HUSCAP logo Hokkaido Univ. logo

Hokkaido University Collection of Scholarly and Academic Papers >
Global Institution for Collaborative Research and Education : GI-CoRE >
Peer-reviewed Journal Articles, etc >

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

This item is licensed under: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International

Files in This Item:

The file(s) associated with this item can be obtained from the following URL:https://doi.org/10.2463/mrms.mp.2019-0049


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)

Export metadata:

OAI-PMH ( junii2 , jpcoar )

MathJax is now OFF:


 

 - Hokkaido University