SynthesisNeuroradiology2025
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.
Synthesis in Neuroradiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of Deep Learning for Predicting Hematoma Expansion in Intracerebral Hemorrhage Using Computed Tomography Scans: A Systematic Review and Meta-Analysis of Diagnostic Accuracy.La Radiologia medica · 2025Pooled it
- Classification of histology and molecular subtypes of brain gliomas and glioneuronal and neuronal tumors using a deep learning approach.European radiology · 2026Article
- Exploring the role of advanced MRI in understanding glioblastoma biology: A scoping review protocol.MethodsX · 2026Article
- Exploring the Role of Advanced MRI in Understanding Glioblastoma Biology: A Scoping Review.Cancers · 2026Review
- Radiomics-Driven Hybrid Deep Learning for MRI-Based Prediction of Glioma Grade and 1p/19q Codeletion.Tomography (Ann Arbor, Mich.) · 2026Article
- Review
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Authors and funding
11 authors.
Funding
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Abstract
purposeWe 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.
methodsThe 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 I
resultsTwenty 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.
conclusionDL 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.
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