基于增强CT的诊断模型鉴别腹盆部卡斯尔曼病与副神经节瘤的价值OA

Objective To investigate CT imaging differences between abdominopelvic Castleman disease(CD)and paraganglioma(PGL),identify imaging features with differential value,construct a diagnostic model,and evaluate its value for preoperative differential diagnosis.Methods A total of 57 patients with abdominopelvic CD or PGL,including 24 patients with CD and 33 patients with PGL,whose diagnoses were confirmed by postoperative histopathology,were retrospectively included.All patients underwent unenhanced and three-phase contrast-enhanced CT examinations.Multiphase CT attenuation values and morphological features of the lesions were assessed.The Mann-Whitney U test and the chi-square test were used to compare the two groups.Variables showing statistically significant differences in the univariate analysis were entered into multivariable logistic regression.Forward stepwise selection was used to select variables and construct the differential diagnostic model.Receiver operating characteristic(ROC)curve analysis was performed to evaluate model performance.Results Venous-phase CT attenuation and the difference between venous-phase and unenhanced CT attenuation values(ΔHU)were higher in the PGL group than in the CD group.Perilesional fascial reaction,satellite lesions,and the rim sign were more frequent in the CD group than in the PGL group(all P<0.05).A combined model incorporating ΔHU,perilesional fascial reaction,satellite lesions,and the rim sign yielded an area under the ROC curve(AUC)of 0.914(95%CI:0.809-0.972),which was higher than that of venous-phase CT attenuation alone(AUC=0.693)or ΔHU alone(AUC=0.711).At a predicted probability cutoff of P(CD)=0.50,the combined model achieved the highest sensitivity(0.792),specificity(0.939),and accuracy(0.877).Conclusion The logistic regression model based on ΔHU,perilesional fascial reaction,satellite lesions,and the rim sign had good performance in differentiating abdominopelvic CD from PGL.This model may provide imaging-based information for preoperative differential diagnosis and perioperative management.