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

Shimizu, Yukie; Kudo, Kohsuke; Kameda, Hiroyuki; Harada, Taisuke; Fujima, Noriyuki; Toyonaga, Takuya; Tha, Khin Khin; Shirato, Hiroki

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

Shimizu, Yukie; Kudo, Kohsuke; Kameda, Hiroyuki et al.

Magnetic resonance in medical sciences, 2020, 19(3), 227-234

Permalink : https://hdl.handle.net/2115/79244

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アクセス権	open access
URI	https://hdl.handle.net/2115/79244
タイトル	Prediction of Hypoxia in Brain Tumors Using a Multivariate Model Built from MR Imaging and F-18-Fluorodeoxyglucose Accumulation Data
著者	Shimizu, Yukie ; Kudo, Kohsuke ; Kameda, Hiroyuki ; Harada, Taisuke ; Fujima, Noriyuki ; Toyonaga, Takuya ; Tha, Khin Khin ; Shirato, Hiroki
著者名 Shimizu, Yukie
e-Rad_Researcher 10374232 著者名 Kudo, Kohsuke
著者名 Kameda, Hiroyuki
著者名 Harada, Taisuke
e-Rad_Researcher 80431360 著者名 Fujima, Noriyuki
著者名 Toyonaga, Takuya
e-Rad_Researcher 20451445 著者名 Tha, Khin Khin
e-Rad_Researcher 20187537 著者名 Shirato, Hiroki
言語	英語
キーワード	brain tumors
	hypoxia
	magnetic resonance imaging
	positron emission tomography
	prediction model
発行日	2020-08-03
出版者	Japanese Society for Magnetic Resonance in Medicine
収録物名	Magnetic resonance in medical sciences
巻(号)	19(3)
ページ	227-234
抄録	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.
資源タイプ	学術雑誌論文
出版タイプ	Version of Record
権利情報	Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
PISSN	1347-3182
関連情報 (isIdenticalTo)	出版社版DOI https://doi.org/10.2463/mrms.mp.2019-0049