SynthesisAJNR. American journal of neuroradiology2025
Predicting 1p/19q Codeletion Status in Glioma Using MRI-Derived Radiomics: A Systematic Review and Meta-Analysis of Diagnostic Accuracy.
Synthesis in AJNR. American journal of neuroradiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Advancing precision risk stratification in adult diffuse gliomas through DSC-MRI-based habitat analysis.BMC medical imaging · 2026Article
- Amide proton transfer and arterial spin labeling for non-invasive molecular stratification of glioma: a multi-dataset imaging biomarker study.Neuroradiology · 2026Article
- Casting wider than U-Nets: evaluating the role of novel machine learning applications in radiation therapy.BJR artificial intelligence · 2026Review
- Improving radiomics-based isocitrate dehydrogenase 1 prediction in glioma patients using semi-supervised machine learning models.BMC medical imaging · 2025Article
- From Anatomy to Genomics Using a Multi-Task Deep Learning Approach for Comprehensive Glioma Profiling.Bioengineering (Basel, Switzerland) · 2025Article
- Explanation and Elaboration with Examples for METRICS (METRICS-E3): an initiative from the EuSoMII Radiomics Auditing Group.Insights into imaging · 2025Article
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Abstract
backgroundThe 1p/19q codeletion is a key genetic marker in gliomas and plays a crucial role in prognosis and treatment decisions. Traditional methods for detecting this genetic alteration rely on invasive tissue biopsies. PURPOSE: This systematic review and meta-analysis aimed to evaluate the performance of MRI-derived radiomics-based models to predict glioma 1p/19q codeletion status. DATA SOURCES: A literature search was conducted in 4 databases: PubMed, Web of Science, EMBASE, and Scopus. STUDY SELECTION: We selected the studies that assessed the performance of radiomics-based models in determining 1p/19q codeletion status. DATA ANALYSIS: The Methodological Radiomics Score was used to evaluate study quality. Pooled diagnostic estimates were calculated, and heterogeneity was assessed by using the DATA SYNTHESIS: Twenty-eight studies met the inclusion criteria for the systematic review. A meta-analysis of 10 studies yielded a pooled sensitivity of 0.82 (95% CI, 0.67-0.91), specificity of 0.80 (95% CI, 0.70-0.88), positive diagnostic likelihood (DLR) of 4.14 (95% CI, 2.62-6.52), negative DLR of 0.23 (95% CI, 0.12-0.43), diagnostic odds ratio of 18.37 (95% CI, 7.36-45.85), and area under the curve of 0.87 (95% CI, 0.84-0.90). Subgroup analysis revealed significant differences based on the country and segmentation method. LIMITATIONS: Our meta-analysis is limited by small number of studies with external validation cohorts.
conclusionsMRI-derived radiomics-based models demonstrated good predictive performance for glioma 1p/19q codeletion status, highlighting their potential as a noninvasive tool for glioma characterization and for aiding in treatment decision-making.
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