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Objectives <p>This study aimed to investigate the diagnostic test accuracy of MRI-based radiomics studies for predicting EGFR mutation in brain metastasis originating from lung cancer.</p> Methods <p>This meta-analysis, conducted following PRISMA guidelines, involved a systematic search in PubMed, Embase, and Web of Science up to November 3, 2024. Eligibility criteria followed the PICO framework, assessing population, intervention, comparison, and outcome. The RQS and QUADAS-2 tools were employed for quality assessment. A Bayesian model determined summary estimates, and statistical analysis was conducted using R and STATA software.</p> Results <p>Eleven studies consisting of nine training and ten validation cohorts were included in the meta-analysis. In the training cohorts, MRI-based radiomics showed robust predictive performance for EGFR mutations in brain metastases, with an AUC of 0.90 (95% CI: 0.82–0.93), sensitivity of 0.84 (95% CI: 0.80–0.88), specificity of 0.86 (95% CI: 0.80–0.91), and a diagnostic odds ratio (DOR) of 34.17 (95% CI: 19.16–57.49). Validation cohorts confirmed strong performance, with an AUC of 0.91 (95% CI: 0.69–0.95), sensitivity of 0.79 (95% CI: 0.73–0.84), specificity of 0.88 (95% CI: 0.83–0.93), and a DOR of 31.33 (95% CI: 15.50–58.3). Subgroup analyses revealed notable trends: the T1C + T2WI sequences and 3.0 T scanners showed potential superiority, machine learning-based radiomics and manual segmentation exhibited higher diagnostic accuracy, and PyRadiomics emerged as the preferred feature extraction software.</p> Conclusion <p>This meta-analysis suggests that MRI-based radiomics holds promise for the non-invasive prediction of EGFR mutations in brain metastases of lung cancer.</p>

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A Bayesian meta-analysis on MRI-based radiomics for predicting EGFR mutation in brain metastasis of lung cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月10日

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  • Peyman Tabnak,
  • Zana Kargar,
  • Mohammad Ebrahimnezhad,
  • Zanyar HajiEsmailPoor

摘要

Objectives

This study aimed to investigate the diagnostic test accuracy of MRI-based radiomics studies for predicting EGFR mutation in brain metastasis originating from lung cancer.

Methods

This meta-analysis, conducted following PRISMA guidelines, involved a systematic search in PubMed, Embase, and Web of Science up to November 3, 2024. Eligibility criteria followed the PICO framework, assessing population, intervention, comparison, and outcome. The RQS and QUADAS-2 tools were employed for quality assessment. A Bayesian model determined summary estimates, and statistical analysis was conducted using R and STATA software.

Results

Eleven studies consisting of nine training and ten validation cohorts were included in the meta-analysis. In the training cohorts, MRI-based radiomics showed robust predictive performance for EGFR mutations in brain metastases, with an AUC of 0.90 (95% CI: 0.82–0.93), sensitivity of 0.84 (95% CI: 0.80–0.88), specificity of 0.86 (95% CI: 0.80–0.91), and a diagnostic odds ratio (DOR) of 34.17 (95% CI: 19.16–57.49). Validation cohorts confirmed strong performance, with an AUC of 0.91 (95% CI: 0.69–0.95), sensitivity of 0.79 (95% CI: 0.73–0.84), specificity of 0.88 (95% CI: 0.83–0.93), and a DOR of 31.33 (95% CI: 15.50–58.3). Subgroup analyses revealed notable trends: the T1C + T2WI sequences and 3.0 T scanners showed potential superiority, machine learning-based radiomics and manual segmentation exhibited higher diagnostic accuracy, and PyRadiomics emerged as the preferred feature extraction software.

Conclusion

This meta-analysis suggests that MRI-based radiomics holds promise for the non-invasive prediction of EGFR mutations in brain metastases of lung cancer.