MRI-derived deep learning models for predicting 1p/19q codeletion status in glioma patients: a systematic review and meta-analysis of diagnostic test accuracy studies.
Purpose: We conducted a systematic review and meta-analysis to evaluate the performance of magnetic resonance imaging (MRI)-derived deep learning (DL) models in predicting 1p/19q codeletion status in glioma patients. Methods: The literature search was performed in four databases: PubMed, Web of Scie...
| Published in: | Neuroradiology Vol. 67; no. 7; pp. 1667 - 1682 |
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| Main Authors: | , , , , , , , , , , |
| Format: | research systematic review tables/charts Journal Article |
| Published: |
Springer Nature
Jul2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187625127&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187625127 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Jul2025 vid: 67 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187625127 185144814 187625127 187625127 10.1007/s00234-025-03631-z 187625127 ppf: 1667 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: MRI-derived deep learning models for predicting 1p/19q codeletion status in glioma patients: a systematic review and meta-analysis of diagnostic test accuracy studies. aug: au: Ahmadzadeh, Amir Mahmoud Broomand Lomer, Nima Ashoobi, Mohammad Amin Elyassirad, Danial Gheiji, Benyamin Vatanparast, Mahsa Rostami, Amirhossein Abouei Mehrizi, Mohammad Ali Tabari, Azadeh Bathla, Girish Faghani, Shahriar affil: https://ror.org/04sfka033 Mashhad University of Medical Sciences, Mashhad, Iran sug: subj: Magnetic Resonance Imaging Deep Learning Chromosome Aberrations Glioma Sensitivity and Specificity Evaluation Validity Evaluation Human Systematic Review Meta Analysis PubMed Embase Publication Bias Evaluation Confidence Intervals Probability Checklists Odds Ratio ROC Curve ab: Purpose: We conducted a systematic review and meta-analysis to evaluate the performance of magnetic resonance imaging (MRI)-derived deep learning (DL) models in predicting 1p/19q codeletion status in glioma patients. Methods: The literature search was performed in four databases: PubMed, Web of Science, Embase, and Scopus. We included the studies that evaluated the performance of end-to-end DL models in predicting the status of glioma 1p/19q codeletion. The quality of the included studies was assessed by the Quality assessment of diagnostic accuracy studies-2 (QUADAS-2) METhodological RadiomICs Score (METRICS). We calculated diagnostic pooled estimates and heterogeneity was evaluated using I2. Subgroup analysis and sensitivity analysis were conducted to explore sources of heterogeneity. Publication bias was evaluated by Deeks' funnel plots. Results: Twenty studies were included in the systematic review. Only two studies had a low quality. A meta-analysis of the ten studies demonstrated a pooled sensitivity of 0.77 (95% CI: 0.63–0.87), a specificity of 0.85 (95% CI: 0.74–0.92), a positive diagnostic likelihood ratio (DLR) of 5.34 (95% CI: 2.88–9.89), a negative DLR of 0.26 (95% CI: 0.16–0.45), a diagnostic odds ratio of 20.24 (95% CI: 8.19–50.02), and an area under the curve of 0.89 (95% CI: 0.86–0.91). The subgroup analysis identified a significant difference between groups depending on the segmentation method used. Conclusion: DL models can predict glioma 1p/19q codeletion status with high accuracy and may enhance non-invasive tumor characterization and aid in the selection of optimal therapeutic strategies. pubtype: Academic Journal doctype: meta analysis research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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