Radiomics Analysis of Apparent Diffusion Coefficient Maps of Parotid Gland to Diagnose Morphologically Normal Sjogren Syndrome

This study investigated novel radiomic features derived from apparent diffusion coefficient (ADC) maps for diagnosing Sjögren syndrome (SS) in patients without visible magnetic resonance morphologic changes.

This study prospectively analyzed 119 consecutive patients with SS and 95 healthy volunteers using 3.0 T magnetic resonance imaging, including diffusion-weighted imaging with b values of 0 and 1000 s/mm 2 . Regions of interest (ROIs) were manually delineated along the margins of the largest parotid gland slice on ADC maps, from which 838 quantitative features were automatically extracted. Based on the intraclass correlation coefficient and absolute correlation coefficient, 45 radiomic parameters were selected for analysis. The differentiation between patients with SS and healthy controls was evaluated using univariate analysis and receiver operating characteristic analysis. Multiple radiomic features were integrated using binary logistic regression analysis. Through machine learning algorithms, 4 predictive models were developed, and each was thoroughly evaluated for predictive performance. The Shapley Additive exPlanations (SHAP) approach was employed to elucidate the predictive factors influencing the model.

Twenty-two radiomic parameters demonstrated significant differences between SS and control groups. The AUCs were 0.681 ± 0.100 (0.559~0.878). The optimal diagnostic combination for SS consisted of 6 parameters: 0.975Quantile, 180dr_D(4)_Cluster Prominence, 225dr_D(7)_Entropy, 315dr_D(7)_Entropy, Compactness2, and Max3D Diameter, achieving an AUC of 0.956. The SVM, GBM, and XGBoost models were effectively distinguished SS from healthy controls. Among all the parameters, Max3DDiameter demonstrated the strongest predictive power in the model.

Radiomic features derived from ADC maps demonstrate significant potential in facilitating the early diagnosis of SS.