Literature DB >> 35416485

Prediction of H3 K27M-mutant in midline gliomas by magnetic resonance imaging: a systematic review and meta-analysis.

Tiantian Hua1, Zhizheng Zhuo1, Liwei Zhang2, Yaou Liu3, Yunyun Duan1, Xindi Li4, Hongqiu Gu5,6, Sven Haller7, Peng Zhang8, Xing Liu9.   

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.
© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Entities:  

Keywords:  H3 K27M-mutant; Magnetic resonance imaging; Meta-analysis; Midline gliomas

Year:  2022        PMID: 35416485     DOI: 10.1007/s00234-022-02947-4

Source DB:  PubMed          Journal:  Neuroradiology        ISSN: 0028-3940            Impact factor:   2.804


  1 in total

1.  Automated machine learning based on radiomics features predicts H3 K27M mutation in midline gliomas of the brain.

Authors:  Xiaorui Su; Ni Chen; Huaiqiang Sun; Yanhui Liu; Xibiao Yang; Weina Wang; Simin Zhang; Qiaoyue Tan; Jingkai Su; Qiyong Gong; Qiang Yue
Journal:  Neuro Oncol       Date:  2020-03-05       Impact factor: 12.300

  1 in total

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