Preoperative differentiation of mediastinum and retroperitoneum ganglioneuroma from schwannoma with clinical data and enhanced CT: developing a multivariable prediction model.

AIM: To develop a multivariable prediction model for preoperative differentiation of ganglioneuroma (GN) from schwannoma in mediastinum and retroperitoneum based on clinical data and enhanced computed tomography (CT).

MATERIALS AND METHODS: This was a retrospective diagnostic study. Patients diagnosed with mediastinum or retroperitoneal GN or schwannoma at Zhongshan Hospital between July 2006 and March 2022 were divided into a training cohort and a validation cohort at a ratio of 7:3. Clinical information and CT features were collected. Histopathology was the reference standard for diagnosis. The model was developed using binary logistic regression. The predictive performance of the model was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

RESULTS: A total of 105 patients (47 men and 58 women; mean age of 41 ± 15 years) were enrolled. There were significant differences in symptoms (p=0.006), location (p=0.008), ratio of the craniocaudal diameter (CC) to the major axis on axial images (CC/M; p=0.025), ratio of the CC to the diameter on axial images (CC/D; p<0.001), density homogeneity (p=0.001), enhancement homogeneity (p<0.001), enhancement degree (p<0.001), venous phase CT attenuation value (V; p=0.011), and blood vessels changes (p=0.045) between GN and schwannoma. The area under the ROC curve (AUC) and accuracy in the validation cohort were 0.841 (95% confidence interval [CI] 0.672, 1.000) and 0.839 (95% CI: 0.674, 0.929), respectively. Calibration curves and DCA showed that the model was beneficial for patients.

CONCLUSION: The multivariable prediction model exhibited good predictive performance and may facilitate preoperative planning.