Prediction of Hypoxia in Brain Tumors Using a Multivariate Model Built from MR Imaging and (18)F-Fluorodeoxyglucose Accumulation Data

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 (18)F-fluorodeoxyglucose (FDG) to predict the extent of hypoxia in an (18)F-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. K(trans) and V(p) 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 K(trans) (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). CONCLUSION: 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.