Differentiating Pancreatic Ductal Adenocarcinoma and Autoimmune Pancreatitis Using a Machine Learning Model Based on Ultrasound Clinical Features

Lihua Zhang ¹ Xiang Chen ^1* Zhikui Chen ² Weiji Chen ^1* Jianmei Zheng ^3* Minling Zhuo ^2* Xing Chen ^1*

• ¹ Fujian Provincial Hospital, Fuzhou, China
• ² Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China
• ³ First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian Province, China

This study aimed to construct a differential diagnostic model to distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDCA) using ultrasound clinical features and machine learning algorithms. Methods Retrospective ultrasound clinical data of patients with AIP and PDCA from three different centers were used as the training cohort, external validation cohort 1, and external validation cohort 2. Feature selection was conducted via variance filtering and LASSO regression, followed by the construction of a random forest (RF) model. The hyperparameters were optimized in the training cohort, and the final model was evaluated in the external validation cohorts. The model's performance was assessed using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), the F1 score, accuracy, and area under curve(AUC). The clinical application value of the model was clarified through a comparison between humans and machines.An RF model was constructed using six features: Ca 19-9 level, abdominal pain, jaundice, focal/diffuse-type AIP, blood flow signals, and morphology.