Value of Endobronchial Ultrasound-Derived Radiomics in Differentiating Pulmonary Sarcoidosis from Mediastinal Lymph Node Tuberculosis

Introduction: To explore the feasibility of an ultrasound radiomics machine learning model based on endobronchial ultrasound (EBUS) for differentiating pulmonary sarcoidosis from mediastinal lymph node tuberculosis. Methods: Clinical characteristics and ultrasound image data from 100 patients diagnosed with pulmonary sarcoidosis and 70 patients diagnosed with mediastinal lymph node tuberculosis were collected. Statistical analysis was performed to compare clinical features, such as age, gender, smoking history, and lymph node size and so on, between the two groups. The least absolute shrinkage and selection operator (Lasso) was used to analyze the radiomics features extracted from EBUS-based ultrasound images. A support vector machine (SVM) algorithm was applied to establish an EBUS-based radiomics model, and clinical features with statistically significant were incorporated to optimize the model. A total of 170 lymph nodes were randomly divided into training group (n=119) and validation group (n=51), with the diagnostic performance of the model assessed using receiver operating characteristic (ROC) curves and the area under the curve (AUC), accuracy, sensitivity, and specificity. Results: Seven stable radiomics features with non-zero coefficients and four clinical features were selected as inputs for the model. The SVM model demonstrated great performance in both groups. In the training group, the ROC AUC of the SVM model was 0.909 (95% CI: 0.897-0.922), with an accuracy of 88.2%, sensitivity of 82.4%, and specificity of 92.6%. In the validation group, the ROC AUC was 0.917 (95% CI: 0.901-0.934), with an accuracy of 80.4%, sensitivity of 68.4%, and specificity of 87.5%. Conclusion: The SVM model based on EBUS ultrasound radiomics and clinical data shows promising feasibility for differentiating pulmonary sarcoidosis from mediastinal lymph node tuberculosis. It exhibits significant potential in clinical practice and provides a new method for the early diagnosis of these conditions.