Abstract

Malignant gliomas are primary brain tumours with an infiltrative growth pattern, often with contrast enhancement on magnetic resonance imaging (MRI). However, it is well known that tumour infiltration extends beyond the visible contrast enhancement. The aim of this study was to investigate if there is contrast enhancement not detected visually in the peritumoral oedema of malignant gliomas by using relaxometry with synthetic MRI. 25 patients who had brain tumours with a radiological appearance of malignant glioma were prospectively included. A quantitative MR-sequence measuring longitudinal relaxation (R1), transverse relaxation (R2) and proton density (PD), was added to the standard MRI protocol before surgery. Five patients were excluded, and in 20 patients, synthetic MR images were created from the quantitative scans. Manual regions of interest (ROIs) outlined the visibly contrast-enhancing border of the tumours and the peritumoral area. Contrast enhancement was quantified by subtraction of native images from post GD-images, creating an R1-difference-map. The quantitative R1-difference-maps showed significant contrast enhancement in the peritumoral area (0.047) compared to normal appearing white matter (0.032), p = 0.048. Relaxometry detects contrast enhancement in the peritumoral area of malignant gliomas. This could represent infiltrative tumour growth.

Details

Title
Quantitative MRI using relaxometry in malignant gliomas detects contrast enhancement in peritumoral oedema
Author
Blystad, I 1   VIAFID ORCID Logo  ; Warntjes J B M 2 ; Smedby, Ö 3 ; Lundberg, P 4 ; E-M, Larsson 5 ; Tisell, A 4 

 Linköping University, Department of Radiology in Linköping and Department of Health, Medicine and Caring Sciences, Linköping, Sweden (GRID:grid.5640.7) (ISNI:0000 0001 2162 9922); Linköping University, Centre for Medical Image Science and Visualization (CMIV), Linköping, Sweden (GRID:grid.5640.7) (ISNI:0000 0001 2162 9922) 
 Linköping University, Centre for Medical Image Science and Visualization (CMIV), Linköping, Sweden (GRID:grid.5640.7) (ISNI:0000 0001 2162 9922); Linköping University, Division of Cardiovascular Medicine, Department of Health, Medicine and Caring Sciences, Linköping, Sweden (GRID:grid.5640.7) (ISNI:0000 0001 2162 9922) 
 Linköping University, Department of Radiology in Linköping and Department of Health, Medicine and Caring Sciences, Linköping, Sweden (GRID:grid.5640.7) (ISNI:0000 0001 2162 9922); Linköping University, Centre for Medical Image Science and Visualization (CMIV), Linköping, Sweden (GRID:grid.5640.7) (ISNI:0000 0001 2162 9922); KTH Royal Institute of Technology, School of Technology and Health, Stockholm, Sweden (GRID:grid.5037.1) (ISNI:0000000121581746) 
 Linköping University, Centre for Medical Image Science and Visualization (CMIV), Linköping, Sweden (GRID:grid.5640.7) (ISNI:0000 0001 2162 9922); Linköping University, Department of Radiation Physics and Department of Health, Medicine and Caring Sciences, Linköping, Sweden (GRID:grid.5640.7) (ISNI:0000 0001 2162 9922) 
 Linköping University, Centre for Medical Image Science and Visualization (CMIV), Linköping, Sweden (GRID:grid.5640.7) (ISNI:0000 0001 2162 9922); Uppsala University, Department of Surgical Sciences, Radiology, Uppsala, Sweden (GRID:grid.8993.b) (ISNI:0000 0004 1936 9457) 
Publication year
2020
Publication date
2020
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2471519798
Copyright
© The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.