Quantification of Radiomics features of peritumoral vasogenic edema extracted from FLAIR images in glioblastoma and isolated brain metastasis, using T1-DCE perfusion analysis.

The peritumoral vasogenic edema (PVE) in brain tumors exhibits varied characteristics. Brain metastasis (BM) and meningioma barely have tumor cells in PVE, while Glioblastoma (GB) show tumor cell infiltration in most subjects. The purpose of this study was to investigate the PVE of these three pathologies using Radiomics features in FLAIR images, with the hypothesis that the tumor cells might influence textural variation. Ex-vivo experimentation of Radiomics analysis of T1-weighted images of the culture medium with and without suspended tumor cells was also attempted to infer the possible influence of increasing tumor cells on Radiomics features. This retrospective study involved MR images acquired using 3.0T MR machine from 83 patients having 48 GB, 21 BM and 14 Meningioma. The 93 Radiomics features were extracted from each subject's PVE mask from 3 pathologies using T1-DCE MRI. Statistically significant (<0.05, independent samples T-test) features were considered. Features maps were also computed for qualitative investigation. The same was carried out for T1-weighted cell line images but group comparison was carried using One-way ANOVA. Further, a random forest (RF) based machine learning (ML) model was designed to classify the PVE of GB and BM. The texture-based variation especially higher non-uniformity values were observed in the PVE of GB. No significance was observed between BM and Meningioma PVE. In cell line images, the culture medium had higher non-uniformity and considerably reduced with increasing cell densities in 4 features. RF model implemented with highly significant features provided improved AUC results. The possible infiltrative tumor cells in the PVE of the GB are likely influencing the texture values and are higher in comparison with BM PVE and may be of value in differentiation of solitary metastasis from GB. However, the robustness of the features needs to be investigated with larger cohort and across the scanners in future.