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 F-18-fluorodeoxyglucose (FDG) to predict the extent of hypoxia in an F-18-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.