Differentiating the grades of thymic epithelial tumor malignancy using textural features of intratumoral heterogeneity via ¹⁸F-FDG PET/CT

We aimed to explore the ability of textural heterogeneity indices determined by ¹⁸F-FDG PET/CT for grading the malignancy of thymic epithelial tumors (TETs).

We retrospectively enrolled 47 patients with pathologically proven TETs who underwent pre-treatment ¹⁸F-FDG PET/CT. TETs were classified by pathological results into three subgroups with increasing grades of malignancy: low-risk thymoma (LRT; WHO classification A, AB and B1), high-risk thymoma (B2 and B3), and thymic carcinoma (TC). Using ¹⁸F-FDG PET/CT, we obtained conventional imaging indices including SUV_max and 20 intratumoral heterogeneity indices: i.e., four local-scale indices derived from the neighborhood gray-tone difference matrix (NGTDM), eight regional-scale indices from the gray-level run-length matrix (GLRLM), and eight regional-scale indices from the gray-level size zone matrix (GLSZM). Area under the receiver operating characteristic curve (AUC) was used to demonstrate the abilities of the imaging indices for differentiating subgroups. Multivariable logistic regression analysis was performed to show the independent significance of the textural indices. Combined criteria using optimal cutoff values of the SUV_max and a best-performing heterogeneity index were applied to investigate whether they improved differentiation between the subgroups.

Most of the GLRLM and GLSZM indices and the SUV_max showed good or fair discrimination (AUC >0.7) with best performance for some of the GLRLM indices and the SUV_max, whereas the NGTDM indices showed relatively inferior performance. The discriminative ability of some of the GLSZM indices was independent from that of SUV_max in multivariate analysis. Combined use of the SUV_max and a GLSZM index improved positive predictive values for LRT and TC.

Texture analysis of ¹⁸F-FDG PET/CT scans has the potential to differentiate between TET tumor grades; regional-scale indices from GLRLM and GLSZM perform better than local-scale indices from the NGTDM. The SUV_max and heterogeneity indices may have complementary value in differentiating TET subgroups.