Interpretable Prediction of Pancreatic Neoplasms in Chronic Pancreatitis Patients With Focal Pancreatic Lesions Based on XGBoost Machine Learning and SHAP

Pancreatic neoplasms include various types, with pancreatic cancer being the most common and having a poor prognosis. Chronic pancreatitis (CP) can progress to pancreatic cancer, and detecting neoplasms in CP patients is challenging due to similar imaging and clinical presentations. Current diagnostic methods like CT and tumor markers have limitations, and endoscopic ultrasound-guided tissue acquisition has moderate sensitivity. Machine learning (ML) shows promise in medical fields, but its "black box" nature limits its application. SHapley additive exPlanations (SHAP) can provide intuitive explanations for ML models. This study aims to develop an ML model to predict pancreatic neoplasms in CP patients with focal pancreatic lesions and use SHAP to explain the model, aiding future research.