Diagnostic Accuracy of Deep Learning Models in Predicting Glioma Molecular Markers: A Systematic Review and Meta-Analysis

Abstract

Background/Objectives: Integrating deep learning (DL) into radiomics offers a noninvasive approach to predicting molecular markers in gliomas, a crucial step toward personalized medicine. This study aimed to assess the diagnostic accuracy of DL models in predicting various glioma molecular markers using MRI. Methods: Following PRISMA guidelines, we systematically searched PubMed, Scopus, Ovid, and Web of Science until 27 February 2024 for studies employing DL algorithms to predict gliomas’ molecular markers from MRI sequences. The publications were assessed for the risk of bias, applicability concerns, and quality using the QUADAS-2 tool and the radiomics quality score (RQS). A bivariate random-effects model estimated pooled sensitivity and specificity, accounting for inter-study heterogeneity. Results: Of 728 articles, 43 were qualified for qualitative analysis, and 30 were included in the meta-analysis. In the validation cohorts, MGMT methylation had a pooled sensitivity of 0.74 (95% CI: 0.66–0.80) and a pooled specificity of 0.75 (95% CI: 0.65–0.82), both with significant heterogeneity (p = 0.00, I2 = 80.90–84.50%). ATRX and TERT mutations had a pooled sensitivity of 0.79 (95% CI: 0.67–0.87) and 0.81 (95% CI: 0.72–0.87) and a pooled specificity of 0.85 (95% CI: 0.78–0.91) and 0.70 (95% CI: 0.61–0.77), respectively. Meta-regression analyses revealed that significant heterogeneity was influenced by data sources, MRI sequences, feature extraction methods, and validation techniques. Conclusions: While the DL models show promising prediction accuracy for glioma molecular markers, variability in the study settings complicates clinical translation. To bridge this gap, future efforts should focus on harmonizing multi-center MRI datasets, incorporating external validation, and promoting open-source studies and data sharing.

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