Prediction Model Of Pancreatic Neoplasms In CP Patients With Focal Pancreatic Lesions

Study Overview

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.

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 ȫlack 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.

Chronic Pancreatitis
Pancreatic Neoplasm
Machine Learning

DIAGNOSTIC_TEST: XGBoost machine learning

IPNPM

Study Record Dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Registration Dates	Results Reporting Dates	Study Record Updates
First Submitted 2025-06-22	Results First Submitted N/A	Last Update Submitted that met QC Criteria 2025-06-22
First Submitted that Met QC Criteria 2025-06-22	Results First Submitted that Met QC Criteria N/A	Last Update Posted (ACTUAL) 2025-07-01
First Posted (ACTUAL) 2025-07-01	Results First Posted N/A	Last Verified 2025-06

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

Design Details

Primary Purpose:
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Allocation:
N/A

Interventional Model:
N/A

Masking:
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Arms and Interventions

Participant Group/Arm	Intervention/Treatment
: Pancreatic neoplasm group This cohort consists of chronic pancreatitis patients whose focal pancreatic lesions were diagnosed as pancreatic neoplasm	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, ty
: Non-pancreatic neoplasm group This cohort consists of chronic pancreatitis patients whose focal pancreatic lesions were diagnosed as benign lesions	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, ty

Participant Group/Arm

Intervention/Treatment

: Pancreatic neoplasm group

This cohort consists of chronic pancreatitis patients whose focal pancreatic lesions were diagnosed as pancreatic neoplasm

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, ty

: Non-pancreatic neoplasm group

This cohort consists of chronic pancreatitis patients whose focal pancreatic lesions were diagnosed as benign lesions

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, ty

Primary Outcome Measures	Measure Description	Time Frame
Diagnostic yield	The diagnostic yield of XGBoost machine learning, including AUC、Sensitivity、Specificity	10 years

Secondary Outcome Measures	Measure Description	Time Frame

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person’s general health condition or prior treatments.

Ages Eligible for Study:
ALL

Sexes Eligible for Study:

Accepts Healthy Volunteers:

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

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

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Publications

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Kirkegard J, Mortensen FV, Cronin-Fenton D. Chronic Pancreatitis and Pancreatic Cancer Risk: A Systematic Review and Meta-analysis. Am J Gastroenterol. 2017 Sep;112(9):1366-1372. doi: 10.1038/ajg.2017.218. Epub 2017 Aug 1.
Hao L, Zeng XP, Xin L, Wang D, Pan J, Bi YW, Ji JT, Du TT, Lin JH, Zhang D, Ye B, Zou WB, Chen H, Xie T, Li BR, Zheng ZH, Wang T, Guo HL, Liao Z, Li ZS, Hu LH. Incidence of and risk factors for pancreatic cancer in chronic pancreatitis: A cohort of 1656 patients. Dig Liver Dis. 2017 Nov;49(11):1249-1256. doi: 10.1016/j.dld.2017.07.001. Epub 2017 Jul 15.
Korpela T, Udd M, Mustonen H, Ristimaki A, Haglund C, Seppanen H, Kylanpaa L. Association between chronic pancreatitis and pancreatic cancer: A 10-year retrospective study of endoscopically treated and surgical patients. Int J Cancer. 2020 Sep 1;147(5):1450-1460. doi: 10.1002/ijc.32971. Epub 2020 Apr 3.

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