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

Abstract

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.