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Prediction of Hypoxia in Brain Tumors Using a Multivariate Model Built from MR Imaging and 18F-Fluorodeoxyglucose Accumulation Data.
Shimizu, Yukie; Kudo, Kohsuke; Kameda, Hiroyuki; Harada, Taisuke; Fujima, Noriyuki; Toyonaga, Takuya; Tha, Khin Khin; Shirato, Hiroki.
Afiliación
  • Shimizu Y; Department of Radiation Medicine, Hokkaido University Graduate School of Medicine.
  • Kudo K; Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital.
  • Kameda H; Global Station for Quantum Medical Science and Engineering, Global Institution for Collaborative Research and Education, Hokkaido University.
  • Harada T; Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital.
  • Fujima N; Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital.
  • Toyonaga T; Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital.
  • Tha KK; Department of Nuclear Medicine, Hokkaido University Graduate School of Medicine.
  • Shirato H; Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital.
Magn Reson Med Sci ; 19(3): 227-234, 2020 Aug 03.
Article en En | MEDLINE | ID: mdl-31611541
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 18F-fluorodeoxyglucose (FDG) to predict the extent of hypoxia in an 18F-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. Ktrans and Vp 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 Ktrans (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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Encefálicas / Imagen por Resonancia Magnética / Interpretación de Imagen Asistida por Computador / Hipoxia de la Célula Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Adult / Aged / Humans / Middle aged Idioma: En Revista: Magn Reson Med Sci Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2020 Tipo del documento: Article Pais de publicación: Japón
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Encefálicas / Imagen por Resonancia Magnética / Interpretación de Imagen Asistida por Computador / Hipoxia de la Célula Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Adult / Aged / Humans / Middle aged Idioma: En Revista: Magn Reson Med Sci Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2020 Tipo del documento: Article Pais de publicación: Japón