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Prediction of Hypoxia in Brain Tumors Using a Multivariate Model Built from MR Imaging and F-18-Fluorodeoxyglucose Accumulation Data
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| Title: | Prediction of Hypoxia in Brain Tumors Using a Multivariate Model Built from MR Imaging and F-18-Fluorodeoxyglucose Accumulation Data |
| Authors: | Shimizu, Yukie Browse this author | | Kudo, Kohsuke Browse this author →KAKEN DB | | Kameda, Hiroyuki Browse this author | | Harada, Taisuke Browse this author | | Fujima, Noriyuki Browse this author →KAKEN DB | | Toyonaga, Takuya Browse this author | | Tha, Khin Khin Browse this author →KAKEN DB | | Shirato, Hiroki Browse this author →KAKEN DB |
| Keywords: | brain tumors | | hypoxia | | magnetic resonance imaging | | positron emission tomography | | prediction model |
| Issue Date: | 3-Aug-2020 |
| Publisher: | Japanese Society for Magnetic Resonance in Medicine |
| Journal Title: | Magnetic resonance in medical sciences |
| Volume: | 19 |
| Issue: | 3 |
| Start Page: | 227 |
| End Page: | 234 |
| Publisher DOI: | 10.2463/mrms.mp.2019-0049 |
| Abstract: | 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. |
| Rights: | https://creativecommons.org/licenses/by-nc-nd/4.0/ |
| Type: | article |
| URI: | http://hdl.handle.net/2115/79244 |
| Appears in Collections: | 国際連携研究教育局 : GI-CoRE (Global Institution for Collaborative Research and Education : GI-CoRE) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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