Feasibility of generalised diffusion kurtosis imaging approach for brain glioma grading.

Autor: Pogosbekian EL; Neuroimaging Department, N.N. Burdenko National Medical Research Centre of Neurosurgery, Moscow, Russian Federation.; General and Clinical Neurophysiology Lab, Institute of Higher Nervous Activity and Neurophysiology of RAS, Moscow, Russian Federation., Pronin IN; Neuroimaging Department, N.N. Burdenko National Medical Research Centre of Neurosurgery, Moscow, Russian Federation., Zakharova NE; Neuroimaging Department, N.N. Burdenko National Medical Research Centre of Neurosurgery, Moscow, Russian Federation., Batalov AI; Neuroimaging Department, N.N. Burdenko National Medical Research Centre of Neurosurgery, Moscow, Russian Federation., Turkin AM; Neuroimaging Department, N.N. Burdenko National Medical Research Centre of Neurosurgery, Moscow, Russian Federation., Konakova TA; Neuroimaging Department, N.N. Burdenko National Medical Research Centre of Neurosurgery, Moscow, Russian Federation., Maximov II; Department of Psychology, University of Oslo, Oslo, Norway. ivan.maximov@psykologi.uio.no.; Department of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital, Oslo, Norway. ivan.maximov@psykologi.uio.no.; Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway. ivan.maximov@psykologi.uio.no.
Jazyk: angličtina
Zdroj: Neuroradiology [Neuroradiology] 2021 Aug; Vol. 63 (8), pp. 1241-1251. Date of Electronic Publication: 2021 Jan 07.
DOI: 10.1007/s00234-020-02613-7
Abstrakt: Purpose: An accurate differentiation of brain glioma grade constitutes an important clinical issue. Powerful non-invasive approach based on diffusion MRI has already demonstrated its feasibility in glioma grade stratification. However, the conventional diffusion tensor (DTI) and kurtosis imaging (DKI) demonstrated moderate sensitivity and performance in glioma grading. In the present work, we apply generalised DKI (gDKI) approach in order to assess its diagnostic accuracy and potential application in glioma grading.
Methods: Diffusion scalar metrics were obtained from 50 patients with different glioma grades confirmed by histological tests following biopsy or surgery. All patients were divided into two groups with low- and high-grade gliomas as grade II versus grades III and IV, respectively. For a comparison, trained radiologists segmented the brain tissue into three regions with solid tumour, oedema, and normal appearing white matter. For each region, we estimated the conventional and gDKI metrics including DTI maps.
Results: We found high correlations between DKI and gDKI metrics in high-grade glioma. Further, gDKI metrics enabled introduction of a complementary measure for glioma differentiation based on correlations between the conventional and generalised approaches. Both conventional and generalised DKI metrics showed quantitative maps of tumour heterogeneity and oedema behaviour. gDKI approach demonstrated largely similar sensitivity and specificity in low-high glioma differentiation as in the case of conventional DKI method.
Conclusion: The generalised diffusion kurtosis imaging enables differentiation of low- and high-grade gliomas at the same level as the conventional DKI. Additionally, gDKI exhibited higher sensitivity to tumour heterogeneity and tissue contrast between tumour and healthy tissue and, thus, may contribute as a complementary source of information on tumour differentiation.
(© 2021. The Author(s).)
Databáze: MEDLINE
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