Explainable Machine Learning for Predicting Early Gastric Cancer
NCT ID: NCT07047937
Last Updated: 2025-07-02
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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ENROLLING_BY_INVITATION
10 participants
OBSERVATIONAL
2025-06-28
2025-07-01
Brief Summary
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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.
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Detailed Description
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Conditions
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Study Design
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COHORT
PROSPECTIVE
Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
* significant missing laboratory data
* progressive and advanced gastric cancer
ALL
No
Sponsors
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Wenzhou Central Hospital
OTHER
Responsible Party
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sunmeng chen
Resident in gastrointestinal surgery
Locations
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Wenzhou Central Hospital
Wenzhou, Zhejiang, China
Countries
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Other Identifiers
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202506031607000064611
Identifier Type: -
Identifier Source: org_study_id
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