Integration of MRI radiomics features and clinical data for predicting neurological recovery after thoracic spinal stenosis surgery: a machine learning model.

Background: Thoracic spinal stenosis (TSS) is a rare yet debilitating condition, often requiring surgical decompression. Prognostic assessments traditionally rely on single clinical or imaging features, limiting prediction accuracy. This study explores whether radiomics-based models enhance outcome prediction in TSS. Methods: We retrospectively enrolled 106 surgically treated TSS patients (2012-2022), collecting clinical data and T2 axial MRI scans. Radiomics features were extracted from the most stenotic level, followed by rigorous feature selection (ICC > 0.9, U-test, Spearman, mRMR, and LASSO). Six machine learning classifiers were trained using radiomics and/or clinical data. Model performance was evaluated using AUC on an independent test set. Results: Radiomics models outperformed clinical models (SVM AUC: 0.824 vs. 0.731). The combined radiomics-clinical model achieved the highest test-set AUC of 0.867, offering improved sensitivity and specificity. Conclusion: In this preliminary exploratory study, integrating MRI radiomics with clinical data appeared to improve prediction of neurological recovery in TSS. These findings suggest that radiomics may enable objective, high-dimensional assessment of spinal cord pathology and potentially support individualized surgical decision-making, although further validation in larger, multicenter prospective cohorts is required.