C Deuschl1,2, J Kirchner3, T D Poeppel4, B Schaarschmidt3, S Kebir5, N El Hindy6, J Hense7, H H Quick8,9, M Glas5, K Herrmann4, L Umutlu10, C Moenninghoff10, A Radbruch10, M Forsting10, M Schlamann10,11. 1. Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany. cornelius.deuschl@uk-essen.de. 2. Erwin L. Hahn Institute for Magnetic Resonance Imaging, University of Duisburg-Essen, Essen, Germany. cornelius.deuschl@uk-essen.de. 3. Department of Diagnostic and Interventional Radiology, Medical Faculty, University Duesseldorf, Duesseldorf, Germany. 4. Clinic for Nuclear Medicine, University Hospital Essen, Essen, Germany. 5. Division of Clinical Neurooncology, Department of Neurology, University Hospital Essen, Essen, Germany. 6. Department of Neurosurgery, University Hospital Essen, Essen, Germany. 7. Department of Medical Oncology, West German Cancer Center, University Hospital Essen, Essen, Germany. 8. Erwin L. Hahn Institute for Magnetic Resonance Imaging, University of Duisburg-Essen, Essen, Germany. 9. High Field and Hybrid MR Imaging, University Hospital Essen, Essen, Germany. 10. Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany. 11. Department of Diagnostic and Interventional Radiology, University Hospital Cologne, Cologne, Germany.
Abstract
INTRODUCTION: Radiological assessment of brain tumors is widely based on the Radiology Assessment of Neuro-Oncology (RANO) criteria that consider non-specific T1 and T2 weighted images. Limitation of the RANO criteria is that they do not include metabolic imaging techniques that have been reported to be helpful to differentiate treatment related changes from true tumor progression. In the current study, we assessed if the combined use of MRI and PET with hybrid 11C-MET PET/MRI can improve diagnostic accuracy and diagnostic confidence of the readers to differentiate treatment related changes from true progression in recurrent glioma. METHODS: Fifty consecutive patients with histopathologically proven glioma were prospectively enrolled for a hybrid 11C-MET PET/MRI to differentiate recurrent glioma from treatment induced changes. Sole MRI data were analyzed based on RANO. Sole PET data and in a third evaluation hybrid 11C-MET-PET/MRI data were assessed for metabolic respectively metabolic and morphologic glioma recurrence. Diagnostic performance and diagnostic confidence of the reader were calculated for the different modalities, and the McNemar test and Mann-Whitney U Test were applied for statistical analysis. RESULTS: Hybrid 11C-MET PET/MRI was successfully performed in all 50 patients. Glioma recurrence was diagnosed in 35 of the 50 patients (70%). Sensitivity and specificity were calculated for MRI (86.11% and 71.43%), for 11C-MET PET (96.77% and 73.68%), and for hybrid 11C-MET-PET/MRI (97.14% and 93.33%). For diagnostic accuracy hybrid 11C-MET-PET/MRI (96%) showed significantly higher values than MRI alone (82%), whereas no significant difference was found for 11C-MET PET (88%). Furthermore, by rating on a five-point Likert scale significantly higher scores were found for diagnostic confidence when comparing 11C-MET PET/MRI (4.26 ± 0,777) to either PET alone (3.44 ± 0.705) or MRI alone (3.56 ± 0.733). CONCLUSION: This feasibility study showed that hybrid PET/MRI might strengthen RANO classification by adding metabolic information to conventional MRI information. Future studies should evaluate the clinical utility of the combined use of 11C-MET PET/MRI in larger patient cohorts.
INTRODUCTION: Radiological assessment of brain tumors is widely based on the Radiology Assessment of Neuro-Oncology (RANO) criteria that consider non-specific T1 and T2 weighted images. Limitation of the RANO criteria is that they do not include metabolic imaging techniques that have been reported to be helpful to differentiate treatment related changes from true tumor progression. In the current study, we assessed if the combined use of MRI and PET with hybrid 11C-MET PET/MRI can improve diagnostic accuracy and diagnostic confidence of the readers to differentiate treatment related changes from true progression in recurrent glioma. METHODS: Fifty consecutive patients with histopathologically proven glioma were prospectively enrolled for a hybrid 11C-MET PET/MRI to differentiate recurrent glioma from treatment induced changes. Sole MRI data were analyzed based on RANO. Sole PET data and in a third evaluation hybrid 11C-MET-PET/MRI data were assessed for metabolic respectively metabolic and morphologic glioma recurrence. Diagnostic performance and diagnostic confidence of the reader were calculated for the different modalities, and the McNemar test and Mann-Whitney U Test were applied for statistical analysis. RESULTS: Hybrid 11C-MET PET/MRI was successfully performed in all 50 patients. Glioma recurrence was diagnosed in 35 of the 50 patients (70%). Sensitivity and specificity were calculated for MRI (86.11% and 71.43%), for 11C-MET PET (96.77% and 73.68%), and for hybrid 11C-MET-PET/MRI (97.14% and 93.33%). For diagnostic accuracy hybrid 11C-MET-PET/MRI (96%) showed significantly higher values than MRI alone (82%), whereas no significant difference was found for 11C-MET PET (88%). Furthermore, by rating on a five-point Likert scale significantly higher scores were found for diagnostic confidence when comparing 11C-MET PET/MRI (4.26 ± 0,777) to either PET alone (3.44 ± 0.705) or MRI alone (3.56 ± 0.733). CONCLUSION: This feasibility study showed that hybrid PET/MRI might strengthen RANO classification by adding metabolic information to conventional MRI information. Future studies should evaluate the clinical utility of the combined use of 11C-MET PET/MRI in larger patient cohorts.
Authors: Zhaolin Chen; Sharna D Jamadar; Shenpeng Li; Francesco Sforazzini; Jakub Baran; Nicholas Ferris; Nadim Jon Shah; Gary F Egan Journal: Hum Brain Mapp Date: 2018-08-04 Impact factor: 5.038
Authors: D Zopfs; K Laukamp; R Reimer; N Grosse Hokamp; C Kabbasch; J Borggrefe; L Pennig; A C Bunck; M Schlamann; S Lennartz Journal: AJNR Am J Neuroradiol Date: 2022-01-06 Impact factor: 3.825
Authors: Anastasia Zikou; Chrissa Sioka; George A Alexiou; Andreas Fotopoulos; Spyridon Voulgaris; Maria I Argyropoulou Journal: Contrast Media Mol Imaging Date: 2018-12-02 Impact factor: 3.161
Authors: Daniel F Fleischmann; Marcus Unterrainer; Stefanie Corradini; Maya Rottler; Stefan Förster; Christian la Fougère; Timo Siepmann; Markus Schwaiger; Peter Bartenstein; Claus Belka; Nathalie L Albert; Maximilian Niyazi Journal: PLoS One Date: 2019-07-24 Impact factor: 3.240