T1WI Radiomics Analysis of Anterior Scalene Muscle: A Preliminary Application in Neurogenic Thoracic Outlet Syndrome

Aim This study aimed to analyze the differences in radiomic features of the anterior scalene muscle and evaluate the diagnostic performance of MRI-based radiomics model for neurogenic thoracic outlet syndrome (NTOS). Materials and Methods Imaging data of patients with NTOS who underwent preoperative brachial plexus magnetic resonance neurography were collected and were randomly divided into training and test groups. The anterior scalene muscle area was sliced in the T1WI sequence as the region of interest for the extraction of radiomics features. The most significant features were identified using feature selection and dimensionality-reduction methods. Various machine learning algorithms were applied to construct regression models. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC). Results Totally, 267 radiomics features were extracted, of which 57 showed significant differences ( P ≤ 0.05) between the abnormal and normal anterior scalene muscle groups. The least absolute shrinkage and selection operator regression model identified 13 optimal radiomic features with nonzero coefficients for constructing the model. In the training set, the AUROCs of diagnostic models built by different machine learning algorithms, ranked from highest to lowest, were as follows: support vector machine (SVM), 0.953; multilayer perception (MLP), 0.936; logistic regression (LR), 0.926; light gradient boosting machine (LightGBM), 0.906; and K-nearest neighbors (KNN), 0.813. In the testing set, the rankings were as follows: LR, 0.933; SVM, 0.886; KNN, 0.843; LightGBM, 0.824; and MLP, 0.706. Conclusions NTOS is attributed to anterior scalene muscle abnormalities and exhibits distinct radiomic features. Integrating these features with machine learning can improve traditional manual image interpretation, offering further clarity in NTOS diagnosis.