Diffusion kurtosis imaging for characterizing tumor heterogeneity in an intracranial rat glioblastoma model.
NMR Biomed
; 33(11): e4386, 2020 11.
Article
en En
| MEDLINE
| ID: mdl-32729637
The utility of diffusion kurtosis imaging (DKI) for assessing intra-tumor heterogeneity was evaluated in a rat model of glioblastoma multiforme. Longitudinal MRI including T2 -weighted and diffusion-weighted MRI (DWI) was performed on six female Fischer rats 8, 11 and 14 days after intracranial transplantation of F98 cells. T2 -weighted images were used to measure the tumor volumes and DWI images were used to compute diffusion tensor imaging (DTI) and DWI based parametric maps including mean diffusivity (MD), mean kurtosis (MK), axial diffusivity (AD), axial kurtosis, radial diffusivity, radial kurtosis, fractional anisotropy (FA) and kurtosis fractional anisotropy (KFA). Median values from the segmented normal contralateral cortex, tumor and edema from the diffusion parameters were compared at the three imaging time points to assess any changes in tumor heterogeneity over time. ex vivo DKI was also performed in a representative sample and compared with histology. Significant differences were observed between normal cortex, tumor and edema in both the DTI and DKI parameters. Notably, at the earliest time point MK and KFA were significantly different between normal cortex and tumor in comparison with MD or FA. Although a decreasing trend in MD, AD and FA values of the tumor were observed as the tumor grew, no significant changes in any of the DTI or DKI parameters were observed longitudinally. While DKI was equally sensitive to DTI in differentiating tumor from edema and normal brain, it was unable to detect longitudinal increases in intra-tumoral heterogeneity in the F98 model of glioblastoma multiforme.
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1
Banco de datos:
MEDLINE
Asunto principal:
Neoplasias Encefálicas
/
Glioblastoma
/
Imagen de Difusión Tensora
Límite:
Animals
Idioma:
En
Revista:
NMR Biomed
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
/
MEDICINA NUCLEAR
Año:
2020
Tipo del documento:
Article