Differentiating brucellar spondylitis from tuberculous spondylitis with an interpretable machine learning model using intra- and peri-lesion radiomics features from multi-sequence MRI

English Keywords:Brucellar spondylitis  Tuberculous spondylitis  Multi-modal radiomics  Machine learning  Differential diagnosis

English Abstract:

  【Abstract】 Objectives: To construct an interpretable machine learning model based on intra- and peri-lesional multi-modal radiomics features of patients with brucellarspondylitis(BS) and tuberculous spondylitis(TS), and to explore the values in differential diagnosis. Methods: A retrospective analysis was conducted on 186 patients with BS(n=83) and TS(n=103) admitted to the Sixth People′s Hospital of Xinjiang Uygur Autonomous Region from January 2020 to December 2024. All patients were confirmed by pathological examination or microbial culture. All patients underwent spinal MRI, including T1-weighted imaging(T1WI), T2-weighted imaging(T2WI), and fat-suppressed T2-weighted imaging(FS-T2WI) sequences. The core lesion area(region of interest, ROI) and a 5mm surrounding expansion area(peri-ROI) were delineatedusing the 3D Slicer software. A total of 1, 500 radiomics features, covering first-order statistics, texture features, and morphological features, were extracted from each sequence. A total of 113 machine learning combination workflows were constructed, each comprising two modules: feature selection and model fitting. The models were evaluated using the receiver operating characteristic(ROC) curve for the area under the curve(AUC), for accuracy, sensitivity, specificity, and F1 score. Meanwhile, calibration curves were used to analyze the reliability of predicted probabilities, and Shapley additive explanations(SHAP) values were utilized to interpret the contribution of key features to model decision-making. Results: The training set AUCs of the intra-lesional feature models for single-sequence T1WI, T2WI, and FS-T2WI ranged from 0.987 to 0.998, with accuracies ranging from 0.909 to 0.977. For the T1WI intra-lesional feature model, the test set AUC was 0.787 with a false positive rate of 0.338; After combining peri-lesional features, the model AUC increased to 0.806, and accuracy increased to 0.762. For the T2WI intra-lesional feature model, the test set AUC was 0.782 with a false positive rate of 0.380; After combining peri-lesional features, the test set AUC increased to 0.824, and the false positive rate decreased to 0.310. For the FS-T2WI intra-lesional model, the test set AUC was 0.829 with a false positive rate of 0.296; After combining peri-lesional features, the test set AUC increased to 0.810, with a false positive rate of 0.296. The multi-modal combined model, fusing intra- and peri-lesional features from T1WI, T2WI, and FS-T2WI sequences, achieved a test set AUC of 0.841(95% confidence interval: 0.777-0.899), an accuracy of 0.804, a sensitivity of 0.856, a specificity of 0.732, F1 score of 0.834, and false positive and false negative rates of 0.268 and 0.144, respectively. Feature analysis revealed that the wavelet low-high-low(LHL) subband first-order mean absolute deviation of the intra-lesional FS-T2WI sequence(P=1.56×10-21) and the dependence variance of the peri-lesional gray level dependence matrix(GLDM)(P=1.68×10-12) were the most discriminative features. SHAP interpretability analysis found that intra-lesional features dominated the contribution(mean SHAP value of 0.0709), among which the wavelet LHL mean absolute deviation(SHAP value=0.0709) and the intra-lesional FS wavelet LHL GLDM dependence entropy(SHAP value=0.0588) had the most significant impact on classification decisions. Conclusions: The interpretable machine learning model based on multi-modal MRI radiomics features can effectively capture the microstructural and pathological heterogeneity between BS and TS, significantly improving the differential diagnostic efficacy for both diseases.