Prediction of Hypoxia in Brain Tumors Using a Multivariate Model Built from MR Imaging and
brain tumors
hypoxia
magnetic resonance imaging
positron emission tomography
prediction model
Journal
Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
ISSN: 1880-2206
Titre abrégé: Magn Reson Med Sci
Pays: Japan
ID NLM: 101153368
Informations de publication
Date de publication:
03 Aug 2020
03 Aug 2020
Historique:
pubmed:
16
10
2019
medline:
24
11
2020
entrez:
16
10
2019
Statut:
ppublish
Résumé
The aim of this study was to generate a multivariate model using various MRI markers of blood flow and vascular permeability and accumulation of Fifteen patients aged 27-74 years with brain tumors (glioma, n = 13; lymphoma, n = 1; germinoma, n = 1) were included. MRI scans were performed using a 3T scanner, and dynamic contrast-enhanced (DCE) perfusion and arterial spin labeling images were obtained. K The area under the curve (AUC) was highest for the multivariate prediction model with FDG (0.892) followed by the multivariate model without FDG and univariate analysis with FDG and K A multivariate prediction model created using blood flow, vascular permeability, and glycometabolism parameters can predict the extent of hypoxia in FMISO-positive areas in patients with brain tumors.
Identifiants
pubmed: 31611541
doi: 10.2463/mrms.mp.2019-0049
pmc: PMC7553805
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
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