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

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Abstract
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.

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