Radiomics-based evaluation and possible characterization of dynamic contrast enhanced (DCE) perfusion derived different sub-regions of Glioblastoma. Issue 159 (February 2023)

• Record Type: Journal Article
• Authors: Suhail Parvaze, ; Bhattacharjee, Rupsa; Singh, Anup; Ahlawat, Sunita; Patir, Rana; Vaishya, Sandeep; Shah, Tejas J.; Gupta, Rakesh K.
• Is Part Of: European journal of radiology. Issue 159(2023)
• Publication Date: 2023-02
• Subjects: PVE Peritumoral vasogenic edema -- BM Brain metastasis -- GB Glioblastoma -- IDH Isocitrate dehydrogenase -- NET Non-enhancing tumor -- CET Contrast-enhancing tumor -- DSC Dynamic susceptibility contrast -- DCE Dynamic contrast enhanced -- RF Random Forest -- ML Machine Learning -- BBB Blood brain barrier -- rCBV Relative cerebral blood volume -- ADC Apparent diffusion constant -- FLAIR Fluid-attenuated inversion recovery -- TSE Turbo spin echo -- SWI Susceptibility weighted imaging -- DWI Diffusion weighted imaging -- NAWM Normal appearing white matter; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2022.110655
• Languages: English
• ISSNs: 0720-048X

Abstract

Abstract: Background: Glioblastoma (GB) is among the most devastative brain tumors, which usually comprises sub-regions like enhancing tumor (ET), non-enhancing tumor (NET), edema (ED), and necrosis (NEC) as described on MRI. Semi-automated algorithms to extract these tumor subpart volumes and boundaries have been demonstrated using dynamic contrast-enhanced (DCE) perfusion imaging. We aim to characterize these sub-regions derived from DCE perfusion MRI using routine 3D post-contrast-T1 (T1GD) and FLAIR images with the aid of Radiomics analysis. We also explored the possibility of separating edema from tumor sub-regions by extracting the most influential radiomics features. Methods: A total of 89 patients with histopathological confirmed IDH wild type GB were considered, who underwent the MR imaging with DCE perfusion-MRI. Perfusion and kinetic indices were computed and further used to segment tumor sub-regions. Radiomics features were extracted from FLAIR and T1GD images with PyRadiomics tool. Statistical analysis of the features was carried out using two approaches as well as machine learning (ML) models were constructed separately, i) within different tumor sub-regions and ii) ED as one category and the remaining sub-regions combined as another category. ML based predictive feature maps was also constructed. Results: Seven features found to be statistically significant to differentiate tumor sub-regions in FLAIR and T1GD images, with p-value < 0.05 and AUC values in the range of 0.72 to 0.93. However, the edema features stood out in the analysis. In the second approach, the ML model was able to categorize the ED from the rest of the tumor sub-regions in FLAIR and T1GD images with AUC of 0.95 and 0.89 respectively. Conclusion: Radiomics-based specific feature values and maps help to characterize different tumor sub-regions. However, the GLDM_DependenceNonUniformity feature appears to be most specific for separating edema from the remaining tumor sub-regions using conventional FLAIR images. This may be of value in the segmentation of edema from tumors using conventional MRI in the future.