MRI-derived radiomics and end-to-end deep learning models for predicting glioma ATRX status: a systematic review and meta-analysis of diagnostic test accuracy studies

Abstract

We aimed to systematically review and meta-analyze the predictive value of magnetic resonance imaging (MRI)-derived radiomics/end-to-end deep learning (DL) models in predicting glioma alpha thalassemia/mental retardation syndrome X-linked (ATRX) status. We conducted a comprehensive search across four major databases—Web of Science, PubMed, Scopus, and Embase. All the studies that assessed the performance of radiomics and/or end-to-end DL models for predicting glioma ATRX status were included. Quality assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) criteria and the METhodological RadiomICs Score (METRICS). Pooled estimates for performance metrics were calculated. I-squared was used to assess heterogeneity, while subgroup and sensitivity analyses were performed to find its potential sources. Publication bias was assessed using Deeks' funnel plots. Seventeen and eleven studies were included in the systematic review and meta-analysis, respectively. Most of the studies had a low risk of bias and low concern for applicability according to the QUADAS-2. Also, most of them had good quality according to the METRICS. Meta-analysis showed a pooled sensitivity of 0.80 (95%CI: 0.71–0.96), a specificity of 0.82 (95%CI: 0.67–0.93), a positive diagnostic likelihood ratio (DLR) of 6.77 (95%CI: 4.67–9.82), a negative DLR of 0.15 (95%CI: 0.06–0.38), a diagnostic odds ratio of 30.36 (95%CI: 15.87–58.05), and an area under the curve (AUC) of 0.92 (95%CI: 0.89–0.94). Subgroup analysis revealed significant intergroup differences based on several factors. Radiomics models can accurately predict ATRX status in gliomas, enhancing non-invasive tumor characterization and guiding treatment strategies.