11C–MET PET/MRI for detection of recurrent glioma
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...
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| Autor*in: |
Deuschl, C. [verfasserIn] |
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E-Artikel |
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Englisch |
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2017 |
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| Anmerkung: |
© Springer-Verlag GmbH Germany, part of Springer Nature 2017 |
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| Übergeordnetes Werk: |
Enthalten in: European journal of nuclear medicine and molecular imaging - Heidelberg [u.a.] : Springer-Verl., 2002, 45(2017), 4 vom: 28. Dez., Seite 593-601 |
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| Übergeordnetes Werk: |
volume:45 ; year:2017 ; number:4 ; day:28 ; month:12 ; pages:593-601 |
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| DOI / URN: |
10.1007/s00259-017-3916-9 |
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SPR003165299 |
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| 100 | 1 | |a Deuschl, C. |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a 11C–MET PET/MRI for detection of recurrent glioma |
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| 520 | |a 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. | ||
| 650 | 4 | |a Pet/MRI |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Glioma |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Pseudoprogression |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Kirchner, J. |4 aut | |
| 700 | 1 | |a Poeppel, T. D. |4 aut | |
| 700 | 1 | |a Schaarschmidt, B. |4 aut | |
| 700 | 1 | |a Kebir, S. |4 aut | |
| 700 | 1 | |a El Hindy, N. |4 aut | |
| 700 | 1 | |a Hense, J. |4 aut | |
| 700 | 1 | |a Quick, H. H. |4 aut | |
| 700 | 1 | |a Glas, M. |4 aut | |
| 700 | 1 | |a Herrmann, K. |4 aut | |
| 700 | 1 | |a Umutlu, L. |4 aut | |
| 700 | 1 | |a Moenninghoff, C. |4 aut | |
| 700 | 1 | |a Radbruch, A. |4 aut | |
| 700 | 1 | |a Forsting, M. |4 aut | |
| 700 | 1 | |a Schlamann, M. |4 aut | |
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10.1007/s00259-017-3916-9 doi (DE-627)SPR003165299 (SPR)s00259-017-3916-9-e DE-627 ger DE-627 rakwb eng Deuschl, C. verfasserin aut 11C–MET PET/MRI for detection of recurrent glioma 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2017 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. Pet/MRI (dpeaa)DE-He213 Glioma (dpeaa)DE-He213 Pseudoprogression (dpeaa)DE-He213 Kirchner, J. aut Poeppel, T. D. aut Schaarschmidt, B. aut Kebir, S. aut El Hindy, N. aut Hense, J. aut Quick, H. H. aut Glas, M. aut Herrmann, K. aut Umutlu, L. aut Moenninghoff, C. aut Radbruch, A. aut Forsting, M. aut Schlamann, M. aut Enthalten in European journal of nuclear medicine and molecular imaging Heidelberg [u.a.] : Springer-Verl., 2002 45(2017), 4 vom: 28. Dez., Seite 593-601 (DE-627)359787258 (DE-600)2098375-X 1619-7089 nnns volume:45 year:2017 number:4 day:28 month:12 pages:593-601 https://dx.doi.org/10.1007/s00259-017-3916-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 45 2017 4 28 12 593-601 |
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10.1007/s00259-017-3916-9 doi (DE-627)SPR003165299 (SPR)s00259-017-3916-9-e DE-627 ger DE-627 rakwb eng Deuschl, C. verfasserin aut 11C–MET PET/MRI for detection of recurrent glioma 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2017 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. Pet/MRI (dpeaa)DE-He213 Glioma (dpeaa)DE-He213 Pseudoprogression (dpeaa)DE-He213 Kirchner, J. aut Poeppel, T. D. aut Schaarschmidt, B. aut Kebir, S. aut El Hindy, N. aut Hense, J. aut Quick, H. H. aut Glas, M. aut Herrmann, K. aut Umutlu, L. aut Moenninghoff, C. aut Radbruch, A. aut Forsting, M. aut Schlamann, M. aut Enthalten in European journal of nuclear medicine and molecular imaging Heidelberg [u.a.] : Springer-Verl., 2002 45(2017), 4 vom: 28. Dez., Seite 593-601 (DE-627)359787258 (DE-600)2098375-X 1619-7089 nnns volume:45 year:2017 number:4 day:28 month:12 pages:593-601 https://dx.doi.org/10.1007/s00259-017-3916-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 45 2017 4 28 12 593-601 |
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10.1007/s00259-017-3916-9 doi (DE-627)SPR003165299 (SPR)s00259-017-3916-9-e DE-627 ger DE-627 rakwb eng Deuschl, C. verfasserin aut 11C–MET PET/MRI for detection of recurrent glioma 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2017 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. Pet/MRI (dpeaa)DE-He213 Glioma (dpeaa)DE-He213 Pseudoprogression (dpeaa)DE-He213 Kirchner, J. aut Poeppel, T. D. aut Schaarschmidt, B. aut Kebir, S. aut El Hindy, N. aut Hense, J. aut Quick, H. H. aut Glas, M. aut Herrmann, K. aut Umutlu, L. aut Moenninghoff, C. aut Radbruch, A. aut Forsting, M. aut Schlamann, M. aut Enthalten in European journal of nuclear medicine and molecular imaging Heidelberg [u.a.] : Springer-Verl., 2002 45(2017), 4 vom: 28. Dez., Seite 593-601 (DE-627)359787258 (DE-600)2098375-X 1619-7089 nnns volume:45 year:2017 number:4 day:28 month:12 pages:593-601 https://dx.doi.org/10.1007/s00259-017-3916-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 45 2017 4 28 12 593-601 |
| allfieldsGer |
10.1007/s00259-017-3916-9 doi (DE-627)SPR003165299 (SPR)s00259-017-3916-9-e DE-627 ger DE-627 rakwb eng Deuschl, C. verfasserin aut 11C–MET PET/MRI for detection of recurrent glioma 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2017 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. Pet/MRI (dpeaa)DE-He213 Glioma (dpeaa)DE-He213 Pseudoprogression (dpeaa)DE-He213 Kirchner, J. aut Poeppel, T. D. aut Schaarschmidt, B. aut Kebir, S. aut El Hindy, N. aut Hense, J. aut Quick, H. H. aut Glas, M. aut Herrmann, K. aut Umutlu, L. aut Moenninghoff, C. aut Radbruch, A. aut Forsting, M. aut Schlamann, M. aut Enthalten in European journal of nuclear medicine and molecular imaging Heidelberg [u.a.] : Springer-Verl., 2002 45(2017), 4 vom: 28. Dez., Seite 593-601 (DE-627)359787258 (DE-600)2098375-X 1619-7089 nnns volume:45 year:2017 number:4 day:28 month:12 pages:593-601 https://dx.doi.org/10.1007/s00259-017-3916-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 45 2017 4 28 12 593-601 |
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10.1007/s00259-017-3916-9 doi (DE-627)SPR003165299 (SPR)s00259-017-3916-9-e DE-627 ger DE-627 rakwb eng Deuschl, C. verfasserin aut 11C–MET PET/MRI for detection of recurrent glioma 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2017 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. Pet/MRI (dpeaa)DE-He213 Glioma (dpeaa)DE-He213 Pseudoprogression (dpeaa)DE-He213 Kirchner, J. aut Poeppel, T. D. aut Schaarschmidt, B. aut Kebir, S. aut El Hindy, N. aut Hense, J. aut Quick, H. H. aut Glas, M. aut Herrmann, K. aut Umutlu, L. aut Moenninghoff, C. aut Radbruch, A. aut Forsting, M. aut Schlamann, M. aut Enthalten in European journal of nuclear medicine and molecular imaging Heidelberg [u.a.] : Springer-Verl., 2002 45(2017), 4 vom: 28. Dez., Seite 593-601 (DE-627)359787258 (DE-600)2098375-X 1619-7089 nnns volume:45 year:2017 number:4 day:28 month:12 pages:593-601 https://dx.doi.org/10.1007/s00259-017-3916-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 45 2017 4 28 12 593-601 |
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Deuschl, C. @@aut@@ Kirchner, J. @@aut@@ Poeppel, T. D. @@aut@@ Schaarschmidt, B. @@aut@@ Kebir, S. @@aut@@ El Hindy, N. @@aut@@ Hense, J. @@aut@@ Quick, H. H. @@aut@@ Glas, M. @@aut@@ Herrmann, K. @@aut@@ Umutlu, L. @@aut@@ Moenninghoff, C. @@aut@@ Radbruch, A. @@aut@@ Forsting, M. @@aut@@ Schlamann, M. @@aut@@ |
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Deuschl, C. |
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Deuschl, C. misc Pet/MRI misc Glioma misc Pseudoprogression 11C–MET PET/MRI for detection of recurrent glioma |
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1619-7089 |
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11C–MET PET/MRI for detection of recurrent glioma Pet/MRI (dpeaa)DE-He213 Glioma (dpeaa)DE-He213 Pseudoprogression (dpeaa)DE-He213 |
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misc Pet/MRI misc Glioma misc Pseudoprogression |
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European journal of nuclear medicine and molecular imaging |
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European journal of nuclear medicine and molecular imaging |
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11C–MET PET/MRI for detection of recurrent glioma |
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11C–MET PET/MRI for detection of recurrent glioma |
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Deuschl, C. |
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European journal of nuclear medicine and molecular imaging |
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European journal of nuclear medicine and molecular imaging |
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Deuschl, C. Kirchner, J. Poeppel, T. D. Schaarschmidt, B. Kebir, S. El Hindy, N. Hense, J. Quick, H. H. Glas, M. Herrmann, K. Umutlu, L. Moenninghoff, C. Radbruch, A. Forsting, M. Schlamann, M. |
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Elektronische Aufsätze |
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Deuschl, C. |
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10.1007/s00259-017-3916-9 |
| title_sort |
11c–met pet/mri for detection of recurrent glioma |
| title_auth |
11C–MET PET/MRI for detection of recurrent glioma |
| 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. © Springer-Verlag GmbH Germany, part of Springer Nature 2017 |
| abstractGer |
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. © Springer-Verlag GmbH Germany, part of Springer Nature 2017 |
| abstract_unstemmed |
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. © Springer-Verlag GmbH Germany, part of Springer Nature 2017 |
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11C–MET PET/MRI for detection of recurrent glioma |
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Kirchner, J. Poeppel, T. D. Schaarschmidt, B. Kebir, S. El Hindy, N. Hense, J. Quick, H. H. Glas, M. Herrmann, K. Umutlu, L. Moenninghoff, C. Radbruch, A. Forsting, M. Schlamann, M. |
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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). 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