Differentiating primary CNS lymphoma from glioblastoma multiforme: assessment using arterial spin labeling, diffusion-weighted imaging, and 18F-fluorodeoxyglucose positron emission tomography
Introduction Our purpose was to evaluate the diagnostic performance of arterial spin labeling (ASL) perfusion imaging, diffusion-weighted imaging (DWI), and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) in differentiating primary central nervous system lymphomas (PCNSLs) from gliobla...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yamashita, Koji [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2012 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag 2012 |
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| Übergeordnetes Werk: |
Enthalten in: Neuroradiology - Berlin : Springer, 1970, 55(2012), 2 vom: 09. Sept., Seite 135-143 |
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| Übergeordnetes Werk: |
volume:55 ; year:2012 ; number:2 ; day:09 ; month:09 ; pages:135-143 |
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| DOI / URN: |
10.1007/s00234-012-1089-6 |
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| Katalog-ID: |
SPR002660253 |
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| 041 | |a eng | ||
| 100 | 1 | |a Yamashita, Koji |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Differentiating primary CNS lymphoma from glioblastoma multiforme: assessment using arterial spin labeling, diffusion-weighted imaging, and 18F-fluorodeoxyglucose positron emission tomography |
| 264 | 1 | |c 2012 | |
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| 500 | |a © Springer-Verlag 2012 | ||
| 520 | |a Introduction Our purpose was to evaluate the diagnostic performance of arterial spin labeling (ASL) perfusion imaging, diffusion-weighted imaging (DWI), and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) in differentiating primary central nervous system lymphomas (PCNSLs) from glioblastoma multiformes (GBMs). Methods Fifty-six patients including 19 with PCNSL and 37 with GBM were retrospectively studied. From the ASL data, an absolute tumor blood flow (aTBF) and a relative tumor blood flow (rTBF) were obtained within the enhancing portion of each tumor. In addition, the minimum apparent diffusion coefficient (ADCmin) and the maximum standard uptake value (SUVmax) were obtained from DWI and FDG-PET data, respectively. Each of the four parameters was compared between PCNSLs and GBMs using Kruskal–Wallis test. The performance in discriminating between PCNSLs and GBMs was evaluated using the receiver-operating characteristics analysis. Area-under-the-curve (AUC) values were compared among the four parameters using a nonparametric method. Results The aTBF, rTBF, and ADCmin were significantly higher in GBMs (mean aTBF ± SD = 91.6 ± 56.0 mL/100 g/min, mean rTBF ± SD = 2.61 ± 1.61, mean ADCmin ± SD = 0.78 ± 0.19 × $ 10^{−3} $ $ mm^{2} $/s) than in PCNSLs (mean aTBF ± SD = 37.3 ± 10.5 mL/100 g/min, mean rTBF ± SD = 1.24 ± 0.37, mean ADCmin ± SD = 0.61 ± 0.13 × $ 10^{−3} $ $ mm^{2} $/s) (p < 0.005, respectively). In addition, SUVmax was significantly lower in GBMs (mean ± SD = 13.1 ± 6.34) than in PCNSLs (mean ± SD = 22.5 ± 7.83) (p < 0.005). The AUC for aTBF (0.888) was higher than those for rTBF (0.810), ADCmin (0.768), and SUVmax (0.848), although their difference was not statistically significant. Conclusion ASL perfusion imaging is useful for differentiating PCNSLs from GBMs as well as DWI and FDG-PET. | ||
| 650 | 4 | |a Arterial spin labeling |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Glioblastoma |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Primary CNS lymphoma |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Yoshiura, Takashi |4 aut | |
| 700 | 1 | |a Hiwatashi, Akio |4 aut | |
| 700 | 1 | |a Togao, Osamu |4 aut | |
| 700 | 1 | |a Yoshimoto, Koji |4 aut | |
| 700 | 1 | |a Suzuki, Satoshi O. |4 aut | |
| 700 | 1 | |a Abe, Koichiro |4 aut | |
| 700 | 1 | |a Kikuchi, Kazufumi |4 aut | |
| 700 | 1 | |a Maruoka, Yasuhiro |4 aut | |
| 700 | 1 | |a Mizoguchi, Masahiro |4 aut | |
| 700 | 1 | |a Iwaki, Toru |4 aut | |
| 700 | 1 | |a Honda, Hiroshi |4 aut | |
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10.1007/s00234-012-1089-6 doi (DE-627)SPR002660253 (SPR)s00234-012-1089-6-e DE-627 ger DE-627 rakwb eng Yamashita, Koji verfasserin aut Differentiating primary CNS lymphoma from glioblastoma multiforme: assessment using arterial spin labeling, diffusion-weighted imaging, and 18F-fluorodeoxyglucose positron emission tomography 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2012 Introduction Our purpose was to evaluate the diagnostic performance of arterial spin labeling (ASL) perfusion imaging, diffusion-weighted imaging (DWI), and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) in differentiating primary central nervous system lymphomas (PCNSLs) from glioblastoma multiformes (GBMs). Methods Fifty-six patients including 19 with PCNSL and 37 with GBM were retrospectively studied. From the ASL data, an absolute tumor blood flow (aTBF) and a relative tumor blood flow (rTBF) were obtained within the enhancing portion of each tumor. In addition, the minimum apparent diffusion coefficient (ADCmin) and the maximum standard uptake value (SUVmax) were obtained from DWI and FDG-PET data, respectively. Each of the four parameters was compared between PCNSLs and GBMs using Kruskal–Wallis test. The performance in discriminating between PCNSLs and GBMs was evaluated using the receiver-operating characteristics analysis. Area-under-the-curve (AUC) values were compared among the four parameters using a nonparametric method. Results The aTBF, rTBF, and ADCmin were significantly higher in GBMs (mean aTBF ± SD = 91.6 ± 56.0 mL/100 g/min, mean rTBF ± SD = 2.61 ± 1.61, mean ADCmin ± SD = 0.78 ± 0.19 × $ 10^{−3} $ $ mm^{2} $/s) than in PCNSLs (mean aTBF ± SD = 37.3 ± 10.5 mL/100 g/min, mean rTBF ± SD = 1.24 ± 0.37, mean ADCmin ± SD = 0.61 ± 0.13 × $ 10^{−3} $ $ mm^{2} $/s) (p < 0.005, respectively). In addition, SUVmax was significantly lower in GBMs (mean ± SD = 13.1 ± 6.34) than in PCNSLs (mean ± SD = 22.5 ± 7.83) (p < 0.005). The AUC for aTBF (0.888) was higher than those for rTBF (0.810), ADCmin (0.768), and SUVmax (0.848), although their difference was not statistically significant. Conclusion ASL perfusion imaging is useful for differentiating PCNSLs from GBMs as well as DWI and FDG-PET. Arterial spin labeling (dpeaa)DE-He213 Glioblastoma (dpeaa)DE-He213 Primary CNS lymphoma (dpeaa)DE-He213 Yoshiura, Takashi aut Hiwatashi, Akio aut Togao, Osamu aut Yoshimoto, Koji aut Suzuki, Satoshi O. aut Abe, Koichiro aut Kikuchi, Kazufumi aut Maruoka, Yasuhiro aut Mizoguchi, Masahiro aut Iwaki, Toru aut Honda, Hiroshi aut Enthalten in Neuroradiology Berlin : Springer, 1970 55(2012), 2 vom: 09. Sept., Seite 135-143 (DE-627)254638430 (DE-600)1462953-7 1432-1920 nnns volume:55 year:2012 number:2 day:09 month:09 pages:135-143 https://dx.doi.org/10.1007/s00234-012-1089-6 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_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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 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_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_2472 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 55 2012 2 09 09 135-143 |
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10.1007/s00234-012-1089-6 doi (DE-627)SPR002660253 (SPR)s00234-012-1089-6-e DE-627 ger DE-627 rakwb eng Yamashita, Koji verfasserin aut Differentiating primary CNS lymphoma from glioblastoma multiforme: assessment using arterial spin labeling, diffusion-weighted imaging, and 18F-fluorodeoxyglucose positron emission tomography 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2012 Introduction Our purpose was to evaluate the diagnostic performance of arterial spin labeling (ASL) perfusion imaging, diffusion-weighted imaging (DWI), and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) in differentiating primary central nervous system lymphomas (PCNSLs) from glioblastoma multiformes (GBMs). Methods Fifty-six patients including 19 with PCNSL and 37 with GBM were retrospectively studied. From the ASL data, an absolute tumor blood flow (aTBF) and a relative tumor blood flow (rTBF) were obtained within the enhancing portion of each tumor. In addition, the minimum apparent diffusion coefficient (ADCmin) and the maximum standard uptake value (SUVmax) were obtained from DWI and FDG-PET data, respectively. Each of the four parameters was compared between PCNSLs and GBMs using Kruskal–Wallis test. The performance in discriminating between PCNSLs and GBMs was evaluated using the receiver-operating characteristics analysis. Area-under-the-curve (AUC) values were compared among the four parameters using a nonparametric method. Results The aTBF, rTBF, and ADCmin were significantly higher in GBMs (mean aTBF ± SD = 91.6 ± 56.0 mL/100 g/min, mean rTBF ± SD = 2.61 ± 1.61, mean ADCmin ± SD = 0.78 ± 0.19 × $ 10^{−3} $ $ mm^{2} $/s) than in PCNSLs (mean aTBF ± SD = 37.3 ± 10.5 mL/100 g/min, mean rTBF ± SD = 1.24 ± 0.37, mean ADCmin ± SD = 0.61 ± 0.13 × $ 10^{−3} $ $ mm^{2} $/s) (p < 0.005, respectively). In addition, SUVmax was significantly lower in GBMs (mean ± SD = 13.1 ± 6.34) than in PCNSLs (mean ± SD = 22.5 ± 7.83) (p < 0.005). The AUC for aTBF (0.888) was higher than those for rTBF (0.810), ADCmin (0.768), and SUVmax (0.848), although their difference was not statistically significant. Conclusion ASL perfusion imaging is useful for differentiating PCNSLs from GBMs as well as DWI and FDG-PET. Arterial spin labeling (dpeaa)DE-He213 Glioblastoma (dpeaa)DE-He213 Primary CNS lymphoma (dpeaa)DE-He213 Yoshiura, Takashi aut Hiwatashi, Akio aut Togao, Osamu aut Yoshimoto, Koji aut Suzuki, Satoshi O. aut Abe, Koichiro aut Kikuchi, Kazufumi aut Maruoka, Yasuhiro aut Mizoguchi, Masahiro aut Iwaki, Toru aut Honda, Hiroshi aut Enthalten in Neuroradiology Berlin : Springer, 1970 55(2012), 2 vom: 09. Sept., Seite 135-143 (DE-627)254638430 (DE-600)1462953-7 1432-1920 nnns volume:55 year:2012 number:2 day:09 month:09 pages:135-143 https://dx.doi.org/10.1007/s00234-012-1089-6 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_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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 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_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_2472 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 55 2012 2 09 09 135-143 |
| allfields_unstemmed |
10.1007/s00234-012-1089-6 doi (DE-627)SPR002660253 (SPR)s00234-012-1089-6-e DE-627 ger DE-627 rakwb eng Yamashita, Koji verfasserin aut Differentiating primary CNS lymphoma from glioblastoma multiforme: assessment using arterial spin labeling, diffusion-weighted imaging, and 18F-fluorodeoxyglucose positron emission tomography 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2012 Introduction Our purpose was to evaluate the diagnostic performance of arterial spin labeling (ASL) perfusion imaging, diffusion-weighted imaging (DWI), and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) in differentiating primary central nervous system lymphomas (PCNSLs) from glioblastoma multiformes (GBMs). Methods Fifty-six patients including 19 with PCNSL and 37 with GBM were retrospectively studied. From the ASL data, an absolute tumor blood flow (aTBF) and a relative tumor blood flow (rTBF) were obtained within the enhancing portion of each tumor. In addition, the minimum apparent diffusion coefficient (ADCmin) and the maximum standard uptake value (SUVmax) were obtained from DWI and FDG-PET data, respectively. Each of the four parameters was compared between PCNSLs and GBMs using Kruskal–Wallis test. The performance in discriminating between PCNSLs and GBMs was evaluated using the receiver-operating characteristics analysis. Area-under-the-curve (AUC) values were compared among the four parameters using a nonparametric method. Results The aTBF, rTBF, and ADCmin were significantly higher in GBMs (mean aTBF ± SD = 91.6 ± 56.0 mL/100 g/min, mean rTBF ± SD = 2.61 ± 1.61, mean ADCmin ± SD = 0.78 ± 0.19 × $ 10^{−3} $ $ mm^{2} $/s) than in PCNSLs (mean aTBF ± SD = 37.3 ± 10.5 mL/100 g/min, mean rTBF ± SD = 1.24 ± 0.37, mean ADCmin ± SD = 0.61 ± 0.13 × $ 10^{−3} $ $ mm^{2} $/s) (p < 0.005, respectively). In addition, SUVmax was significantly lower in GBMs (mean ± SD = 13.1 ± 6.34) than in PCNSLs (mean ± SD = 22.5 ± 7.83) (p < 0.005). The AUC for aTBF (0.888) was higher than those for rTBF (0.810), ADCmin (0.768), and SUVmax (0.848), although their difference was not statistically significant. Conclusion ASL perfusion imaging is useful for differentiating PCNSLs from GBMs as well as DWI and FDG-PET. Arterial spin labeling (dpeaa)DE-He213 Glioblastoma (dpeaa)DE-He213 Primary CNS lymphoma (dpeaa)DE-He213 Yoshiura, Takashi aut Hiwatashi, Akio aut Togao, Osamu aut Yoshimoto, Koji aut Suzuki, Satoshi O. aut Abe, Koichiro aut Kikuchi, Kazufumi aut Maruoka, Yasuhiro aut Mizoguchi, Masahiro aut Iwaki, Toru aut Honda, Hiroshi aut Enthalten in Neuroradiology Berlin : Springer, 1970 55(2012), 2 vom: 09. Sept., Seite 135-143 (DE-627)254638430 (DE-600)1462953-7 1432-1920 nnns volume:55 year:2012 number:2 day:09 month:09 pages:135-143 https://dx.doi.org/10.1007/s00234-012-1089-6 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_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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 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_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_2472 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 55 2012 2 09 09 135-143 |
| allfieldsGer |
10.1007/s00234-012-1089-6 doi (DE-627)SPR002660253 (SPR)s00234-012-1089-6-e DE-627 ger DE-627 rakwb eng Yamashita, Koji verfasserin aut Differentiating primary CNS lymphoma from glioblastoma multiforme: assessment using arterial spin labeling, diffusion-weighted imaging, and 18F-fluorodeoxyglucose positron emission tomography 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2012 Introduction Our purpose was to evaluate the diagnostic performance of arterial spin labeling (ASL) perfusion imaging, diffusion-weighted imaging (DWI), and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) in differentiating primary central nervous system lymphomas (PCNSLs) from glioblastoma multiformes (GBMs). Methods Fifty-six patients including 19 with PCNSL and 37 with GBM were retrospectively studied. From the ASL data, an absolute tumor blood flow (aTBF) and a relative tumor blood flow (rTBF) were obtained within the enhancing portion of each tumor. In addition, the minimum apparent diffusion coefficient (ADCmin) and the maximum standard uptake value (SUVmax) were obtained from DWI and FDG-PET data, respectively. Each of the four parameters was compared between PCNSLs and GBMs using Kruskal–Wallis test. The performance in discriminating between PCNSLs and GBMs was evaluated using the receiver-operating characteristics analysis. Area-under-the-curve (AUC) values were compared among the four parameters using a nonparametric method. Results The aTBF, rTBF, and ADCmin were significantly higher in GBMs (mean aTBF ± SD = 91.6 ± 56.0 mL/100 g/min, mean rTBF ± SD = 2.61 ± 1.61, mean ADCmin ± SD = 0.78 ± 0.19 × $ 10^{−3} $ $ mm^{2} $/s) than in PCNSLs (mean aTBF ± SD = 37.3 ± 10.5 mL/100 g/min, mean rTBF ± SD = 1.24 ± 0.37, mean ADCmin ± SD = 0.61 ± 0.13 × $ 10^{−3} $ $ mm^{2} $/s) (p < 0.005, respectively). In addition, SUVmax was significantly lower in GBMs (mean ± SD = 13.1 ± 6.34) than in PCNSLs (mean ± SD = 22.5 ± 7.83) (p < 0.005). The AUC for aTBF (0.888) was higher than those for rTBF (0.810), ADCmin (0.768), and SUVmax (0.848), although their difference was not statistically significant. Conclusion ASL perfusion imaging is useful for differentiating PCNSLs from GBMs as well as DWI and FDG-PET. Arterial spin labeling (dpeaa)DE-He213 Glioblastoma (dpeaa)DE-He213 Primary CNS lymphoma (dpeaa)DE-He213 Yoshiura, Takashi aut Hiwatashi, Akio aut Togao, Osamu aut Yoshimoto, Koji aut Suzuki, Satoshi O. aut Abe, Koichiro aut Kikuchi, Kazufumi aut Maruoka, Yasuhiro aut Mizoguchi, Masahiro aut Iwaki, Toru aut Honda, Hiroshi aut Enthalten in Neuroradiology Berlin : Springer, 1970 55(2012), 2 vom: 09. Sept., Seite 135-143 (DE-627)254638430 (DE-600)1462953-7 1432-1920 nnns volume:55 year:2012 number:2 day:09 month:09 pages:135-143 https://dx.doi.org/10.1007/s00234-012-1089-6 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_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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 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_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_2472 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 55 2012 2 09 09 135-143 |
| allfieldsSound |
10.1007/s00234-012-1089-6 doi (DE-627)SPR002660253 (SPR)s00234-012-1089-6-e DE-627 ger DE-627 rakwb eng Yamashita, Koji verfasserin aut Differentiating primary CNS lymphoma from glioblastoma multiforme: assessment using arterial spin labeling, diffusion-weighted imaging, and 18F-fluorodeoxyglucose positron emission tomography 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2012 Introduction Our purpose was to evaluate the diagnostic performance of arterial spin labeling (ASL) perfusion imaging, diffusion-weighted imaging (DWI), and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) in differentiating primary central nervous system lymphomas (PCNSLs) from glioblastoma multiformes (GBMs). Methods Fifty-six patients including 19 with PCNSL and 37 with GBM were retrospectively studied. From the ASL data, an absolute tumor blood flow (aTBF) and a relative tumor blood flow (rTBF) were obtained within the enhancing portion of each tumor. In addition, the minimum apparent diffusion coefficient (ADCmin) and the maximum standard uptake value (SUVmax) were obtained from DWI and FDG-PET data, respectively. Each of the four parameters was compared between PCNSLs and GBMs using Kruskal–Wallis test. The performance in discriminating between PCNSLs and GBMs was evaluated using the receiver-operating characteristics analysis. Area-under-the-curve (AUC) values were compared among the four parameters using a nonparametric method. Results The aTBF, rTBF, and ADCmin were significantly higher in GBMs (mean aTBF ± SD = 91.6 ± 56.0 mL/100 g/min, mean rTBF ± SD = 2.61 ± 1.61, mean ADCmin ± SD = 0.78 ± 0.19 × $ 10^{−3} $ $ mm^{2} $/s) than in PCNSLs (mean aTBF ± SD = 37.3 ± 10.5 mL/100 g/min, mean rTBF ± SD = 1.24 ± 0.37, mean ADCmin ± SD = 0.61 ± 0.13 × $ 10^{−3} $ $ mm^{2} $/s) (p < 0.005, respectively). In addition, SUVmax was significantly lower in GBMs (mean ± SD = 13.1 ± 6.34) than in PCNSLs (mean ± SD = 22.5 ± 7.83) (p < 0.005). The AUC for aTBF (0.888) was higher than those for rTBF (0.810), ADCmin (0.768), and SUVmax (0.848), although their difference was not statistically significant. Conclusion ASL perfusion imaging is useful for differentiating PCNSLs from GBMs as well as DWI and FDG-PET. Arterial spin labeling (dpeaa)DE-He213 Glioblastoma (dpeaa)DE-He213 Primary CNS lymphoma (dpeaa)DE-He213 Yoshiura, Takashi aut Hiwatashi, Akio aut Togao, Osamu aut Yoshimoto, Koji aut Suzuki, Satoshi O. aut Abe, Koichiro aut Kikuchi, Kazufumi aut Maruoka, Yasuhiro aut Mizoguchi, Masahiro aut Iwaki, Toru aut Honda, Hiroshi aut Enthalten in Neuroradiology Berlin : Springer, 1970 55(2012), 2 vom: 09. Sept., Seite 135-143 (DE-627)254638430 (DE-600)1462953-7 1432-1920 nnns volume:55 year:2012 number:2 day:09 month:09 pages:135-143 https://dx.doi.org/10.1007/s00234-012-1089-6 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_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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 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_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_2472 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 55 2012 2 09 09 135-143 |
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Enthalten in Neuroradiology 55(2012), 2 vom: 09. Sept., Seite 135-143 volume:55 year:2012 number:2 day:09 month:09 pages:135-143 |
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Enthalten in Neuroradiology 55(2012), 2 vom: 09. Sept., Seite 135-143 volume:55 year:2012 number:2 day:09 month:09 pages:135-143 |
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Yamashita, Koji @@aut@@ Yoshiura, Takashi @@aut@@ Hiwatashi, Akio @@aut@@ Togao, Osamu @@aut@@ Yoshimoto, Koji @@aut@@ Suzuki, Satoshi O. @@aut@@ Abe, Koichiro @@aut@@ Kikuchi, Kazufumi @@aut@@ Maruoka, Yasuhiro @@aut@@ Mizoguchi, Masahiro @@aut@@ Iwaki, Toru @@aut@@ Honda, Hiroshi @@aut@@ |
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2012-09-09T00:00:00Z |
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Methods Fifty-six patients including 19 with PCNSL and 37 with GBM were retrospectively studied. From the ASL data, an absolute tumor blood flow (aTBF) and a relative tumor blood flow (rTBF) were obtained within the enhancing portion of each tumor. In addition, the minimum apparent diffusion coefficient (ADCmin) and the maximum standard uptake value (SUVmax) were obtained from DWI and FDG-PET data, respectively. Each of the four parameters was compared between PCNSLs and GBMs using Kruskal–Wallis test. The performance in discriminating between PCNSLs and GBMs was evaluated using the receiver-operating characteristics analysis. Area-under-the-curve (AUC) values were compared among the four parameters using a nonparametric method. Results The aTBF, rTBF, and ADCmin were significantly higher in GBMs (mean aTBF ± SD = 91.6 ± 56.0 mL/100 g/min, mean rTBF ± SD = 2.61 ± 1.61, mean ADCmin ± SD = 0.78 ± 0.19 × $ 10^{−3} $ $ mm^{2} $/s) than in PCNSLs (mean aTBF ± SD = 37.3 ± 10.5 mL/100 g/min, mean rTBF ± SD = 1.24 ± 0.37, mean ADCmin ± SD = 0.61 ± 0.13 × $ 10^{−3} $ $ mm^{2} $/s) (p < 0.005, respectively). In addition, SUVmax was significantly lower in GBMs (mean ± SD = 13.1 ± 6.34) than in PCNSLs (mean ± SD = 22.5 ± 7.83) (p < 0.005). The AUC for aTBF (0.888) was higher than those for rTBF (0.810), ADCmin (0.768), and SUVmax (0.848), although their difference was not statistically significant. 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Yamashita, Koji |
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Yamashita, Koji misc Arterial spin labeling misc Glioblastoma misc Primary CNS lymphoma Differentiating primary CNS lymphoma from glioblastoma multiforme: assessment using arterial spin labeling, diffusion-weighted imaging, and 18F-fluorodeoxyglucose positron emission tomography |
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Differentiating primary CNS lymphoma from glioblastoma multiforme: assessment using arterial spin labeling, diffusion-weighted imaging, and 18F-fluorodeoxyglucose positron emission tomography Arterial spin labeling (dpeaa)DE-He213 Glioblastoma (dpeaa)DE-He213 Primary CNS lymphoma (dpeaa)DE-He213 |
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Differentiating primary CNS lymphoma from glioblastoma multiforme: assessment using arterial spin labeling, diffusion-weighted imaging, and 18F-fluorodeoxyglucose positron emission tomography |
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Differentiating primary CNS lymphoma from glioblastoma multiforme: assessment using arterial spin labeling, diffusion-weighted imaging, and 18F-fluorodeoxyglucose positron emission tomography |
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Yamashita, Koji Yoshiura, Takashi Hiwatashi, Akio Togao, Osamu Yoshimoto, Koji Suzuki, Satoshi O. Abe, Koichiro Kikuchi, Kazufumi Maruoka, Yasuhiro Mizoguchi, Masahiro Iwaki, Toru Honda, Hiroshi |
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10.1007/s00234-012-1089-6 |
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differentiating primary cns lymphoma from glioblastoma multiforme: assessment using arterial spin labeling, diffusion-weighted imaging, and 18f-fluorodeoxyglucose positron emission tomography |
| title_auth |
Differentiating primary CNS lymphoma from glioblastoma multiforme: assessment using arterial spin labeling, diffusion-weighted imaging, and 18F-fluorodeoxyglucose positron emission tomography |
| abstract |
Introduction Our purpose was to evaluate the diagnostic performance of arterial spin labeling (ASL) perfusion imaging, diffusion-weighted imaging (DWI), and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) in differentiating primary central nervous system lymphomas (PCNSLs) from glioblastoma multiformes (GBMs). Methods Fifty-six patients including 19 with PCNSL and 37 with GBM were retrospectively studied. From the ASL data, an absolute tumor blood flow (aTBF) and a relative tumor blood flow (rTBF) were obtained within the enhancing portion of each tumor. In addition, the minimum apparent diffusion coefficient (ADCmin) and the maximum standard uptake value (SUVmax) were obtained from DWI and FDG-PET data, respectively. Each of the four parameters was compared between PCNSLs and GBMs using Kruskal–Wallis test. The performance in discriminating between PCNSLs and GBMs was evaluated using the receiver-operating characteristics analysis. Area-under-the-curve (AUC) values were compared among the four parameters using a nonparametric method. Results The aTBF, rTBF, and ADCmin were significantly higher in GBMs (mean aTBF ± SD = 91.6 ± 56.0 mL/100 g/min, mean rTBF ± SD = 2.61 ± 1.61, mean ADCmin ± SD = 0.78 ± 0.19 × $ 10^{−3} $ $ mm^{2} $/s) than in PCNSLs (mean aTBF ± SD = 37.3 ± 10.5 mL/100 g/min, mean rTBF ± SD = 1.24 ± 0.37, mean ADCmin ± SD = 0.61 ± 0.13 × $ 10^{−3} $ $ mm^{2} $/s) (p < 0.005, respectively). In addition, SUVmax was significantly lower in GBMs (mean ± SD = 13.1 ± 6.34) than in PCNSLs (mean ± SD = 22.5 ± 7.83) (p < 0.005). The AUC for aTBF (0.888) was higher than those for rTBF (0.810), ADCmin (0.768), and SUVmax (0.848), although their difference was not statistically significant. Conclusion ASL perfusion imaging is useful for differentiating PCNSLs from GBMs as well as DWI and FDG-PET. © Springer-Verlag 2012 |
| abstractGer |
Introduction Our purpose was to evaluate the diagnostic performance of arterial spin labeling (ASL) perfusion imaging, diffusion-weighted imaging (DWI), and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) in differentiating primary central nervous system lymphomas (PCNSLs) from glioblastoma multiformes (GBMs). Methods Fifty-six patients including 19 with PCNSL and 37 with GBM were retrospectively studied. From the ASL data, an absolute tumor blood flow (aTBF) and a relative tumor blood flow (rTBF) were obtained within the enhancing portion of each tumor. In addition, the minimum apparent diffusion coefficient (ADCmin) and the maximum standard uptake value (SUVmax) were obtained from DWI and FDG-PET data, respectively. Each of the four parameters was compared between PCNSLs and GBMs using Kruskal–Wallis test. The performance in discriminating between PCNSLs and GBMs was evaluated using the receiver-operating characteristics analysis. Area-under-the-curve (AUC) values were compared among the four parameters using a nonparametric method. Results The aTBF, rTBF, and ADCmin were significantly higher in GBMs (mean aTBF ± SD = 91.6 ± 56.0 mL/100 g/min, mean rTBF ± SD = 2.61 ± 1.61, mean ADCmin ± SD = 0.78 ± 0.19 × $ 10^{−3} $ $ mm^{2} $/s) than in PCNSLs (mean aTBF ± SD = 37.3 ± 10.5 mL/100 g/min, mean rTBF ± SD = 1.24 ± 0.37, mean ADCmin ± SD = 0.61 ± 0.13 × $ 10^{−3} $ $ mm^{2} $/s) (p < 0.005, respectively). In addition, SUVmax was significantly lower in GBMs (mean ± SD = 13.1 ± 6.34) than in PCNSLs (mean ± SD = 22.5 ± 7.83) (p < 0.005). The AUC for aTBF (0.888) was higher than those for rTBF (0.810), ADCmin (0.768), and SUVmax (0.848), although their difference was not statistically significant. Conclusion ASL perfusion imaging is useful for differentiating PCNSLs from GBMs as well as DWI and FDG-PET. © Springer-Verlag 2012 |
| abstract_unstemmed |
Introduction Our purpose was to evaluate the diagnostic performance of arterial spin labeling (ASL) perfusion imaging, diffusion-weighted imaging (DWI), and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) in differentiating primary central nervous system lymphomas (PCNSLs) from glioblastoma multiformes (GBMs). Methods Fifty-six patients including 19 with PCNSL and 37 with GBM were retrospectively studied. From the ASL data, an absolute tumor blood flow (aTBF) and a relative tumor blood flow (rTBF) were obtained within the enhancing portion of each tumor. In addition, the minimum apparent diffusion coefficient (ADCmin) and the maximum standard uptake value (SUVmax) were obtained from DWI and FDG-PET data, respectively. Each of the four parameters was compared between PCNSLs and GBMs using Kruskal–Wallis test. The performance in discriminating between PCNSLs and GBMs was evaluated using the receiver-operating characteristics analysis. Area-under-the-curve (AUC) values were compared among the four parameters using a nonparametric method. Results The aTBF, rTBF, and ADCmin were significantly higher in GBMs (mean aTBF ± SD = 91.6 ± 56.0 mL/100 g/min, mean rTBF ± SD = 2.61 ± 1.61, mean ADCmin ± SD = 0.78 ± 0.19 × $ 10^{−3} $ $ mm^{2} $/s) than in PCNSLs (mean aTBF ± SD = 37.3 ± 10.5 mL/100 g/min, mean rTBF ± SD = 1.24 ± 0.37, mean ADCmin ± SD = 0.61 ± 0.13 × $ 10^{−3} $ $ mm^{2} $/s) (p < 0.005, respectively). In addition, SUVmax was significantly lower in GBMs (mean ± SD = 13.1 ± 6.34) than in PCNSLs (mean ± SD = 22.5 ± 7.83) (p < 0.005). The AUC for aTBF (0.888) was higher than those for rTBF (0.810), ADCmin (0.768), and SUVmax (0.848), although their difference was not statistically significant. Conclusion ASL perfusion imaging is useful for differentiating PCNSLs from GBMs as well as DWI and FDG-PET. © Springer-Verlag 2012 |
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Differentiating primary CNS lymphoma from glioblastoma multiforme: assessment using arterial spin labeling, diffusion-weighted imaging, and 18F-fluorodeoxyglucose positron emission tomography |
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https://dx.doi.org/10.1007/s00234-012-1089-6 |
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Yoshiura, Takashi Hiwatashi, Akio Togao, Osamu Yoshimoto, Koji Suzuki, Satoshi O. Abe, Koichiro Kikuchi, Kazufumi Maruoka, Yasuhiro Mizoguchi, Masahiro Iwaki, Toru Honda, Hiroshi |
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Yoshiura, Takashi Hiwatashi, Akio Togao, Osamu Yoshimoto, Koji Suzuki, Satoshi O. Abe, Koichiro Kikuchi, Kazufumi Maruoka, Yasuhiro Mizoguchi, Masahiro Iwaki, Toru Honda, Hiroshi |
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10.1007/s00234-012-1089-6 |
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2024-07-03T14:22:15.592Z |
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Methods Fifty-six patients including 19 with PCNSL and 37 with GBM were retrospectively studied. From the ASL data, an absolute tumor blood flow (aTBF) and a relative tumor blood flow (rTBF) were obtained within the enhancing portion of each tumor. In addition, the minimum apparent diffusion coefficient (ADCmin) and the maximum standard uptake value (SUVmax) were obtained from DWI and FDG-PET data, respectively. Each of the four parameters was compared between PCNSLs and GBMs using Kruskal–Wallis test. The performance in discriminating between PCNSLs and GBMs was evaluated using the receiver-operating characteristics analysis. Area-under-the-curve (AUC) values were compared among the four parameters using a nonparametric method. Results The aTBF, rTBF, and ADCmin were significantly higher in GBMs (mean aTBF ± SD = 91.6 ± 56.0 mL/100 g/min, mean rTBF ± SD = 2.61 ± 1.61, mean ADCmin ± SD = 0.78 ± 0.19 × $ 10^{−3} $ $ mm^{2} $/s) than in PCNSLs (mean aTBF ± SD = 37.3 ± 10.5 mL/100 g/min, mean rTBF ± SD = 1.24 ± 0.37, mean ADCmin ± SD = 0.61 ± 0.13 × $ 10^{−3} $ $ mm^{2} $/s) (p < 0.005, respectively). In addition, SUVmax was significantly lower in GBMs (mean ± SD = 13.1 ± 6.34) than in PCNSLs (mean ± SD = 22.5 ± 7.83) (p < 0.005). The AUC for aTBF (0.888) was higher than those for rTBF (0.810), ADCmin (0.768), and SUVmax (0.848), although their difference was not statistically significant. 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| score |
7.3996477 |