NCT07045181

Prediction Model of Pancreatic Neoplasms in CP Patients With Focal Pancreatic Lesions

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

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 113 (actual)
• Sponsor: Changhai Hospital · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: Not accepted

Summary

This study aims to develop XGBoost machine learning model to predict pancreatic neoplasms in CP patients with focal pancreatic lesions.

Detailed description

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.

Conditions

• Chronic Pancreatitis
• Pancreatic Neoplasm
• Machine Learning

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	XGBoost machine learning	XGBoost is a powerful machine learning algorithm known for its efficiency and performance. It is an optimized gradient boosting library designed to be highly efficient, flexible, and portable. XGBoost works by combining multiple weak prediction models, typically decision trees, to produce a strong predictive model. It supports various objective functions and evaluation metrics, making it suitable for a wide range of tasks, including classification and regression. XGBoost also includes features like regularization to prevent overfitting and can handle missing data effectively.

Timeline

• Start date: 2025-07-01
• Primary completion: 2025-08-01
• Completion: 2025-08-05
• First posted: 2025-07-01
• Last updated: 2025-09-30

Locations

1 site across 1 country: China