Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine 2019-Oct
Prediction of Hypoxia in Brain Tumors Using a Multivariate Model Built from MR Imaging and 18F-Fluorodeoxyglucose Accumulation Data.
Nur registrierte Benutzer können Artikel übersetzen
Einloggen Anmelden
Der Link wird in der Zwischenablage gespeichert
Schlüsselwörter
Abstrakt
PURPOSE
The aim of this study was to generate a multivariate model using various MRI markers of blood flow and vascular permeability and accumulation of 18F-fluorodeoxyglucose (FDG) to predict the extent of hypoxia in an 18F-fluoromisonidazole (FMISO)-positive region.METHODS
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. Ktrans and Vp maps were generated using the DCE images. FDG and FMISO positron emission tomography scans were also obtained. A model for predicting FMISO positivity was generated on a voxel-by-voxel basis by a multivariate logistic regression model using all the MRI parameters with and without FDG. Receiver-operating characteristic curve analysis was used to detect FMISO positivity with multivariate and univariate analysis of each parameter. Cross-validation was performed using the leave-one-out method.RESULTS
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 Ktrans (0.844 for all). In cross-validation, the multivariate model with FDG had the highest AUC (0.857 ± 0.08) followed by the multivariate model without FDG (0.834 ± 0.119).