinical data were also collected. The semi-parameters of F-FDG PET/CT, including lesion size, SUVmax (maximum standard uptake value), SUVmean (mean standard uptake value), TLG (total lesion glycolysis), MTV (metabolic tumor volume) and SUVR (tumor-to-normal liver standard uptake value ratio) were evaluated. The differential diagnostic efficacy was evaluated using the receiver operating characteristic (ROC) curve. Integrated discriminatory improvement (IDI) and net reclassification improvement (NRI), and Delong test were used to evaluate the improvement in diagnostic efficacy. The clinical efficacy was evaluated by decision curve analysis (DCA).

RESULTS

Age, clinical symptoms, and metabolic parameters differed significantly between patients with TETs and thymic lymphomas. The ROC curve analysis of SUVR showed the highest differentiating diagnostic value (sensitivity = 0.763; specificity = 0.888; area under the curve [AUC] = 0.881). The combined diagnostics model of age, clinical symptoms and SUVR resulted in the highest AUC of 0.964 (sensitivity = 0.882, specificity = 0.963). Compared with SUVR, the diagnostic efficiency of the model was improved significantly. The DCA also confirmed the clinical efficacy of the model.

CONCLUSIONS

The multiparameter diagnosis model based on F-FDG PET and clinical characteristics had excellent value in the differential diagnosis of TETs and thymic lymphomas.