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

Not yet recruiting

• Conditions: Machine Learning; Chronic Pancreatitis; Pancreatic Neoplasm

• Registration Number: NCT07045181
• Lead Sponsor: Changhai Hospital

Brief 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.

Study Timeline

• Status Verified: 2025-06
• Study First Submitted: 2025-06-22
• Study First Submitted QC: 2025-06-22
• Last Update Submitted: 2025-06-22
• Study Start: 2025-07-01
• Study First Posted: 2025-07-01
• Last Update Posted: 2025-07-01
• Primary Completion: 2025-07-15
• Study Completion: 2025-07-15

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 106

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

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Diagnostic yield	10 years	The diagnostic yield of XGBoost machine learning, including AUC、Sensitivity、Specificity

Trial Locations

Locations (1)

Changhai Hospital; 🇨🇳 Shanghai, Shanghai, China