Explainable Machine Learning for Predicting Early Gastric Cancer
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Early Gastric Cancer. Basic parameters: No limits · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Explainable Machine Learning for Predicting Early Gastric Cancer: a Retrospective Cohort Study
Overview
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.
Primary outcome measures
- Explainable machine learning for predicting early gastric cancer [Time frame: From June 2025 to July 2025]
Secondary outcome measures (1)
- Explainable machine learning for predicting early gastric cancer [Time frame: From June 2025 to July 2025]
Eligibility criteria
Inclusion criteria
- all patients with a gastric tissue pathology result are included
Exclusion criteria
- unclear or incomplete pathology results
- significant missing laboratory data
- progressive and advanced gastric cancer
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Study design
- Observational model
- Cohort
Study locations
China · 1 center
- Wenzhou Central Hospital — Wenzhou
Identifiers
NCT: NCT07047937 · 202506031607000064611