The diagnostic role of diffusional kurtosis imaging in glioma grading and differentiation of gliomas from other intra-axial brain tumours: a systematic review with critical appraisal and meta-analysis
Purpose We aim to illustrate the diagnostic performance of diffusional kurtosis imaging (DKI) in the diagnosis of gliomas. Methods A review protocol was developed according to the (PRISMA-P) checklist, registered in the international prospective register of systematic reviews (PROSPERO) and published. A literature search in 4 databases was performed using the keywords ‘glioma’ and ‘diffusional kurtosis’. After applying a robust inclusion/exclusion criteria, included articles were independently evaluated according to the QUADAS-2 tool and data extraction was done. Reported sensitivities and specificities were used to construct 2 × 2 tables and paired forest plots using the Review Manager (RevMan®) software. A random-effect model was pursued using the hierarchical summary receiver operator characteristics. Results A total of 216 hits were retrieved. Considering duplicates and inclusion criteria, 23 articles were eligible for full-text reading. Ultimately, 19 studies were eligible for final inclusion. The quality assessment revealed 9 studies with low risk of bias in the 4 domains. Using a bivariate random-effect model for data synthesis, summary ROC curve showed a pooled area under the curve (AUC) of 0.92 and estimated sensitivity of 0.87 (95% CI 0.78–0.92) in high-/low-grade gliomas’ differentiation. A mean difference in mean kurtosis (MK) value between HGG and LGG of 0.22 (95% CI 0.25–0.19) was illustrated (p value = 0.0014) with moderate heterogeneity (I2 = 73.8%). Conclusion DKI shows good diagnostic accuracy in the differentiation of high- and low-grade gliomas further supporting its potential role in clinical practice. Further exploration of DKI in differentiating IDH status and in characterising non-glioma CNS tumours is however needed..
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Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:62 |
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| Enthalten in: |
Neuroradiology - 62(2020), 7 vom: 04. Mai, Seite 791-802 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Abdalla, Gehad [VerfasserIn] |
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| Links: |
Volltext [lizenzpflichtig] |
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| Themen: |
Diagnosis |
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| Anmerkungen: |
© The Author(s) 2020 |
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| doi: |
10.1007/s00234-020-02425-9 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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OLC203520402X |
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| 245 | 1 | 0 | |a The diagnostic role of diffusional kurtosis imaging in glioma grading and differentiation of gliomas from other intra-axial brain tumours: a systematic review with critical appraisal and meta-analysis |
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| 520 | |a Purpose We aim to illustrate the diagnostic performance of diffusional kurtosis imaging (DKI) in the diagnosis of gliomas. Methods A review protocol was developed according to the (PRISMA-P) checklist, registered in the international prospective register of systematic reviews (PROSPERO) and published. A literature search in 4 databases was performed using the keywords ‘glioma’ and ‘diffusional kurtosis’. After applying a robust inclusion/exclusion criteria, included articles were independently evaluated according to the QUADAS-2 tool and data extraction was done. Reported sensitivities and specificities were used to construct 2 × 2 tables and paired forest plots using the Review Manager (RevMan®) software. A random-effect model was pursued using the hierarchical summary receiver operator characteristics. Results A total of 216 hits were retrieved. Considering duplicates and inclusion criteria, 23 articles were eligible for full-text reading. Ultimately, 19 studies were eligible for final inclusion. The quality assessment revealed 9 studies with low risk of bias in the 4 domains. Using a bivariate random-effect model for data synthesis, summary ROC curve showed a pooled area under the curve (AUC) of 0.92 and estimated sensitivity of 0.87 (95% CI 0.78–0.92) in high-/low-grade gliomas’ differentiation. A mean difference in mean kurtosis (MK) value between HGG and LGG of 0.22 (95% CI 0.25–0.19) was illustrated (p value = 0.0014) with moderate heterogeneity (I2 = 73.8%). Conclusion DKI shows good diagnostic accuracy in the differentiation of high- and low-grade gliomas further supporting its potential role in clinical practice. Further exploration of DKI in differentiating IDH status and in characterising non-glioma CNS tumours is however needed. | ||
| 650 | 4 | |a Magnetic resonance imaging | |
| 650 | 4 | |a Diffusion-weighted imaging | |
| 650 | 4 | |a Gliomas | |
| 650 | 4 | |a Diagnosis | |
| 700 | 1 | |a Dixon, Luke |4 aut | |
| 700 | 1 | |a Sanverdi, Eser |4 aut | |
| 700 | 1 | |a Machado, Pedro M. |4 aut | |
| 700 | 1 | |a Kwong, Joey S. W. |4 aut | |
| 700 | 1 | |a Panovska-Griffiths, Jasmina |4 aut | |
| 700 | 1 | |a Rojas-Garcia, Antonio |4 aut | |
| 700 | 1 | |a Yoneoka, Daisuke |4 aut | |
| 700 | 1 | |a Veraart, Jelle |4 aut | |
| 700 | 1 | |a Van Cauter, Sofie |4 aut | |
| 700 | 1 | |a Abdel-Khalek, Ahmed M. |4 aut | |
| 700 | 1 | |a Settein, Magdy |4 aut | |
| 700 | 1 | |a Yousry, Tarek |4 aut | |
| 700 | 1 | |a Bisdas, Sotirios |4 aut | |
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