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- Fahlström, Markus, et al.
(författare)
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Perfusion magnetic resonance imaging changes in normal appearing brain tissue after radiotherapy in glioblastoma patients may confound longitudinal evaluation of treatment response
- 2018
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Ingår i: Radiology and Oncology. - : Walter de Gruyter. - 1318-2099 .- 1581-3207. ; 52:2, s. 143-151
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Tidskriftsartikel (refereegranskat)abstract
- Background: The aim of this study was assess acute and early delayed radiation-induced changes in normal-appearing brain tissue perfusion as measured with perfusion magnetic resonance imaging (MRI) and the dependence of these changes on the fractionated radiotherapy (FRT) dose level.Patients and methods: Seventeen patients with glioma WHO grade III-IV treated with FRT were included in this prospective study, seven were excluded because of inconsistent FRT protocol or missing examinations. Dynamic susceptibility contrast MRI and contrast-enhanced 3D-T1-weighted (3D-T1w) images were acquired prior to and in average (standard deviation): 3.1 (3.3), 34.4 (9.5) and 103.3 (12.9) days after FRT. Pre-FRT 3D-T1w images were segmented into white-and grey matter. Cerebral blood volume (CBV) and cerebral blood flow (CBF) maps were calculated and co-registered patient-wise to pre-FRT 3D-T1w images. Seven radiation dose regions were created for each tissue type: 0-5 Gy, 5-10 Gy, 10-20 Gy, 20-30 Gy, 30-40 Gy, 40-50 Gy and 50-60 Gy. Mean CBV and CBF were calculated in each dose region and normalised (nCBV and nCBF) to the mean CBV and CBF in 0-5 Gy white-and grey matter reference regions, respectively.Results: Regional and global nCBV and nCBF in white-and grey matter decreased after FRT, followed by a tendency to recover. The response of nCBV and nCBF was dose-dependent in white matter but not in grey matter.Conclusions: Our data suggest that radiation-induced perfusion changes occur in normal-appearing brain tissue after FRT. This can cause an overestimation of relative tumour perfusion using dynamic susceptibility contrast MRI, and can thus confound tumour treatment evaluation.
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| 2. |
- Falk Delgado, Anna, et al.
(författare)
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Diffusion kurtosis imaging of gliomas grades II and III : a study of perilesional tumor infiltration, tumor grades and subtypes at clinical presentation
- 2017
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Ingår i: Radiology and Oncology. - : Walter de Gruyter GmbH. - 1318-2099 .- 1581-3207. ; 51:2, s. 121-129
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Tidskriftsartikel (refereegranskat)abstract
- Background. Diffusion kurtosis imaging (DKI) allows for assessment of diffusion influenced by microcellular structures. We analyzed DKI in suspected low-grade gliomas prior to histopathological diagnosis. The aim was to investigate if diffusion parameters in the perilesional normal-appearing white matter (NAWM) differed from contralesional white matter, and to investigate differences between glioma malignancy grades II and III and glioma subtypes (astrocytomas and oligodendrogliomas).Patients and methods. Forty-eight patients with suspected low-grade glioma were prospectively recruited to this institutional review board-approved study and investigated with preoperative DKI at 3T after written informed consent. Patients with histologically proven glioma grades II or III were further analyzed (n=35). Regions of interest (ROIs) were delineated on T2FLAIR images and co-registered to diffusion MRI parameter maps. Mean DKI data were compared between perilesional and contralesional NAWM (student's t-test for dependent samples, Wilcoxon matched pairs test). Histogram DKI data were compared between glioma types and glioma grades (multiple comparisons of mean ranks for all groups). The discriminating potential for DKI in assessing glioma type and grade was assessed with receiver operating characteristics (ROC) curves.Results. There were significant differences in all mean DKI variables between perilesional and contralesional NAWM (p=< 0.000), except for axial kurtosis (p=0.099). Forty-four histogram variables differed significantly between glioma grades II (n=23) and III (n=12) (p=0.003-0.048) and 10 variables differed significantly between ACs (n=18) and ODs (n=17) (p=0.011-0.050). ROC curves of the best discriminating variables had an area under the curve (AUC) of 0.657-0.815.Conclusions. Mean DKI variables in perilesional NAWM differ significantly from contralesional NAWM, suggesting altered microstructure by tumor infiltration not depicted on morphological MRI. Histogram analysis of DKI data identifies differences between glioma grades and subtypes.
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