Diagnostic performance of radiomics using machine learning algorithms to predict MGMT promoter methylation status in glioma patients: a meta-analysis.
Purpose: We aimed to assess the diagnostic performance of radiomics using machine learning algorithms to predict the methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter in glioma patients.Methods: A comprehensive literature search of PubMed, EMBASE, and Web of Science un...
| Publicado en: | Diagnostic & Interventional Radiology Vol. 27; no. 6; pp. 716 - 726 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Galenos Yayinevi Tic. LTD. STI
Nov2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=153806581&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153806581 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13053825 39NM jtl: Diagnostic & Interventional Radiology issn: 13053825 maglogo: N pubinfo: dt: Nov2021 vid: 27 iid: 6 pid: 28155 pub: Galenos Yayinevi Tic. LTD. STI artinfo: ui: 153806581 153806581 NLM34792025 153806581 10.5152/dir.2021.21153 NLM34792025 153806581 ppf: 716 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Diagnostic performance of radiomics using machine learning algorithms to predict MGMT promoter methylation status in glioma patients: a meta-analysis. aug: au: Huan Huang Fei-fei Wang Shigang Luo Guangxiang Chen Guangcai Tang Huang, Huan Wang, Fei-Fei Luo, Shigang Chen, Guangxiang Tang, Guangcai affil: Department of Radiology, Affiliated Hospital of Southwest Medical University, Sichuan, China sug: subj: Brain Neoplasms Glioma Proteins DNA Methylation Enzymes Human Comparative Studies Meta Analysis Multicenter Studies Evaluation Research Validation Studies Interview Guides ab: Purpose: We aimed to assess the diagnostic performance of radiomics using machine learning algorithms to predict the methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter in glioma patients.Methods: A comprehensive literature search of PubMed, EMBASE, and Web of Science until 27 July 2021 was performed to identify eligible studies. Stata SE 15.0 and Meta-Disc 1.4 were used for data analysis.Results: A total of fifteen studies with 1663 patients were included: five studies with training and validation cohorts and ten with only training cohorts. The pooled sensitivity and specificity of machine learning for predicting MGMT promoter methylation in gliomas were 85% (95% CI 79%-90%) and 84% (95% CI 78%-88%) in the training cohort (n=15) and 84% (95% CI 70%-92%) and 78% (95% CI 63%-88%) in the validation cohort (n=5). The AUC was 0.91 (95% CI 0.88-0.93) in the training cohort and 0.88 (95% CI 0.85-0.91) in the validation cohort. The meta-regression demonstrated that magnetic resonance imaging sequences were related to heterogeneity. The sensitivity analysis showed that heterogeneity was reduced by excluding one study with the lowest diagnostic performance.Conclusion: This meta-analysis demonstrated that machine learning is a promising, reliable and repeatable candidate method for predicting MGMT promoter methylation status in glioma and showed a higher performance than non-machine learning methods. pubtype: Academic Journal doctype: meta analysis research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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