Abstract:

Objective: To evaluate the value of a contrast-enhanced T₁-weighted imaging (CE-T₁WI) based radiomics model in differentiating tuberculous spondylitis (TS) from brucella spondylitis (BS). Methods: Clinical data and CE-T₁WI images from 274 confirmed patients (131 TS, 143 BS) in 4 medical centers were collected. Radiomics features were extracted from manually delineated regions of interest (ROIs) using 3D-Slicer. Feature selection was performed using the Mann-Whitney U test (P<0.05), Spearman correlation analysis (coefficient>0.9), and the LASSO algorithm. The dataset was randomly split into a training cohort (n=219) and a validation cohort (n=55) in an 8∶2 ratio. Four machine learning models (Random Forest, Extra Trees, XGBoost, LightGBM) were established, and their diagnostic performances were evaluated using ROC curves. Results: A total of 107 radiomics features were initially extracted from CE-T₁WI images. After feature selection, 11 optimal features were retained for model construction, including 7 texture features, 3 shape features, and 1 first-order feature. In the validation cohort, the AUC values of Random Forest, Extra Trees, XGBoost, and LightGBM models established based on these features were 0.773, 0.822, 0.838, and 0.702, respectively. Among them, the XGBoost model demonstrated the best overall diagnostic performance with relatively high accurate rate, sensitivity and specificity. Conclusion: The CE-T₁WI-based radiomics machine learning models are effective for differentiating TS from BS. The XGBoost model showed best diagnostic performance, making it a promising tool for clinical decision-making.

Key words: Magnetic resonance imaging, Artificial intelligence, Tuberculosis, spinal, Brucellosis