Prediction of H3 K27M-mutant in midline gliomas by magnetic resonance imaging: a systematic review and meta-analysis
Purpose To summarize the predictive value of MRI for H3 K27M-mutant in midline gliomas using meta-analysis. Methods Systematic electronic searches of the PubMed, Embase, ISI Web of Science, and Cochrane Library up to Jun 31, 2021, were conducted by two experienced neuroradiologists with the keywords...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Hua, Tiantian [verfasserIn] |
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E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Neuroradiology - Berlin : Springer, 1970, 64(2022), 7 vom: 13. Apr., Seite 1311-1319 |
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| Übergeordnetes Werk: |
volume:64 ; year:2022 ; number:7 ; day:13 ; month:04 ; pages:1311-1319 |
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| DOI / URN: |
10.1007/s00234-022-02947-4 |
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| Katalog-ID: |
SPR047221976 |
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| 520 | |a Purpose To summarize the predictive value of MRI for H3 K27M-mutant in midline gliomas using meta-analysis. Methods Systematic electronic searches of the PubMed, Embase, ISI Web of Science, and Cochrane Library up to Jun 31, 2021, were conducted by two experienced neuroradiologists with the keywords of “MRI,” “Glioma,” and “H3 K27M.” The hierarchical summary receiver-operating characteristic (HSROC) model was used to calculate the pooled sensitivity, specificity, positive likelihood ratio (LR +), negative likelihood ratio (LR −), and diagnostic odds ratio (DOR). Coupled forest plots were used to evaluate the heterogeneity of the included studies. Results Of seven original studies with a total of 593 patients, 240 glioma patients were included, with 45.5–70.6% H3 K27M-mutant gliomas. Using MRI, a pooled sensitivity of 0.78 (95% CI, 0.66–0.87), specificity of 0.85 (95% CI, 0.76–0.91), LR + of 5.07 (95% CI, 3.19–8.08), LR − of 0.26 (95% CI, 0.16–0.42), and DOR of 19.80 (95% CI, 9.28–42.28) were achieved for H3 K27M-mutant prediction. Significant heterogeneity was observed among the studies in terms of sensitivity (Q = 16.83, df = 7, p = 0.02; I2 = 58.40 [95% CI, 25.83–90.97]), LR − (Q = 16.61, df = 7, p = 0.02; I2 = 57.87 [95% CI, 24.81–90.93]), and DOR (Q = 14.05, df = 7, p = 0.05; I2 = 50.18 [95% CI, 10.06–90.31]). Conclusions This meta-analysis demonstrated a clinical value of MRI to predict H3 K27M-mutant in midline gliomas with a pooled sensitivity of 0.78 and specificity of 0.85. | ||
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| 650 | 4 | |a Midline gliomas |7 (dpeaa)DE-He213 | |
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10.1007/s00234-022-02947-4 doi (DE-627)SPR047221976 (SPR)s00234-022-02947-4-e DE-627 ger DE-627 rakwb eng Hua, Tiantian verfasserin aut Prediction of H3 K27M-mutant in midline gliomas by magnetic resonance imaging: a systematic review and meta-analysis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Purpose To summarize the predictive value of MRI for H3 K27M-mutant in midline gliomas using meta-analysis. Methods Systematic electronic searches of the PubMed, Embase, ISI Web of Science, and Cochrane Library up to Jun 31, 2021, were conducted by two experienced neuroradiologists with the keywords of “MRI,” “Glioma,” and “H3 K27M.” The hierarchical summary receiver-operating characteristic (HSROC) model was used to calculate the pooled sensitivity, specificity, positive likelihood ratio (LR +), negative likelihood ratio (LR −), and diagnostic odds ratio (DOR). Coupled forest plots were used to evaluate the heterogeneity of the included studies. Results Of seven original studies with a total of 593 patients, 240 glioma patients were included, with 45.5–70.6% H3 K27M-mutant gliomas. Using MRI, a pooled sensitivity of 0.78 (95% CI, 0.66–0.87), specificity of 0.85 (95% CI, 0.76–0.91), LR + of 5.07 (95% CI, 3.19–8.08), LR − of 0.26 (95% CI, 0.16–0.42), and DOR of 19.80 (95% CI, 9.28–42.28) were achieved for H3 K27M-mutant prediction. Significant heterogeneity was observed among the studies in terms of sensitivity (Q = 16.83, df = 7, p = 0.02; I2 = 58.40 [95% CI, 25.83–90.97]), LR − (Q = 16.61, df = 7, p = 0.02; I2 = 57.87 [95% CI, 24.81–90.93]), and DOR (Q = 14.05, df = 7, p = 0.05; I2 = 50.18 [95% CI, 10.06–90.31]). Conclusions This meta-analysis demonstrated a clinical value of MRI to predict H3 K27M-mutant in midline gliomas with a pooled sensitivity of 0.78 and specificity of 0.85. H3 K27M-mutant (dpeaa)DE-He213 Midline gliomas (dpeaa)DE-He213 Magnetic resonance imaging (dpeaa)DE-He213 Meta-analysis (dpeaa)DE-He213 Zhuo, Zhizheng aut Duan, Yunyun aut Li, Xindi aut Gu, Hongqiu aut Haller, Sven aut Zhang, Peng aut Liu, Xing aut Zhang, Liwei aut Liu, Yaou (orcid)0000-0002-9930-0331 aut Enthalten in Neuroradiology Berlin : Springer, 1970 64(2022), 7 vom: 13. Apr., Seite 1311-1319 (DE-627)254638430 (DE-600)1462953-7 1432-1920 nnns volume:64 year:2022 number:7 day:13 month:04 pages:1311-1319 https://dx.doi.org/10.1007/s00234-022-02947-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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 64 2022 7 13 04 1311-1319 |
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10.1007/s00234-022-02947-4 doi (DE-627)SPR047221976 (SPR)s00234-022-02947-4-e DE-627 ger DE-627 rakwb eng Hua, Tiantian verfasserin aut Prediction of H3 K27M-mutant in midline gliomas by magnetic resonance imaging: a systematic review and meta-analysis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Purpose To summarize the predictive value of MRI for H3 K27M-mutant in midline gliomas using meta-analysis. Methods Systematic electronic searches of the PubMed, Embase, ISI Web of Science, and Cochrane Library up to Jun 31, 2021, were conducted by two experienced neuroradiologists with the keywords of “MRI,” “Glioma,” and “H3 K27M.” The hierarchical summary receiver-operating characteristic (HSROC) model was used to calculate the pooled sensitivity, specificity, positive likelihood ratio (LR +), negative likelihood ratio (LR −), and diagnostic odds ratio (DOR). Coupled forest plots were used to evaluate the heterogeneity of the included studies. Results Of seven original studies with a total of 593 patients, 240 glioma patients were included, with 45.5–70.6% H3 K27M-mutant gliomas. Using MRI, a pooled sensitivity of 0.78 (95% CI, 0.66–0.87), specificity of 0.85 (95% CI, 0.76–0.91), LR + of 5.07 (95% CI, 3.19–8.08), LR − of 0.26 (95% CI, 0.16–0.42), and DOR of 19.80 (95% CI, 9.28–42.28) were achieved for H3 K27M-mutant prediction. Significant heterogeneity was observed among the studies in terms of sensitivity (Q = 16.83, df = 7, p = 0.02; I2 = 58.40 [95% CI, 25.83–90.97]), LR − (Q = 16.61, df = 7, p = 0.02; I2 = 57.87 [95% CI, 24.81–90.93]), and DOR (Q = 14.05, df = 7, p = 0.05; I2 = 50.18 [95% CI, 10.06–90.31]). Conclusions This meta-analysis demonstrated a clinical value of MRI to predict H3 K27M-mutant in midline gliomas with a pooled sensitivity of 0.78 and specificity of 0.85. H3 K27M-mutant (dpeaa)DE-He213 Midline gliomas (dpeaa)DE-He213 Magnetic resonance imaging (dpeaa)DE-He213 Meta-analysis (dpeaa)DE-He213 Zhuo, Zhizheng aut Duan, Yunyun aut Li, Xindi aut Gu, Hongqiu aut Haller, Sven aut Zhang, Peng aut Liu, Xing aut Zhang, Liwei aut Liu, Yaou (orcid)0000-0002-9930-0331 aut Enthalten in Neuroradiology Berlin : Springer, 1970 64(2022), 7 vom: 13. Apr., Seite 1311-1319 (DE-627)254638430 (DE-600)1462953-7 1432-1920 nnns volume:64 year:2022 number:7 day:13 month:04 pages:1311-1319 https://dx.doi.org/10.1007/s00234-022-02947-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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 64 2022 7 13 04 1311-1319 |
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10.1007/s00234-022-02947-4 doi (DE-627)SPR047221976 (SPR)s00234-022-02947-4-e DE-627 ger DE-627 rakwb eng Hua, Tiantian verfasserin aut Prediction of H3 K27M-mutant in midline gliomas by magnetic resonance imaging: a systematic review and meta-analysis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Purpose To summarize the predictive value of MRI for H3 K27M-mutant in midline gliomas using meta-analysis. Methods Systematic electronic searches of the PubMed, Embase, ISI Web of Science, and Cochrane Library up to Jun 31, 2021, were conducted by two experienced neuroradiologists with the keywords of “MRI,” “Glioma,” and “H3 K27M.” The hierarchical summary receiver-operating characteristic (HSROC) model was used to calculate the pooled sensitivity, specificity, positive likelihood ratio (LR +), negative likelihood ratio (LR −), and diagnostic odds ratio (DOR). Coupled forest plots were used to evaluate the heterogeneity of the included studies. Results Of seven original studies with a total of 593 patients, 240 glioma patients were included, with 45.5–70.6% H3 K27M-mutant gliomas. Using MRI, a pooled sensitivity of 0.78 (95% CI, 0.66–0.87), specificity of 0.85 (95% CI, 0.76–0.91), LR + of 5.07 (95% CI, 3.19–8.08), LR − of 0.26 (95% CI, 0.16–0.42), and DOR of 19.80 (95% CI, 9.28–42.28) were achieved for H3 K27M-mutant prediction. Significant heterogeneity was observed among the studies in terms of sensitivity (Q = 16.83, df = 7, p = 0.02; I2 = 58.40 [95% CI, 25.83–90.97]), LR − (Q = 16.61, df = 7, p = 0.02; I2 = 57.87 [95% CI, 24.81–90.93]), and DOR (Q = 14.05, df = 7, p = 0.05; I2 = 50.18 [95% CI, 10.06–90.31]). Conclusions This meta-analysis demonstrated a clinical value of MRI to predict H3 K27M-mutant in midline gliomas with a pooled sensitivity of 0.78 and specificity of 0.85. H3 K27M-mutant (dpeaa)DE-He213 Midline gliomas (dpeaa)DE-He213 Magnetic resonance imaging (dpeaa)DE-He213 Meta-analysis (dpeaa)DE-He213 Zhuo, Zhizheng aut Duan, Yunyun aut Li, Xindi aut Gu, Hongqiu aut Haller, Sven aut Zhang, Peng aut Liu, Xing aut Zhang, Liwei aut Liu, Yaou (orcid)0000-0002-9930-0331 aut Enthalten in Neuroradiology Berlin : Springer, 1970 64(2022), 7 vom: 13. Apr., Seite 1311-1319 (DE-627)254638430 (DE-600)1462953-7 1432-1920 nnns volume:64 year:2022 number:7 day:13 month:04 pages:1311-1319 https://dx.doi.org/10.1007/s00234-022-02947-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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 64 2022 7 13 04 1311-1319 |
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10.1007/s00234-022-02947-4 doi (DE-627)SPR047221976 (SPR)s00234-022-02947-4-e DE-627 ger DE-627 rakwb eng Hua, Tiantian verfasserin aut Prediction of H3 K27M-mutant in midline gliomas by magnetic resonance imaging: a systematic review and meta-analysis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Purpose To summarize the predictive value of MRI for H3 K27M-mutant in midline gliomas using meta-analysis. Methods Systematic electronic searches of the PubMed, Embase, ISI Web of Science, and Cochrane Library up to Jun 31, 2021, were conducted by two experienced neuroradiologists with the keywords of “MRI,” “Glioma,” and “H3 K27M.” The hierarchical summary receiver-operating characteristic (HSROC) model was used to calculate the pooled sensitivity, specificity, positive likelihood ratio (LR +), negative likelihood ratio (LR −), and diagnostic odds ratio (DOR). Coupled forest plots were used to evaluate the heterogeneity of the included studies. Results Of seven original studies with a total of 593 patients, 240 glioma patients were included, with 45.5–70.6% H3 K27M-mutant gliomas. Using MRI, a pooled sensitivity of 0.78 (95% CI, 0.66–0.87), specificity of 0.85 (95% CI, 0.76–0.91), LR + of 5.07 (95% CI, 3.19–8.08), LR − of 0.26 (95% CI, 0.16–0.42), and DOR of 19.80 (95% CI, 9.28–42.28) were achieved for H3 K27M-mutant prediction. Significant heterogeneity was observed among the studies in terms of sensitivity (Q = 16.83, df = 7, p = 0.02; I2 = 58.40 [95% CI, 25.83–90.97]), LR − (Q = 16.61, df = 7, p = 0.02; I2 = 57.87 [95% CI, 24.81–90.93]), and DOR (Q = 14.05, df = 7, p = 0.05; I2 = 50.18 [95% CI, 10.06–90.31]). Conclusions This meta-analysis demonstrated a clinical value of MRI to predict H3 K27M-mutant in midline gliomas with a pooled sensitivity of 0.78 and specificity of 0.85. H3 K27M-mutant (dpeaa)DE-He213 Midline gliomas (dpeaa)DE-He213 Magnetic resonance imaging (dpeaa)DE-He213 Meta-analysis (dpeaa)DE-He213 Zhuo, Zhizheng aut Duan, Yunyun aut Li, Xindi aut Gu, Hongqiu aut Haller, Sven aut Zhang, Peng aut Liu, Xing aut Zhang, Liwei aut Liu, Yaou (orcid)0000-0002-9930-0331 aut Enthalten in Neuroradiology Berlin : Springer, 1970 64(2022), 7 vom: 13. Apr., Seite 1311-1319 (DE-627)254638430 (DE-600)1462953-7 1432-1920 nnns volume:64 year:2022 number:7 day:13 month:04 pages:1311-1319 https://dx.doi.org/10.1007/s00234-022-02947-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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 64 2022 7 13 04 1311-1319 |
| allfieldsSound |
10.1007/s00234-022-02947-4 doi (DE-627)SPR047221976 (SPR)s00234-022-02947-4-e DE-627 ger DE-627 rakwb eng Hua, Tiantian verfasserin aut Prediction of H3 K27M-mutant in midline gliomas by magnetic resonance imaging: a systematic review and meta-analysis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Purpose To summarize the predictive value of MRI for H3 K27M-mutant in midline gliomas using meta-analysis. Methods Systematic electronic searches of the PubMed, Embase, ISI Web of Science, and Cochrane Library up to Jun 31, 2021, were conducted by two experienced neuroradiologists with the keywords of “MRI,” “Glioma,” and “H3 K27M.” The hierarchical summary receiver-operating characteristic (HSROC) model was used to calculate the pooled sensitivity, specificity, positive likelihood ratio (LR +), negative likelihood ratio (LR −), and diagnostic odds ratio (DOR). Coupled forest plots were used to evaluate the heterogeneity of the included studies. Results Of seven original studies with a total of 593 patients, 240 glioma patients were included, with 45.5–70.6% H3 K27M-mutant gliomas. Using MRI, a pooled sensitivity of 0.78 (95% CI, 0.66–0.87), specificity of 0.85 (95% CI, 0.76–0.91), LR + of 5.07 (95% CI, 3.19–8.08), LR − of 0.26 (95% CI, 0.16–0.42), and DOR of 19.80 (95% CI, 9.28–42.28) were achieved for H3 K27M-mutant prediction. Significant heterogeneity was observed among the studies in terms of sensitivity (Q = 16.83, df = 7, p = 0.02; I2 = 58.40 [95% CI, 25.83–90.97]), LR − (Q = 16.61, df = 7, p = 0.02; I2 = 57.87 [95% CI, 24.81–90.93]), and DOR (Q = 14.05, df = 7, p = 0.05; I2 = 50.18 [95% CI, 10.06–90.31]). Conclusions This meta-analysis demonstrated a clinical value of MRI to predict H3 K27M-mutant in midline gliomas with a pooled sensitivity of 0.78 and specificity of 0.85. H3 K27M-mutant (dpeaa)DE-He213 Midline gliomas (dpeaa)DE-He213 Magnetic resonance imaging (dpeaa)DE-He213 Meta-analysis (dpeaa)DE-He213 Zhuo, Zhizheng aut Duan, Yunyun aut Li, Xindi aut Gu, Hongqiu aut Haller, Sven aut Zhang, Peng aut Liu, Xing aut Zhang, Liwei aut Liu, Yaou (orcid)0000-0002-9930-0331 aut Enthalten in Neuroradiology Berlin : Springer, 1970 64(2022), 7 vom: 13. Apr., Seite 1311-1319 (DE-627)254638430 (DE-600)1462953-7 1432-1920 nnns volume:64 year:2022 number:7 day:13 month:04 pages:1311-1319 https://dx.doi.org/10.1007/s00234-022-02947-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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 64 2022 7 13 04 1311-1319 |
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Enthalten in Neuroradiology 64(2022), 7 vom: 13. Apr., Seite 1311-1319 volume:64 year:2022 number:7 day:13 month:04 pages:1311-1319 |
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H3 K27M-mutant Midline gliomas Magnetic resonance imaging Meta-analysis |
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Hua, Tiantian @@aut@@ Zhuo, Zhizheng @@aut@@ Duan, Yunyun @@aut@@ Li, Xindi @@aut@@ Gu, Hongqiu @@aut@@ Haller, Sven @@aut@@ Zhang, Peng @@aut@@ Liu, Xing @@aut@@ Zhang, Liwei @@aut@@ Liu, Yaou @@aut@@ |
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Methods Systematic electronic searches of the PubMed, Embase, ISI Web of Science, and Cochrane Library up to Jun 31, 2021, were conducted by two experienced neuroradiologists with the keywords of “MRI,” “Glioma,” and “H3 K27M.” The hierarchical summary receiver-operating characteristic (HSROC) model was used to calculate the pooled sensitivity, specificity, positive likelihood ratio (LR +), negative likelihood ratio (LR −), and diagnostic odds ratio (DOR). Coupled forest plots were used to evaluate the heterogeneity of the included studies. Results Of seven original studies with a total of 593 patients, 240 glioma patients were included, with 45.5–70.6% H3 K27M-mutant gliomas. Using MRI, a pooled sensitivity of 0.78 (95% CI, 0.66–0.87), specificity of 0.85 (95% CI, 0.76–0.91), LR + of 5.07 (95% CI, 3.19–8.08), LR − of 0.26 (95% CI, 0.16–0.42), and DOR of 19.80 (95% CI, 9.28–42.28) were achieved for H3 K27M-mutant prediction. Significant heterogeneity was observed among the studies in terms of sensitivity (Q = 16.83, df = 7, p = 0.02; I2 = 58.40 [95% CI, 25.83–90.97]), LR − (Q = 16.61, df = 7, p = 0.02; I2 = 57.87 [95% CI, 24.81–90.93]), and DOR (Q = 14.05, df = 7, p = 0.05; I2 = 50.18 [95% CI, 10.06–90.31]). Conclusions This meta-analysis demonstrated a clinical value of MRI to predict H3 K27M-mutant in midline gliomas with a pooled sensitivity of 0.78 and specificity of 0.85.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">H3 K27M-mutant</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Midline gliomas</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Magnetic resonance imaging</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Meta-analysis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhuo, Zhizheng</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Duan, Yunyun</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Xindi</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gu, Hongqiu</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Haller, Sven</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Peng</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Xing</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Liwei</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Yaou</subfield><subfield code="0">(orcid)0000-0002-9930-0331</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Neuroradiology</subfield><subfield code="d">Berlin : Springer, 1970</subfield><subfield code="g">64(2022), 7 vom: 13. 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Hua, Tiantian |
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Prediction of H3 K27M-mutant in midline gliomas by magnetic resonance imaging: a systematic review and meta-analysis H3 K27M-mutant (dpeaa)DE-He213 Midline gliomas (dpeaa)DE-He213 Magnetic resonance imaging (dpeaa)DE-He213 Meta-analysis (dpeaa)DE-He213 |
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prediction of h3 k27m-mutant in midline gliomas by magnetic resonance imaging: a systematic review and meta-analysis |
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Prediction of H3 K27M-mutant in midline gliomas by magnetic resonance imaging: a systematic review and meta-analysis |
| abstract |
Purpose To summarize the predictive value of MRI for H3 K27M-mutant in midline gliomas using meta-analysis. Methods Systematic electronic searches of the PubMed, Embase, ISI Web of Science, and Cochrane Library up to Jun 31, 2021, were conducted by two experienced neuroradiologists with the keywords of “MRI,” “Glioma,” and “H3 K27M.” The hierarchical summary receiver-operating characteristic (HSROC) model was used to calculate the pooled sensitivity, specificity, positive likelihood ratio (LR +), negative likelihood ratio (LR −), and diagnostic odds ratio (DOR). Coupled forest plots were used to evaluate the heterogeneity of the included studies. Results Of seven original studies with a total of 593 patients, 240 glioma patients were included, with 45.5–70.6% H3 K27M-mutant gliomas. Using MRI, a pooled sensitivity of 0.78 (95% CI, 0.66–0.87), specificity of 0.85 (95% CI, 0.76–0.91), LR + of 5.07 (95% CI, 3.19–8.08), LR − of 0.26 (95% CI, 0.16–0.42), and DOR of 19.80 (95% CI, 9.28–42.28) were achieved for H3 K27M-mutant prediction. Significant heterogeneity was observed among the studies in terms of sensitivity (Q = 16.83, df = 7, p = 0.02; I2 = 58.40 [95% CI, 25.83–90.97]), LR − (Q = 16.61, df = 7, p = 0.02; I2 = 57.87 [95% CI, 24.81–90.93]), and DOR (Q = 14.05, df = 7, p = 0.05; I2 = 50.18 [95% CI, 10.06–90.31]). Conclusions This meta-analysis demonstrated a clinical value of MRI to predict H3 K27M-mutant in midline gliomas with a pooled sensitivity of 0.78 and specificity of 0.85. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
| abstractGer |
Purpose To summarize the predictive value of MRI for H3 K27M-mutant in midline gliomas using meta-analysis. Methods Systematic electronic searches of the PubMed, Embase, ISI Web of Science, and Cochrane Library up to Jun 31, 2021, were conducted by two experienced neuroradiologists with the keywords of “MRI,” “Glioma,” and “H3 K27M.” The hierarchical summary receiver-operating characteristic (HSROC) model was used to calculate the pooled sensitivity, specificity, positive likelihood ratio (LR +), negative likelihood ratio (LR −), and diagnostic odds ratio (DOR). Coupled forest plots were used to evaluate the heterogeneity of the included studies. Results Of seven original studies with a total of 593 patients, 240 glioma patients were included, with 45.5–70.6% H3 K27M-mutant gliomas. Using MRI, a pooled sensitivity of 0.78 (95% CI, 0.66–0.87), specificity of 0.85 (95% CI, 0.76–0.91), LR + of 5.07 (95% CI, 3.19–8.08), LR − of 0.26 (95% CI, 0.16–0.42), and DOR of 19.80 (95% CI, 9.28–42.28) were achieved for H3 K27M-mutant prediction. Significant heterogeneity was observed among the studies in terms of sensitivity (Q = 16.83, df = 7, p = 0.02; I2 = 58.40 [95% CI, 25.83–90.97]), LR − (Q = 16.61, df = 7, p = 0.02; I2 = 57.87 [95% CI, 24.81–90.93]), and DOR (Q = 14.05, df = 7, p = 0.05; I2 = 50.18 [95% CI, 10.06–90.31]). Conclusions This meta-analysis demonstrated a clinical value of MRI to predict H3 K27M-mutant in midline gliomas with a pooled sensitivity of 0.78 and specificity of 0.85. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
| abstract_unstemmed |
Purpose To summarize the predictive value of MRI for H3 K27M-mutant in midline gliomas using meta-analysis. Methods Systematic electronic searches of the PubMed, Embase, ISI Web of Science, and Cochrane Library up to Jun 31, 2021, were conducted by two experienced neuroradiologists with the keywords of “MRI,” “Glioma,” and “H3 K27M.” The hierarchical summary receiver-operating characteristic (HSROC) model was used to calculate the pooled sensitivity, specificity, positive likelihood ratio (LR +), negative likelihood ratio (LR −), and diagnostic odds ratio (DOR). Coupled forest plots were used to evaluate the heterogeneity of the included studies. Results Of seven original studies with a total of 593 patients, 240 glioma patients were included, with 45.5–70.6% H3 K27M-mutant gliomas. Using MRI, a pooled sensitivity of 0.78 (95% CI, 0.66–0.87), specificity of 0.85 (95% CI, 0.76–0.91), LR + of 5.07 (95% CI, 3.19–8.08), LR − of 0.26 (95% CI, 0.16–0.42), and DOR of 19.80 (95% CI, 9.28–42.28) were achieved for H3 K27M-mutant prediction. Significant heterogeneity was observed among the studies in terms of sensitivity (Q = 16.83, df = 7, p = 0.02; I2 = 58.40 [95% CI, 25.83–90.97]), LR − (Q = 16.61, df = 7, p = 0.02; I2 = 57.87 [95% CI, 24.81–90.93]), and DOR (Q = 14.05, df = 7, p = 0.05; I2 = 50.18 [95% CI, 10.06–90.31]). Conclusions This meta-analysis demonstrated a clinical value of MRI to predict H3 K27M-mutant in midline gliomas with a pooled sensitivity of 0.78 and specificity of 0.85. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
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Prediction of H3 K27M-mutant in midline gliomas by magnetic resonance imaging: a systematic review and meta-analysis |
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Zhuo, Zhizheng Duan, Yunyun Li, Xindi Gu, Hongqiu Haller, Sven Zhang, Peng Liu, Xing Zhang, Liwei Liu, Yaou |
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| score |
7.4008837 |