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

Naval Military Medical University

Status

Completed

Conditions

Chronic Pancreatitis

Machine Learning

Pancreatic Neoplasm

Treatments

Diagnostic Test: XGBoost machine learning

Study type

Observational

Funder types

Other

Identifiers

NCT07045181

IPNPM

Details and patient eligibility

About

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

Full 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.

Enrollment

113 patients

Sex

All

Volunteers

No Healthy Volunteers

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