Quantification of Radiomics features of Peritumoral Vasogenic Edema extracted from fluid‐attenuated inversion recovery images in glioblastoma and isolated brain metastasis, using T1‐dynamic contrast‐enhanced Perfusion analysis. (23rd December 2022)

• Record Type: Journal Article
• Authors: Parvaze, P. Suhail; Bhattacharjee, Rupsa; Verma, Yogesh Kumar; Singh, Rakesh Kumar; Yadav, Virendra; Singh, Anup; Khanna, Gaurav; Ahlawat, Sunita; Trivedi, Richa; Patir, Rana; Vaishya, Sandeep; Shah, Tejas J.; Gupta, Rakesh K.
• Is Part Of: NMR in biomedicine. Volume 36:Number 5(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-23
• Subjects: brain metastases -- brain tumor -- DCE perfusion MRI -- glioblastoma -- MRI -- radiomics; Nuclear magnetic resonance -- Periodicals; Magnetic Resonance Spectroscopy -- Periodicals; 574
• DOI: 10.1002/nbm.4884
• Languages: English
• ISSNs: 0952-3480

Abstract

Abstract: 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 magnetic resonance (MR) images acquired using a 3.0‐T MR machine from 83 patients with 48 GB, 21 BM, and 14 meningioma. The 93 radiomics features were extracted from each subject's PVE mask from three pathologies using T1‐dynamic contrast‐enhanced MR imaging. 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 out using one‐way analysis of variance. Further, a random forest (RF)‐based machine learning model was designed to classify the PVE of GB and BM. Texture‐based variations, especially higher nonuniformity 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 nonuniformity and was considerably reduced with increasing cell densities in four features. The RF model implemented with highly significant features provided improved area under the curve 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 the differentiation of solitary metastasis from GB. However, the robustness of the features needs to be investigated with a larger cohort and across different scanners in the future. Abstract : 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.