Development and validation of a nomogram model based on ultrasound and contrast-enhanced ultrasound features for differentiating mass-forming pancreatitis and pancreatic ductal adenocarcinoma.

PURPOSE

To explore the value of ultrasound (US) and contrast-enhanced ultrasound (CEUS) in differentiating mass-forming Pancreatitis (MFP) and pancreatic ductal adenocarcinoma (PDAC).

METHODS

This retrospective study analyzed clinical and imaging data from 281 patients who underwent pancreatic CEUS between January 2018 and December 2023. Patients were randomly divided into training (n = 196) and validation (n = 85) sets. Logistic regression analyses were conducted to identify independent predictive imaging features for differentiating PDAC from MFP in the training set. Based on the identified predictors, two nomogram models were constructed: the US model and the US + CEUS model. The diagnostic performance of both models was assessed via the area under the receiver operating characteristic curve (AUC), calibration plots, Hosmer-Lemeshow test, and decision-curve analysis (DCA).

RESULTS

Multivariate logistic regression analysis based on these factors identified taller-than-wide shape (P = 0.002, OR = 0.12), calcification (P = 0.003, OR = 13.76), and washout pattern (P = 0.002, OR = 0.13) as independent predictive factors for distinguishing PDAC from MFP. Compared to the US model, the US + CEUS model demonstrated better performance with AUC values 0.930 (95% CI: 0.895-0.965) in the training set and 0.914 (95% CI: 0.853-0.976) in the validation set. Calibration curve plots and the Hosmer-Lemeshow test (P > 0.05) confirmed that the model has good calibration, and DAC showed significant clinical benefit.

CONCLUSION

The nomogram model constructed using taller-than-wide shape, calcification, and washout pattern demonstrated excellent discriminative ability, accuracy, and clinical utility in differentiating PDAC from MFP.