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Purpose <p>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.</p> Methods <p>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 I<sup>2</sup>. Subgroup analysis and sensitivity analysis were conducted to explore sources of heterogeneity. Publication bias was evaluated by Deeks’ funnel plots.</p> Results <p>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.</p> Conclusion <p>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.</p> Graphical Abstract <p></p>

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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

  • 期刊论文

  • 发表日期：

    2025年5月15日

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  • Amir Mahmoud Ahmadzadeh,
  • Nima Broomand Lomer,
  • Mohammad Amin Ashoobi,
  • Danial Elyassirad,
  • Benyamin Gheiji,
  • Mahsa Vatanparast,
  • Amirhossein Rostami,
  • Mohammad Ali Abouei Mehrizi,
  • Azadeh Tabari,
  • Girish Bathla,
  • Shahriar Faghani

摘要

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.

Graphical Abstract