Early Gastric CancerNCT07047937
Explainable Machine Learning for Predicting Early Gastric Cancer
An observational study of Early Gastric Cancer, run by Wenzhou Central Hospital at 1 site in China.
Trial summary
Abstract Background: Early detection of gastric cancer is crucial for improving patient survival rates. Currently, the primary method for diagnosing early-stage gastric cancer is endoscopy, which has various limitations. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Early Gastric Cancer
- How many people
- 10 participants
- Where
- 1 siteChina
- Study dates
- 28 Jun 2025to 1 Jul 2025
- How long will I take part?
- 10 Days
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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
- Child, Adult, Older adult
Study population: all patients with a gastric tissue pathology result are included,1,085 patients were included in the study
Who may be able to join
- all patients with a gastric tissue pathology result are included
Who cannot join
- unclear or incomplete pathology results
- significant missing laboratory data
- progressive and advanced gastric cancer
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
About this study
Official title: Explainable Machine Learning for Predicting Early Gastric Cancer: a Retrospective Cohort Study
Abstract Background: Early detection of gastric cancer is crucial for improving patient survival rates. Currently, the primary method for diagnosing early-stage gastric cancer is endoscopy, which has various limitations. Additionally, single laboratory tests continue to fall short of the requirements for early screening. This study aims to develop a machine learning (ML) model using clinical data to predict early-stage gastric cancer and apply SHapley Additive exPlanation (SHAP) values to explain the ML model.
Methods: This study involved patients who provided gastric tissue samples at Wenzhou Central Hospital from 2019 to 2023. The investigators gathered various laboratory test results from these patients. The investigators constructed and evaluated nine ML models to predict early-stage gastric cancer, using the area under the curve (AUC), accuracy, and sensitivity to assess their performance. For the most effective prediction model, The investigators utilized the SHAP method to determine the features' importance and explain the ML model.
- Conditions
- Early Gastric Cancer
- Keywords
- early detection of cancer, early gastric cancer, machine learning, prediction model, SHapley Additive exPlanation (SHAP)
- Medical subject headings
- Stomach Neoplasms
What is being measured
The primary outcome is the main question the study is designed to answer.
Explainable machine learning for predicting early gastric cancer
The area under the ROC curve (AUC) was used as the primary outcome measure
Measured: From June 2025 to July 2025
Secondary outcomes (1)
Explainable machine learning for predicting early gastric cancer
We considered the sensitivity of the model as a secondary outcome measure.
Measured: From June 2025 to July 2025
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Jul 2025
- Study start28 Jun 2025 (estimated)
- Primary completion1 Jul 2025 (estimated)
- Study completion1 Jul 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Early Gastric CancerStudies of the same condition, open and under way.
- 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
- Prospective
- Enrolment
- 10 participants (estimated)
- Target duration
- 10 Days
Sponsor and investigators
- Lead sponsor
- Wenzhou Central Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, sunmeng chen, Wenzhou Central Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07047937
- Sponsor's protocol ID
- 202506031607000064611
- Status last verified
- Jun 2025
- First submitted
- 4 Jun 2025
- Last updated
- 2 Jul 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: All IPD collected throughout the trial.
Study at a glance
From the registry record, last updated by the sponsor on 2 Jul 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Explainable Machine Learning for Predicting Early Gastric Cancer: a Retrospective Cohort Study
- ClinicalTrials.gov ID
- NCT07047937
- Acronym / sponsor ID
- 202506031607000064611
- Conditions
- Early Gastric Cancer
- Keywords
- early detection of cancer, early gastric cancer, machine learning, prediction model, SHapley Additive exPlanation (SHAP)
- Study type
- Observational
- Recruitment status
- Enrolling by invitation
- Enrolment
- 10 participants (estimated)
- Lead sponsor
- Wenzhou Central HospitalUniversity, hospital or other
- Study start
- 28 Jun 2025 (estimated)
- Primary completion
- 1 Jul 2025 (estimated)
- Study completion
- 1 Jul 2025 (estimated)
- First posted
- 2 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.
Abstract Background: Early detection of gastric cancer is crucial for improving patient survival rates. Currently, the primary method for diagnosing early-stage gastric cancer is endoscopy, which has various limitations. Additionally, single laboratory tests continue to fall short of the requirements for early screening. This study aims to develop a machine learning (ML) model using clinical data to predict early-stage gastric cancer and apply SHapley Additive exPlanation (SHAP) values to explain the ML model.
Methods: This study involved patients who provided gastric tissue samples at Wenzhou Central Hospital from 2019 to 2023. The investigators gathered various laboratory test results from these patients. The investigators constructed and evaluated nine ML models to predict early-stage gastric cancer, using the area under the curve (AUC), accuracy, and sensitivity to assess their performance. For the most effective prediction model, The investigators utilized the SHAP method to determine the features' importance and explain the ML model.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 10 Days
Who may qualify
- Sex
- All
- Age groups
- Child, Adult, Older adult
- Study population
- all patients with a gastric tissue pathology result are included,1,085 patients were included in the study
Key inclusion criteria (1 listed)
- all patients with a gastric tissue pathology result are included
Key exclusion criteria (3 listed)
- unclear or incomplete pathology results
- significant missing laboratory data
- progressive and advanced gastric cancer
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Explainable machine learning for predicting early gastric cancer
Time frame: From June 2025 to July 2025
The area under the ROC curve (AUC) was used as the primary outcome measure
1 secondary outcome
Explainable machine learning for predicting early gastric cancer
Time frame: From June 2025 to July 2025
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Jul 2025.
- Individual participant dataSharing planned: Yes. All IPD collected throughout the trial.
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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.
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 Early Gastric Cancer, run by Wenzhou Central 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 1 inclusion and 3 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: enrolling by invitation, as of the sponsor's last update on 2 Jul 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?
This study is still under way. Results are not available in the source record yet.
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?
Explainable machine learning for predicting early gastric cancer (time frame: From June 2025 to July 2025). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 2 Jul 2025; AzTrials last refreshed its copy on 29 Oct 2026.