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

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

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Key information

Conditions

Chronic Pancreatitis Chronic Disease Digestive System Diseases Digestive System Neoplasms Disease Attributes Endocrine Gland Neoplasms Endocrine System Diseases Machine Learning Neoplasms Neoplasms by Site Pancreatic Diseases Pancreatic Neoplasm Pancreatic Neoplasms Pancreatitis Pancreatitis, Chronic Pathologic Processes Pathological Conditions, Signs and Symptoms

Sex eligibility

All sexes

Study type

Observational

Primary location

Changhai Hospital

Shanghai, Shanghai Municipality, 200433, China

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

Diagnosis of chronic pancreatitis
Patients has indeterminate focal pancreatic lesions discovered through contrast-enhanced CT scans

Exclusion criteria

Patients had incomplete clinical data
Patients had no surgical pathology results for the focal pancreatic lesions and loss to follow-up, indicating that a final diagnosis of the focal pancreatic lesions could not been established

Treatment and study plan

XGBoost machine learning

Diagnostic Test

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.

Primary outcomes

Diagnostic yield

Time frame: 10 years

The diagnostic yield of XGBoost machine learning, including AUC、Sensitivity、Specificity

Sponsors and collaborators

Lead sponsor

Changhai Hospital

Other

Registry information

Official study title

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

Important dates

Study start: 2025
Primary completion: 2025
Study completion: 2025
First posted: Jul 1, 2025
Registry last updated: Sep 30, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

Completed