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

NCT07045181 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 113

Last updated 2025-09-30

No results posted yet for this study

Summary

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

Conditions

  • Chronic Pancreatitis
  • Pancreatic Neoplasm
  • Machine Learning

Interventions

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.

Sponsors & Collaborators

  • Changhai Hospital

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-07-01
Primary Completion
2025-08-01
Completion
2025-08-05

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07045181 on ClinicalTrials.gov