Explainable incremental-value analysis of apparent diffusion coefficient and arterial spin labeling radiomics for ATRX status prediction in glioblastoma

Introduction: Alpha-thalassemia/mental retardation syndrome X-linked (ATRX) mutation is an uncommon but biologically relevant molecular feature in glioblastoma (GBM), linked to tumor heterogeneity, DNA damage response pathways, and treatment-relevant biology. Noninvasive prediction of ATRX status remains challenging, and the incremental value of physiologic MRI beyond structural imaging is unclear. Methods: We analyzed 106 patients with GBM with available ATRX status and complete multiparametric MRI. Four radiomics models were compared. Model 1 used structural MRI features from contrast-enhanced T1-weighted, T2-weighted, and FLAIR images, along with age and sex. Model 1A additionally incorporated ADC and ASL-CBF radiomic features. Models 1B and 1C served as ablation models isolating the individual contribution of ADC and ASL-CBF, respectively. Six machine-learning classifiers were evaluated using stratified cross-validation, class-imbalance-aware metrics, bootstrapped confidence intervals, paired DeLong testing, and SHAP explainability. Results: The best structural model achieved an ROC-AUC of 0.721, a PR-AUC of 0.322, and a sensitivity of 0.737. Model 1A demonstrated statistically significant improvements, achieving an ROC-AUC of 0.753, a PR-AUC of 0.364, and a sensitivity of 0.947. Across classifiers, ADC and ASL improved discrimination in five of six classifiers (DeLong, p