Preprocedural CT Radiomic Features of the Basal Ganglia Are Associated with Post-Thrombectomy Hyperdense Transformation: A LASSO-Based Logistic Regression Analysis

Purpose: To investigate whether radiomic characteristics of visually unremarkable basal ganglia on preprocedural non-contrast computed tomography (CT) are associated with subsequent post-thrombectomy hyperdense transformation (HT) in patients with acute ischemic stroke. Materials and Methods: This retrospective single-center study included a derivation cohort of 84 patients with preserved Alberta Stroke Programme Early CT Scores (ASPECTS ≥ 8), no visually appreciable basal ganglia abnormality, anterior circulation large-vessel occlusion, and complete reperfusion after mechanical thrombectomy between January 2022 and July 2023. An independent temporal validation cohort comprised 31 consecutive eligible patients treated between August and November 2023. The ipsilateral basal ganglia were manually segmented on preprocedural non-contrast CT, and radiomic features were extracted using LIFEx. Candidate features underwent interobserver reproducibility assessment and correlation filtering before least absolute shrinkage and selection operator logistic regression with 10-fold cross-validation. The final model was applied without modification to the temporal validation cohort. Nested cross-validation and learning-curve analysis were additionally performed to assess model-development optimism and stability. Results: The final model retained two texture features, Gray-Level Run Length Matrix Short Runs Emphasis and Gray-Level Size Zone Matrix Grey Level Variance. In the derivation cohort, the model achieved an area under the receiver operating characteristic curve (AUC) of 0.760 (95% CI, 0.652–0.855), with 48.8% sensitivity, 95.3% specificity, and 72.6% accuracy. When applied without modification to the independent temporal validation cohort, the AUC was 0.677 (95% CI, 0.475–0.858), with 33.3% sensitivity, 93.8% specificity, and 64.5% accuracy. Nested cross-validation yielded a mean AUC of 0.673 ± 0.037, with variability in feature selection across resampling iterations. Learning-curve analysis showed an early plateau, with little improvement in cross-validated discrimination as the training sample increased within the evaluated range. Conclusions: Radiomic analysis of visually unremarkable basal ganglia on preprocedural non-contrast CT identified an exploratory two-feature imaging signature associated with subsequent post-thrombectomy HT. The model retained modest discriminatory ability in temporal validation, although internal resampling demonstrated model-development optimism and limited feature-selection stability. These findings should be considered hypothesis-generating and warrant further evaluation using larger datasets, alternative modeling approaches, and independent multicenter validation.