Clinical value of the nomogram model based on endoscopic ultrasonography radiomics and clinical indicators in identifying benign and malignant lesions of the pancreas.

Objective: Based on endoscopic ultrasonography (EUS) radiomics and clinical data, we constructed a radiomics model and a nomogram model for identifying benign and malignant pancreatic lesions, and explored the diagnostic performance of these two prediction models. Methods: Images and clinical data of 151 patients with pancreatic lesions detected by EUS from January 2018 to September 2023 were retrospectively collected. The patients were randomly divided into a training set and a validation set at a ratio of 7:3. Through feature extraction and feature screening of EUS images, we calculated the radiomics score (rad-score) to realize the construction of the radiomics model. Collecting the clinical data, laboratory test results, and rad-scores from patients, univariate and multivariate logistic regression analyses were used to screen statistically significant influencing factors that could help identify benign and malignant lesions of the pancreas, and a nomogram model was constructed. The diagnostic performance and clinical utility of the two prediction models were evaluated using the receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results: Through feature extraction and screening, eight non-zero coefficient features were finally selected to calculate the rad-score. Multivariate logistic regression analysis showed that rad-score, age, and CA199 were the influencing factors in predicting benign and malignant pancreatic lesions. A nomogram model was constructed based on the three factors. In the validation set, the nomogram model exhibited superior performance with an AUC = 0.865 (95% CI 0.761-0.968) compared to the radiomics prediction model. The calibration curve and DCA depicted that the nomogram model demonstrated superior accuracy and yielded a higher net benefit for clinical decision-making compared to the radiomics prediction model. Conclusion: Based on EUS radiomics and clinical indicators, we constructed a promising nomogram model to accurately identify benign and malignant pancreatic lesions.