CT-based deep learning radiogenomics for predicting key glioma genotypes (IDH, ATRX, EGFR, TP53)
Neuroradiology, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/s00234-026-04043-3
- Dergi Adı: Neuroradiology
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, CINAHL, EMBASE, MEDLINE, Academic Search Ultimate (EBSCO), Natural Science Collection (ProQuest), Biological Science Database (ProQuest), Biomedical Reference Collection: Corporate Edition (EBSCO), Health Research Premium Collection (ProQuest), Pharma Collection (ProQuest), Technology Collection (ProQuest)
- Anahtar Kelimeler: Computed Tomography, Deep Learning, Glioma, Molecular Biomarkers, Radiogenomics
- Dokuz Eylül Üniversitesi Adresli: Evet
Özet
Purpose: Molecular subtyping guides diagnosis and targeted therapy for gliomas. Although MRI—the current imaging standard—can be time-consuming and is sometimes contraindicated, computed tomography (CT) is faster, more widely available, and often preferable in emergency and resource-limited settings. We evaluated whether CT-based radiogenomic signatures combined with machine learning could accurately predict clinically relevant glioma molecular markers. Methods: In this retrospective study, we characterised non-contrast CT (NCCT) scans from 197 adults with histologically confirmed gliomas. Models were developed to predict mutations in four molecular markers: ATRX (n=81), EGFR (n=17), TP53 (n=71), and IDH (n=183). We extracted 208 quantitative radiomic features and added basic demographic variables. Feature selection used LASSO-RFE and Gradient Boosting–RFE with cross-validation. Six classical machine-learning classifiers and deep-learning approaches — including custom fully connected neural networks (FCNN) and TabNet — were trained and compared using ROC–AUC as the primary performance metric. Results: Deep-learning methods outperformed conventional classifiers for all targets. TabNet achieved ROC–AUCs of 0.900 (95% CI: 0.717–0.989; ATRX), 0.955 (95% CI: 0.661–0.978; TP53), and 0.917 (95% CI: 0.858–0.975; EGFR). A custom FCNN obtained a ROC–AUC of 0.971 (95% CI: 0.876–0.995) for IDH. Cross-validation coefficients of variation were 2.0% for ATRX and 3.0% for TP53 and EGFR, and 16.0% for IDH. Deep-learning approaches yielded statistically significant improvements over conventional methods (p-values ranging from <0.05 to <0.001). Conclusion: NCCT-based analytical methods were able to predict clinically relevant genetic mutations in gliomas and demonstrated performance comparable to established techniques. These findings suggest that CT may serve as a practical option for molecular profiling in urgent or resource-limited settings. Nonetheless, external validation is necessary prior to clinical translation. The EGFR findings, arising from a small EGFR-tested subgroup (n = 17), remain preliminary; the observed performance may not reflect generalisable accuracy, and independent validation in larger cohorts is required.