Chronic PancreatitisNCT07045181
Prediction Model of Pancreatic Neoplasms in CP Patients With Focal Pancreatic Lesions
An observational study of Chronic Pancreatitis and Pancreatic Neoplasm, run by Changhai Hospital at 1 site in China.
Trial summary
This study aims to develop XGBoost machine learning model to predict pancreatic neoplasms in CP patients with focal pancreatic lesions. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Chronic Pancreatitis, Pancreatic Neoplasm, Machine Learning
- What is being tested?
- XGBoost machine learning
- How many people
- 113 participants
- Where
- 1 siteChina
- Study dates
- 1 Jul 2025to 5 Aug 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Chronic pancreatitis patients who has indeterminate focal pancreatic lesions discovered through contrast-enhanced CT scans
Who may be able to join
- Diagnosis of chronic pancreatitis
- Patients has indeterminate focal pancreatic lesions discovered through contrast-enhanced CT scans
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Interpretable Prediction of Pancreatic Neoplasms in Chronic Pancreatitis Patients With Focal Pancreatic Lesions Based on XGBoost Machine Learning and SHAP
This study aims to develop XGBoost machine learning model to predict pancreatic neoplasms in CP patients with focal pancreatic lesions.
Read the 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.
- Conditions
- Chronic Pancreatitis, Pancreatic Neoplasm, Machine Learning
- Being tested
- XGBoost machine learning
- Keywords
- Pancreatic neoplasm, chronic pancreatitis, machine learning, SHAP
- Medical subject headings
- Pancreatitis, Chronic, Pancreatic Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
XGBoost machine learningDiagnostic 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.
| Study group | Role | What they receive |
|---|---|---|
| Pancreatic neoplasm group | This cohort consists of chronic pancreatitis patients whose focal pancreatic lesions were diagnosed as pancreatic neoplasm Receives: Diagnostic Test: XGBoost machine learning | |
| Non-pancreatic neoplasm group | This cohort consists of chronic pancreatitis patients whose focal pancreatic lesions were diagnosed as benign lesions Receives: Diagnostic Test: XGBoost machine learning |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic yield
The diagnostic yield of XGBoost machine learning, including AUC、Sensitivity、Specificity
Measured: 10 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 Jul 2025
- Study start1 Jul 2025
- Primary completion1 Aug 2025
- Study completion5 Aug 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for Chronic PancreatitisStudies of the same condition, open and under way.
- More trials run by Changhai HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 113 participants (actual)
Sponsor and investigators
- Lead sponsor
- Changhai Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Zhaoshen Li, Changhai Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07045181
- Sponsor's protocol ID
- IPNPM
- Status last verified
- Sep 2025
- First submitted
- 22 Jun 2025
- Last updated
- 30 Sep 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
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.
PubMed 28762376Hao 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.
PubMed 28756974Korpela 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.
PubMed 32162688
Study at a glance
From the registry record, last updated by the sponsor on 30 Sep 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Interpretable Prediction of Pancreatic Neoplasms in Chronic Pancreatitis Patients With Focal Pancreatic Lesions Based on XGBoost Machine Learning and SHAP
- ClinicalTrials.gov ID
- NCT07045181
- Acronym / sponsor ID
- IPNPM
- Conditions
- Chronic Pancreatitis, Pancreatic Neoplasm, Machine Learning
- Keywords
- Pancreatic neoplasm, chronic pancreatitis, machine learning, SHAP
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 113 participants (actual)
- Lead sponsor
- Changhai HospitalUniversity, hospital or other
- Study start
- 1 Jul 2025 (actual)
- Primary completion
- 1 Aug 2025 (actual)
- Study completion
- 5 Aug 2025 (actual)
- First posted
- 1 Jul 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Pancreatic neoplasm group | – | Diagnostic Test: XGBoost machine learningThis cohort consists of chronic pancreatitis patients whose focal pancreatic lesions were diagnosed as pancreatic neoplasm |
| Non-pancreatic neoplasm group | – | Diagnostic Test: XGBoost machine learningThis cohort consists of chronic pancreatitis patients whose focal pancreatic lesions were diagnosed as benign lesions |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Chronic pancreatitis patients who has indeterminate focal pancreatic lesions discovered through contrast-enhanced CT scans
Key inclusion criteria (2 listed)
- Diagnosis of chronic pancreatitis
- Patients has indeterminate focal pancreatic lesions discovered through contrast-enhanced CT scans
Key exclusion criteria (2 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| XGBoost machine learningXGBoost 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. | Diagnostic test | Non-pancreatic neoplasm group, Pancreatic neoplasm group |
Primary outcome
Diagnostic yield
Time frame: 10 years
The diagnostic yield of XGBoost machine learning, including AUC、Sensitivity、Specificity
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 30 Sep 2025.
- Background: PubMed 28762376Kirkegard 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.
- Background: PubMed 28756974Hao 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.
- Background: PubMed 32162688Korpela 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Chronic Pancreatitis and Pancreatic Neoplasm, run by Changhai Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 30 Sep 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic yield (time frame: 10 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07045181, which also lists 3 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 30 Sep 2025.